text:
package info.andreaswolf.roadhopper.road

import com.graphhopper.util.shapes.GHPoint3D
import junit.framework.Assert
import org.scalamock.scalatest.MockFactory

class RouteTest extends org.scalatest.FunSuite with MockFactory {

	test("An empty route should have length 0") {
		val list = List()
		Assert.assertEquals(0.0, new Route(list).length)
	}
/* TODO rework these tests to work again
	test("A route with two segments should have the length of both segments") {
		val firstSegment = mock[RoadSegment]
		(firstSegment.length _).when().returns(100.0)
		(firstSegment.orientation _).when().returns(0.0)
		val secondSegment = mock[RoadSegment]
		(secondSegment.length _).when().returns(50.0)
		(secondSegment.orientation _).when().returns(0.0)
		val list = List(
			firstSegment,
			secondSegment
		)
		Assert.assertEquals(150.0, new Route(list).length)
	}

	test("A segment in the route can be retrieved by position") {
		val list = List(
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 100.0f),
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 50.0f),
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 25.0f)
		)
		val route = new Route(list)

		Assert.assertTrue(route.getSegmentForPosition(50.0) == list.head)
		Assert.assertTrue(route.getSegmentForPosition(100.0) == list.head)
		Assert.assertTrue(route.getSegmentForPosition(100.1) == list.toArray.apply(1))
		Assert.assertTrue(route.getSegmentForPosition(125.0) == list.toArray.apply(1))
		Assert.assertTrue(route.getSegmentForPosition(150.0) == list.toArray.apply(1))
		Assert.assertTrue(route.getSegmentForPosition(150.1) == list.toArray.apply(2))
		Assert.assertTrue(route.getSegmentForPosition(175.0) == list.toArray.apply(2))
	}

	test("Accessing a route segment out of the bounds leads to an exception") {
		val list = List(
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 100.0f),
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 50.0f),
			new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 25.0f)
		)
		val route = new Route(list)

		intercept[IllegalArgumentException] {
			route.getSegmentForPosition(175.1)
		}
	}

	test("Coordinates for route points can be fetched") {
		val segments = List(new RoadSegment(new GHPoint3D(0.0,0.0,0.0), new GHPoint3D(0.1, 0.1, 0.0), 100.0f))
		val route = new Route(segments)

		Assert.assertEquals(List(new Tuple2(0.0, 0.0), new Tuple2(100.0f, 0.0)), route.roadSegmentCoordinates)
	}
*/

}


There are 3 public schools and 1 private school listed on our site for Ossian.
There are 117 full-time teachers educating a total of 1,966 students. 454 students receive a free lunch while 158 receive a reduced-price lunch.


The December 22, 2008 has printed two contiguous LTEs critical of Obama's health care plans. One is by Richard Ralston, director of Americans for Free Choice in Medicine and the second is by myself. Both were in response to their December 9, 2008 article, "The Obama Health-Care Express".
You are probably correct that a major new national health-care program will be rushed through the next Congress without substantial debate through some mechanism such as budget reconciliation. That is because many of its elements would not survive close examination. The fatuous claim of Sen. Max Baucus that placing the nation's medical care under the rule of an "independent" council of presidentially appointed experts would not constitute government management of care is only the most conspicuous example. Others include the claim that computerizing those remaining medical records still on paper would reduce insurance costs by $2,500 a year per family.
But the main reason for the big rush is that nobody has a clue how the government will pay for it -- anymore than they know how the current unfunded liability of Medicare and Medicaid can be honored.
Businesses expecting to save money under President-elect Barack Obama's universal health-care plan are going to be in for a rude awakening. President-elect Obama's plan includes an employer mandate in which businesses must either pay their employee health insurance or else pay into a government fund to cover the uninsured.
A similar mandate has already been in place in Massachusetts for two years. As health costs there have skyrocketed, the state government has asked for more and more "contributions" from businesses. During this financial crisis, the last thing America needs is yet more economic burdens on the businessmen who create jobs and prosperity.
The fundamental problem with Mr. Obama's plan is the premise that health care is a "right" that must be guaranteed by the government. Health care is a need, not a right. Rights are freedoms of action, not automatic claims on goods and services that must be produced by another. Attempting to guarantee an alleged "right" to health care must necessarily violate actual individual rights and will destroy the American economy in the process.


text:
\section{Introduction}

Knowledge representation and reasoning (KRR) and reinforcement learning (RL) are two important research areas in artificial intelligence (AI) and have been applied to a variety of problems in robotics.
On the one hand, KRR research aims to concisely represent knowledge, and robustly draw conclusions with the knowledge (or generate new knowledge).
Knowledge in KRR is typically provided by human experts in the form of declarative rules.
Although KRR paradigms are strong in representing and reasoning with knowledge in a variety of forms, they are not designed for (and hence not good at) learning from experiences of accomplishing the tasks.
On the other hand, RL algorithms enable agents to learn by interacting with an environment, and RL agents are good at learning action policies from trial-and-error experiences toward maximizing long-term rewards under uncertainty, but they are ill-equipped to utilize declarative knowledge from human experts.
Motivated by the complementary features of KRR and RL, we aim at a framework that integrates both paradigms to enable agents (robots in our case) to simultaneously reason with declarative knowledge and learn by interacting with an environment.

Most KRR paradigms support the representation and reasoning of knowledge in logical form, e.g., the Prolog-style.
More recently, researchers have developed KRR paradigms that support both logical and probabilistic knowledge.
Examples include Markov Logic Network (MLN)~\cite{richardson2006markov}, P-log~\cite{baral2009probabilistic}, and Probabilistic Soft Logic (PSL)~\cite{bach2017hinge}.
Such logical-probabilistic KRR paradigms can be used for a variety of reasoning tasks.
We use P-log in this work to represent and reason with both human knowledge and the knowledge from RL.
When a task becomes available, our robot reasons at runtime to produce probabilistic transition systems for action policy generation.

Reinforcement learning (RL) algorithms can be used to help robots learn action policies from the experience of interacting with the real world~\cite{sutton1998reinforcement}.
There are at least two types of RL algorithms, namely model-based RL and model-free RL, depending on whether a world model is computed or not.
Model-free RL aims at directly computing the ``value'' of a state (or state-action pair), whereas the output of model-based RL includes a world model, and one can use planning algorithms to compute action policies with the world model.
We use model-based RL in this work, because the learned world model can be used to update the robot's declarative knowledge base and combined with human knowledge.

In this paper, we develop a framework (KRR-RL) that integrates logical-probabilistic KRR and model-based RL.
Transition probabilities that result from actions are learned via model-based RL, and then incorporated into the probabilistic reasoning module, which in turn (together with human knowledge) enables dynamic construction of efficient run-time task-specific planning models.
Our KRR-RL framework, for the first time, enables a robot to:
i) represent the probabilistic knowledge (i.e., world dynamics) learned from RL in declarative form;
ii) unify and reason with both human knowledge and the knowledge from RL; and
iii) compute policies at runtime by dynamically constructing task-oriented partial world models.
The above advantages are backed with experiments both in simulation and using real mobile robots conducting navigation, dialog, and delivery tasks.

\section{Related Work}
\label{sec:related}

This work is on integrating logical-probabilistic knowledge representation and reasoning (KRR) and model-based reinforcement learning (RL).
Related research areas include integrated logical KRR and RL, relational RL, and integrated KRR and probabilistic planning.

For example, logical KRR has previously been integrated with RL.
Action knowledge~\cite{mcdermott1998pddl,jiang2018empirical} has been used to reason about action sequences and help an RL agent explore only the states that can potentially contribute to achieving the ultimate goal~\cite{leonetti2016synthesis}.
As a result, their agents learn faster by avoiding choosing ``unreasonable'' actions.
A similar idea has been applied to domains with non-stationary dynamics~\cite{ferreira2017answer}.
More recently, task planning was used to interact with the high level of a hierarchical RL framework~\cite{yang2018peorl}.
The goal shared by these works is to enable RL agents to use knowledge to improve the performance in learning (e.g., to learn faster and/or avoid risky exploration).
However, the KRR capabilities of these methods are limited to \emph{logical} action knowledge.
By contrast, we use a logical-probabilistic KRR paradigm that can directly reason with probabilities learned from RL.

Relational RL (RRL) combines RL with relational reasoning~\cite{dvzeroski2001relational}.
Action models have been incorporated into RRL, resulting in a relational temporal difference learning method~\cite{asgharbeygi2006relational}.
Recently, RRL has been deployed for learning affordance relations that forbid the execution of specific actions~\cite{sridharan2017can}.
These RRL methods, including deep RRL~\cite{zambaldi2018relational}, exploit structural representations over states and actions in (only) current tasks.
In this research, our framework supports the KRR of factors beyond the ones in state and action representations, e.g., \emph{time} in navigation tasks (Section~\ref{sec:ins}).

The research area of integrated KRR and probabilistic planning is also related to this research.
Logical-probabilistic reasoning has been used to compute informative priors~\cite{zhang2015corpp} and world dynamics~\cite{zhang2017dynamically} for probabilistic planning.
An action language was used to compute a deterministic sequence of actions for robots, where individual actions are then implemented using probabilistic controllers~\cite{sridharan2015refinement}.
Recently, human-provided information has been incorporated into belief state representations to guide robot action selection~\cite{chitnis2018integrating}.
World models must be provided to these methods, where learning was not involved.

Finally, there are a number of robot reasoning and learning architectures~\cite{tenorth2013knowrob,oh2015toward,hanheide2017robot,khandelwal2017bwibots}, which are relatively complex, and support a variety of functionalities.
In comparison, we aim at a concise representation for robot KRR and RL capabilities.
To the best of our knowledge, this is the first work on a tightly coupled integration of logical-probabilistic KRR with model-based RL, where the KRR component supports the representation of and reasoning with the knowledge both from a human and from RL.

\section{Integrated KRR-RL Framework}
\label{sec:alg}

\begin{figure}[tb]
  \begin{center}
    \includegraphics[width=0.45\textwidth]{framework}
  \end{center}
  \vspace{-1.5em}
  \caption{A pictorial overview of our KRR-RL framework. }
  \label{fig:overview}
  \vspace{-1.5em}
\end{figure}

Our unified framework for logical-probabilistic knowledge representation and reasoning (KRR) and model-based reinforcement learning (RL) is illustrated in Figure~\ref{fig:overview}.
The KRR includes both human knowledge and the knowledge learned from model-based RL.
When the robot is free, the robot arbitrarily selects goals (different navigation goals in our case) to work on, and learns the world dynamics, e.g., success rates and costs of navigation actions.
When a task becomes available, the KRR component dynamically constructs a partial world model (excluding unrelated factors), on which a task-oriented controller is computed using planning algorithms.
Human knowledge is on environment variables and their dependencies, e.g., navigation actions' success rates depend on current time and weather (laser sensors can be blinded in areas near east-facing windows in sunny mornings), while the robot must learn specific probabilities by interacting with the environment.

\noindent
\textbf{\emph{Why integrated KRR-RL is needed? }}
Consider an indoor robot navigation domain, where a robot wants to maximize the success rate of moving to goal positions through navigation actions.
\emph{Shall we include factors, such as time, weather, positions of human walkers, etc, into the state space?}
On the one hand, to ensure model completeness, the answer should be ``yes''.
Human walkers and sunlight reduce the success rates of the robot's navigation actions,
and both can cause the robot irrecoverably lost.
On the other hand, to ensure computational feasibility, the answer is ``no''.
Modeling whether one specific grid cell being occupied by humans or not introduces one extra dimension in the state space, and doubles the state space size.
If we consider (only) ten such grid cells, the state space becomes $2^{10}\approx 1000$  times bigger.
As a result, RL practitioners frequently have to make a trade-off between model completeness and computational feasibility.
In this work, we aim at a framework that retains both model scalability and computational feasibility, i.e., the agent is able to learn within relatively small spaces while computing action policies accounting for a large number of domain variables.

\subsection{A General Procedure}

In factored spaces, state variables $\mathcal{V}=\{V_0,V_1,...,V_{n-1}\}$ can be split into two categories, namely endogenous variables $\mathcal{V}^{en}$ and exogenous variables $\mathcal{V}^{ex}$~\cite{chermack2004improving}, where $\mathcal{V}^{en}=\{V_0^{en},V_1^{en},...,V_{p-1}^{en}\}$ and $\mathcal{V}^{ex}=\{V_0^{ex},V_1^{ex},...,V_{q-1}^{ex}\}$.
In our integrated KRR-RL context, $\mathcal{V}^{en}$ is goal-oriented and includes the variables whose values the robot wants to actively change so as to achieve the goal; and $\mathcal{V}^{ex}$ corresponds to the variables whose values affect the robot's action outcomes, but the robot cannot (or does not want to) change their values.
Therefore, $\mathcal{V}^{en}$ and $\mathcal{V}^{ex}$ are both functions of task $\tau$.
Continuing the navigation example, robot position is an endogenous variable, and current time is an exogenous variable.
For each task, $\mathcal{V}=\mathcal{V}^{en} \cup \mathcal{V}^{ex}$ and $n=p+q$, and RL agents learn in spaces specified by $\mathcal{V}^{en}$.

The KRR component models $V$, their dependencies from human knowledge, and conditional probabilities on how actions change their values, as learned through model-based RL.
When a task arrives, the KRR component uses probabilistic rules to generate a task-oriented Markov decision process (MDP)~\cite{puterman1994markov}, which only contains a subset of $\mathcal{V}$ that are relevant to the current task, i.e., $\mathcal{V}^{en}$, and their transition probabilities.
Given this task-oriented MDP, a corresponding action policy is computed using value iteration or policy iteration.

\begin{algorithm}[tb]\small
  \caption{Learning in KRR-RL Framework}
  \label{alg:framework}
  \begin{algorithmic}[1]
    \REQUIRE{Logical rules $\Pi^L$;
        probabilistic rules $\Pi^P$;
        random variables $\mathcal{V}=\{V_0,V_1,...,V_{n-1}\}$;
        task selector $\Delta$; and
        guidance functions (from human knowledge) of $f^V(\mathcal{V}, \tau)$ and $f^A(\tau)$}
    \WHILE{Robot has no task}
        \STATE{$\tau \leftarrow \Delta()$: a task is heuristically selected}
        \STATE{$\mathcal{V}^{en} \leftarrow f^V(\mathcal{V}, \tau)$, and $\mathcal{V}^{ex} \leftarrow \mathcal{V} \setminus \mathcal{V}^{en}$}
    \STATE{$A \leftarrow f^A(\tau)$}
    \STATE{$\mathcal{M} \leftarrow \textit{Procedure-}\ref{alg:generatemodel}(\Pi^L, \Pi^P, \mathcal{V}^{en}, \mathcal{V}^{ex}, A)$}
    \STATE{Initialize agent: $agent\leftarrow$ \emph{R-Max}$(\mathcal{M})$}
    \STATE{RL \emph{agent} repeatedly works on task $\tau$, and keeps maintaining task model $\mathcal{M}'$, until policy convergence}
    \ENDWHILE
    \STATE{Use $\mathcal{M}'$ to update $\Pi^P$}
  \end{algorithmic}
\end{algorithm}

Our KRR-RL agent learns by interacting with an environment when there is no task assigned (Procedure~\ref{alg:framework}).
As soon as a task arrives, it uses the probabilities that are marked ``known'' to update the knowledge base, and start to focus on completing the task at hand (Procedure~\ref{alg:generatemodel}). Next, we present the details of these two interleaved processes.

Procedure~\ref{alg:framework} includes the steps of the learning process.
When the robot is free, it interacts with the environment by heuristically selecting a task\footnote{Here curriculum learning~\cite{narvekar2017autonomous} can play a role to task selection and we leave this aspect of the problem for future work.}, and repeatedly using a model-based RL approach, R-Max~\cite{brafman2002r} in our case, to complete the task.
The two guidance functions come from human knowledge.
For instance, given a navigation task, it comes from human knowledge that the robot should model its own position (specified by $f^V$) and actions that help the robot move between positions (specified by $f^A$).
After the policy converges or this learning process is interrupted (e.g., by task arrivals), the robot uses the learned probabilities to update the corresponding world dynamics in KRR.
For instance, the robot may have learned the probability and cost of navigating through a particular area in early morning.
In case this learning process is interrupted, the so-far-``known'' probabilities are used for knowledge base update.

Procedure~\ref{alg:generatemodel} includes the steps for building the probabilistic transition system of MDPs.
The key point is that we consider only endogenous variables in the task-specific state space.
However, when reasoning to compute the transition probabilities (Line~\ref{line:m}), the KRR component uses both $\Pi^P$ and $\mathcal{V}^{ex}$.
The computed probabilistic transition systems are used for building task-oriented controllers, i.e., $\pi$, for task completions.
In this way, the dynamically constructed controllers do not directly include exogenous variables, but their parameters already account for the values of all variables.

Next, we demonstrate how our KRR-RL framework is instantiated on a real robot.

\subsection{An Instantiation on a Service Robot}
\label{sec:ins}
We consider a mobile service robot domain where a robot can do navigation, dialog, and delivery tasks.
A \emph{navigation task} requires the robot to use a sequence of (unreliable) navigation actions to move from one point to another.
In a \emph{dialog task}, the robot uses spoken dialog actions to specify service requests from people under imperfect language understanding.
A \emph{delivery task} requires the robot to use dialog to figure out the delivery request and conduct navigation tasks to physically fulfill the request.
Specifically, a delivery task requires the robot to deliver item \c{I} to room \c{R} for person \c{P}, resulting in services in the form of \c{<I,R,P>}.
The challenges come from unreliable human language understanding (e.g., speech recognition) and unforeseen obstacles that probabilistically block the robot in navigation.

Next, we present complete world models constructed using P-log~\cite{baral2009probabilistic,balai2017refining}, a logical-probabilistic KRR paradigm, and describe the process of constructing task-oriented controllers.

\begin{algorithm}[tb]\small
  \caption{Model Construction for Task Completion}
  \label{alg:generatemodel}
  \begin{algorithmic}[1]
    \REQUIRE{$\Pi^L$; $\Pi^P$; $\mathcal{V}^{en}$; $\mathcal{V}^{ex}$; Action set $A$}
    \FOR{$V_i \in \mathcal{V}^{en}$, $i~$ in $[0,\cdots,|\mathcal{V}^{en}|\!-\!1]$}
        \FOR{each possible value $v$ in $range(V_i)$}
            \FOR{each $a\in A$}
                \FOR{each possible value $v'$ in $range(V_i)$}
                    \STATE{$\mathcal{M}(v'|a,v) \leftarrow$ Reason with $\Pi^L$ and $\Pi^P$} w.r.t $\mathcal{V}^{ex}$ \label{line:m}
                \ENDFOR
            \ENDFOR
        \ENDFOR
    \ENDFOR
    \RETURN $\mathcal{M}$
  \end{algorithmic}
\end{algorithm}

\begin{figure*}[tb]
\vspace{-1em}
  \begin{center}
    \includegraphics[width=0.8\textwidth]{trans_dialog}
  \end{center}
  \vspace{-1.5em}
  \caption{Transition system specified for delivery tasks, where question-asking actions are used for estimating the service request in dialog. Once the robot becomes confident about the service request, it starts to work on the navigation subtask. After the robot arrives, the robot might have to come back to the dialog subtask and redeliver, depending on whether the service request was correctly identified. }
  \label{fig:transition}
  \vspace{-1.5em}
\end{figure*}

\paragraph{Representing Rigid Knowledge}

Rigid knowledge includes information that does not depend upon the passage of time. We introduce a set of \emph{sorts} including \c{time}, \c{person}, \c{item}, and \c{room}, so as to specify a complete space of service requests. For instance, we can use \c{time=\{morning, noon, afternoon, ...\}} to specify possible values of \c{time}.

To model navigation domains, we introduce $N$ grid cells using \c{cell=\{0,...,N-1\}}, where the robot can travel. The geometric organization of these cells can be specified using predicates of \c{leftof($n_1$,$n_2$)} and \c{belowof($m_1$,$m_2$)}, where $n_i$ and $m_j$ are cells, $i,j\in \{0,\dots, N-1\}$.

We use a set of random variables to model the space of world states.
For instance, \c{curr\_cell:cell} states that the robot's position \c{curr\_cell}, as a random variable, must be in sort \c{cell};
\c{curr\_room:person->room} states that a person's current room must be in sort \c{room};
and \c{curr\_succ} identifies the request being fulfilled or not.
We can then use \c{random(curr\_cell)} to state that the robot's current position follows a uniform distribution over \c{cell}, unless specified elsewhere; and use the following pr-atom (a form of declarative probabilistic rule in P-log) to state that, in probability 0.8, people request deliveries to their own rooms.

\begin{quote}
\begin{scriptsize}
\begin{verbatim}
pr(curr_room(P)=R|place(P,R)=true)=8/10.
\end{verbatim}
\end{scriptsize}
\end{quote}

\paragraph{Representing Action Knowledge}

We use a set of random variables in P-log to specify the set of delivery actions available to the robot. For instance, \c{serve(coffee,lab,alice)} indicates that the current service request is to deliver \c{coffee} to \c{lab} for \c{alice}.
\begin{quote}
\begin{scriptsize}
\begin{verbatim}
serve(I,R,P) :- act_item=I, act_room=R, act_person=P.
\end{verbatim}
\end{scriptsize}
\end{quote}
where predicates with prefix \c{act\_} are random variables for modeling \c{serve} actions.

To model probabilistic transitions, we need to look one step forward, modeling how actions lead state transitions. We introduce two identical state spaces using predicates \c{curr\_s} and \c{next\_s}. The following shows the specification of the current state using \c{curr\_s}.

\begin{quote}
\begin{scriptsize}
\begin{verbatim}
curr_s(I,R,P,C,S) :- curr_item(P)=I, curr_room(P)=R,
            curr_person=P, curr_cell=C, curr_succ=S.
\end{verbatim}
\end{scriptsize}
\end{quote}

Given the current and next state spaces, we can use pr-atoms to specify the transition probabilities led by delivery actions.
For instance, to model physical movements, we introduce random variable \c{act\_move:move} and sort \c{move=\{up, down, left, right\}}. Then we can use \c{act\_move=right} to indicate the robot attempting to move rightward by one cell, and use the following pr-atom
\begin{quote}
\begin{scriptsize}
\begin{verbatim}
pr(next_cell=C1 | curr_cell=C, leftof(C,C1),
                  act_move=right) = 8/10.
\end{verbatim}
\end{scriptsize}
\end{quote}
to state that, after taking action \c{right}, the probability of the robot successfully navigating to the cell on the right is $0.8$ (otherwise, it ends up with one of the nearby cells).
Such probabilities are learned through model-based RL.

It should be noted that, even if a request is correctly identified in dialog, the robot still cannot always succeed in delivery, because there are obstacles that can probabilistically trap the robot in navigation.
When the request is misidentified, delivery success rate drops, because the robot has to conduct multiple navigation tasks to figure out the correct request and redo the delivery.
We use $s\odot a$ and $s\otimes a$ to represent delivery action $a$ matches to request component of $s$ or not (i.e., service request is correctly identified in dialog or not).

\paragraph{Constructing (PO)MDP Controllers}

To fulfill a delivery request, the robot needs spoken dialog to identify the request under unreliable speech recognition, and navigation controllers for physically making the delivery.

The service request is not directly observable to the robot, and has to be estimated by asking questions, such as ``What item do you want?'' and ``Is this delivery for Alice?''
Once the robot is confident about the request, it takes a delivery action (i.e., \c{serve(I,R,P)}).
We follow a standard way to use partially observable MDPs (POMDPs)~\cite{kaelbling1998planning} to build our dialog manager, as reviewed in~\cite{Young2013POMDP}.
The state set $\mathcal{S}$ is specified using \c{curr\_s}.
The action set $\mathcal{A}$ is specified using \c{serve} and question-asking actions.
Question-asking actions do not change the current state, and delivery actions lead to one of the terminal states (success or failure).

After the robot becomes confident about the request via dialog, it will take a delivery action \c{serve\{I,R,P\}}. This delivery action is then implemented with a sequence of \c{act\_move} actions. When the request identification is incorrect, the robot needs to come back to the shop, figure out the correct request, and redeliver, where we assume the robot will correctly identify the request in the second dialog.
We use an MDP to model this robot navigation task, where the states and actions are specified using sorts \c{cell} and \c{move}. We use pr-atoms to define the unreliable movements.
Figure~\ref{fig:transition} shows the probabilistic transitions in delivery tasks.

\paragraph{Knowledge from Model-based RL}

We use R-Max~\cite{brafman2002r}, a model-based RL algorithm, to help our robot learn the success rate of navigation actions in different positions.
The agent first initializes an MDP, from which it uses R-Max to learn the partial world model (of navigation tasks).
There is a fixed small cost for each navigation action. The robot receives a big bonus if it successfully achieves the goal ($R_{max}$), whereas it receives a big penalty otherwise ($-R_{max}$).
A transition probability in navigation, $T^N(s,a,s')$, is not computed until there are a minimum number ($M$) of transition samples visiting $s'$.
We recompute the action policy after $E$ action steps.

The update of knowledge base is achieved through updating the success rate of delivery actions \c{serve(I,R,P)} (in dialog task) using the success rate of navigation actions \c{act\_move=M} in different positions (in navigation task).
\begin{align}
\label{eqn:transfer}
    &T^D(s^r, a^d, s^t)= \nonumber\\
    &\begin{cases}
        P^N(s^{sp}\!, s^{gl}),~{\bf if}~s^r\odot a^d\\
        P^N(s^{sp}\!, s^{mi})\times P^N(s^{mi}\!, s^{sp}) \times P^N(s^{sp}\!, s^{gl}),~{\bf if}~s^r\otimes a^d
    \end{cases}
\end{align}
where $T^D(s^r, a^d, s^t)$ is the probability of fulfilling request $s^r$ using delivery action $a^d$;
$s^t$ is the ``success'' terminal state;
$s^{sp}$, $s^{mi}$ and $s^{gl}$ are states of the robot being in the shop, a misidentified goal position, and real goal position respectively;
and $P^N(s,s')$ is the probability of the robot successfully navigating from $s$ to $s'$ positions. In practice, $P^N(s,s')$ is approximated by running a number of navigation tasks in simulation using the learned partial world model, while following an action policy computed using value iteration.

When $s^r$ and $a^d$ are aligned in all three dimensions (i.e., $s^r\odot a^d$), the robot needs to navigate once from the shop ($s^{sp}$) to the requested navigation goal ($s^{gl}$).
$P^N(s^{sp},s^{gl})$ is the probability of the corresponding navigation task. When the request and delivery action are not aligned in at least one dimension (i.e., $s^{r}\otimes a^{d}$), the robot has to navigate back to the shop to figure out the correct request, and then redeliver, resulting in three navigation tasks.

\section{Experiments}
\label{sec:experiment}

\begin{figure}[tb]
 \vspace{.5em}
  \begin{center}
    \includegraphics[height=0.15\textheight]{map}
    \includegraphics[height=0.15\textheight]{KeJia1}
  \end{center}
  \vspace{-.5em}
  \caption{Occupancy-grid map used in our experiments, including five rooms, one shop, and four blocking areas (indicated by `BA'). All deliveries are from the shop and to one of the rooms. }
  \label{fig:map}
  \vspace{-.5em}
\end{figure}

In this section, the goal is to evaluate our hypothesis that our KRR-RL framework enables a robot to learn from model-based RL, reason with both the learned knowledge and human knowledge, and dynamically construct task-oriented controllers.
Specifically, our robot learns from navigation tasks, and applied the learned knowledge (through KRR) to navigation, dialog, and delivery tasks.
We also evaluated whether the learned knowledge can be represented and applied to tasks under different world settings.
In addition to simulation experiments, we have used a real robot to demonstrate how our robot learns from navigation to perform better in dialog.
Figure~\ref{fig:map} shows the map of the working environment (generated using a real robot) used in both simulation and real-robot experiments. Human walkers in the blocking areas (``BA'') can probabilistically impede the robot, resulting in different success rates in navigation tasks.

\paragraph{Learn from and applied to navigation tasks}
Focusing on navigation tasks, in this experiment, the robot learns in the \c{shop-room1} navigation task, and extracts the learned partial world model to the \c{shop-room2} task. It should be noted that navigation from \c{shop} to \c{room2} requires traveling in areas that are unnecessary in the \c{shop-room1} task. The results are shown in Figure~\ref{fig:navigation}, where each data points corresponds to an average of 1000 trials. Each episode allows at most 200 (300) steps in small (large) domain. The curves are smoothed using a window of 10 episodes. The results suggest that with knowledge extraction (the dashed line) the robot learns faster than without extraction, and this performance improvement is more significant in a larger domain (the Right subfigure).

\begin{figure}[tb]
  \begin{center}
    \hspace*{-.1em}\includegraphics[width=0.26\textwidth]{new_exp_nav_30} \hspace*{-1.7em}
    \includegraphics[width=0.26\textwidth]{new_exp_nav_50}\hspace*{-1.4em}
  \end{center}
  \vspace{-1.5em}
  \caption{Navigation tasks in small (Left: $30\times 30$ grid) and large (Right: $50\times 50$ grid) domains.
  With extraction (dashed line), the robot learns faster in the target navigation task. }
  \label{fig:navigation}
  \vspace{-1.5em}
\end{figure}

\paragraph{Learn from navigation and applied to delivery}

Robot delivering objects requires both tasks: dialog management for specifying service request (under unreliable speech recognition) and navigation for physically delivering objects (under unforeseen obstacles).
Our office domain includes five rooms, two persons, and three items, resulting in 30 possible service requests.
In the dialog manager, the reward function gives delivery actions
\begin{itemize}
\item  a big bonus (80) given a request being fulfilled, and
\item  a big penalty (-80) otherwise.
\end{itemize}
General questions and confirming questions cost 2.0 and 1.5 respectively. In case a dialog does not end after 20 turns, the robot is forced to work on the most likely delivery.

\renewcommand{\arraystretch}{1}
\begin{table}[t]
  \centering
  \fontsize{6.5}{8}\selectfont
  \begin{threeparttable}
  \caption{Overall performance of both question-asking in dialog management and navigation in object delivery. }
  \label{tab:both}
    \begin{tabular}{ccccccc}
    \toprule
    \multirow{2}{*}{}&\multicolumn{3}{c}{Without extraction}&\multicolumn{3}{c}{With extraction}\cr
    \cmidrule(lr){2-4} \cmidrule(lr){5-7}
    & Reward & Fulfilled & QA Cost & Reward & Fulfilled & QA Cost \cr
    \midrule
    $br=0.1$ &182.07 &0.851 &20.86     &206.21 &0.932 &18.73 \cr
    $br=0.5$ & 30.54 &0.853 &20.84     & 58.44 &0.927 &18.98 \cr
    $br=0.7$ &-40.33 &0.847 &20.94     &-14.50 &0.905 &20.56 \cr
    \bottomrule
    \end{tabular}
      \vspace{-1.5em}
    \end{threeparttable}
\end{table}

Table~\ref{tab:both} reports the robot's overall performance in fulfilling delivery requests, which requires the robot accurately identifying the request in dialog and then safely delivering the item in navigation.
We conduct 10,000 simulation trials under each blocking rate.
Without learning from RL, the robot uses an outdated world model that was learned under $br=0.3$. With learning, the robot updates its world model in domains with different blocking rates.
We can see, when learning is enabled, our KRR-RL framework produces higher overall reward, higher request fulfillment rate, and lower question-asking cost.
The improvement is statistically significant, e.g., the $p\!-\!values$ are 0.028, 0.035, and 0.049 for overall reward, when \emph{br} is 0.1, 0.5, and 0.7 respectively (100 randomly selected trials with/without extraction).

To better analyze robot dialog behaviors, we generate cumulative distribution function (CDF) plots showing the percentage of dialog completions (y-axis) given different QA costs (x-axis). Figure~\ref{fig:completeness} shows the results when the request are deliveries to \c{room2} (left) and \c{room4} (right).
Comparing the two curves in each subfigure, we find our KRR-RL framework reduces the QA cost in dialogs (consistent to Table~\ref{tab:both}).
Comparing the two subfigures, we find a smaller QA cost is needed, when the request is a delivery to \c{room4} (c.f., \c{room2}). This observation makes sense, because \c{room4} is closer to the shop (see Figure~\ref{fig:map}) and deliveries to \c{room4} is easier.

\begin{figure}[thb]
  \vspace{-1em}
  \begin{center}
    \includegraphics[width=0.26\textwidth]{new_exp_complete12}\hspace*{-1.5em}
    \includegraphics[width=0.26\textwidth]{new_exp_complete14}\hspace*{-1em}\vspace{-.5em}

  \end{center}
  \vspace{-1em}
  \caption{CDF plots of dialog completions. The requests are deliveries to \c{room2} (Left) and \c{room4} (right). }
  \label{fig:completeness}
  \vspace{-.5em}
\end{figure}

In the last experiment, we quantify the information collected in dialog in terms of entropy reduction. The hypothesis is that, using our KRR-RL framework, the dialog manager wants to collect more information before physically working on more challenging tasks.
In each trial, we randomly generate a belief distribution over all possible service requests, evaluate the entropy of this belief, and record the suggested action given this belief. We then statistically analyze the entropy values of beliefs, under which delivery actions are suggested.

Table~\ref{tab:entropy} shows that, when \emph{br} grows from 0.1 to 0.7, the means of belief entropy decreases (i.e., belief is more converged).
This suggests that the robot collected more information in dialog in environments that are more challenging for navigation, which is consistent with Table~\ref{tab:both} (Right).
Comparing the three columns of results, we find the robot collects the most information before it delivers to \c{room5}.
This is because such delivery tasks are the most difficult due to the location of \c{room5}. The results support our hypothesis that learning from navigation tasks enables the robot to adjust its information gathering strategy in dialog given tasks of different difficulties.

\renewcommand{\arraystretch}{1}
\begin{table}[t]
  \centering
  \fontsize{6.5}{8}\selectfont
   \vspace{.5em}
  \begin{threeparttable}
  \caption{The amount of information (in terms of entropy) needed by a robot before taking delivery actions.}
  \vspace{-.5em}
  \label{tab:entropy}
    \begin{tabular}{ccccccc}
    \toprule
    \multirow{3}{*}{}   &\multicolumn{2}{c}{Entropy (room1)}
                        &\multicolumn{2}{c}{Entropy (room2)}
                        &\multicolumn{2}{c}{Entropy (room5)}\cr
    \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
    & Mean (std) & Max & Mean (std) & Max & Mean (std) & Max \cr
    \midrule
    $br=0.1$ & .274 (.090) & .419 & .221 (.075)     & .334 & .177 (.063) & .269 \cr
    $br=0.5$ & .154 (.056) & .233 & .111 (.044)     & .176 & .100 (.041) & .156 \cr
    $br=0.7$ & .132 (.050) & .207 & .104 (.042)     & .166 & .100 (.041) & .156 \cr
    \bottomrule
    \end{tabular}
    \vspace{-2em}
    \end{threeparttable}
\end{table}

\paragraph{Generate controllers for new circumstances}

The knowledge learned through model-based RL is contributed to a knowledge base that can be used for many tasks.
So our KRR-RL framework enables a robot to dynamically generate partial world models for tasks under settings that were never experienced.
For example, an agent does not know the current time is morning or noon, there are two possible values for variable ``time''.
Consider that our agent has learned world dynamics under the times of morning and noon.
Our KRR-RL framework enables the robot to reason about the two transition systems under the two settings and generate a new transition system for this ``morning-or-noon'' setting.
Without our framework, an agent would have to randomly select one between the ``morning'' and ``noon'' policies.

To evaluate our policies dynamically constructed via KRR, we let an agent learn three controllers under three different environment settings -- the navigation actions have decreasing success rates under the settings.
In this experiment, the robot does not know which setting it is in (out of two that are randomly selected).
The baseline agent does not has the KRR capability of merging knowledge learned from different settings, and can only randomly select a policy from the two (each corresponding to a setting).
Experimental results show that the baseline agent received an average of $26.8\%$ in success rate in navigation tasks.
In comparison, our KRR-RL agent achieved $83.8\%$ success rate on average.
Thus, our KRR-RL framework enables a robot to effectively apply the learned knowledge to tasks under new settings.

\paragraph{An Illustrative Trial on a Robot}

We have implemented our KRR-RL framework on a mobile robot in an office environment, the robot is shown in Figure~\ref{fig:map} (Right).
Figure~\ref{fig:distribution} shows the belief changes (in the dimensions of \c{item}, \c{person}, and \c{room}) as the robot interacts with a human user.
The robot started with a uniform distribution in all three categories. It should be noted that, although the marginal distributions are uniform, the joint belief distribution is not, as the robot has prior knowledge such as \c{Bob}'s office is \c{office2} and people prefer deliveries to their own offices.

After hearing ``a coke for Bob to office2'', the three sub-beliefs are updated (\c{turn1}).
Since the robot is aware of its unreliable speech recognition, it asked about the item, ``Which item is it?''
After hearing ``a coke'', the belief is updated (\c{turn2}), and the robot further confirmed on the item by asking ``Should I deliver a coke?''
It received a positive response (\c{turn3}), and decided to move on to ask about the delivery room: ``Should I deliver to office 2?''
\emph{The robot did not confirm the delivery room, because it learned through model-based RL that navigating to \c{office2} is relatively easy and it decided that it is more worth risking an error and having to replan than it is to ask the person another question. }
The robot became confident in three dimensions of the service request (\c{<coke,Bob,office2>} in \c{turn4}) \emph{without} asking about \c{person}, because of the prior knowledge (encoded in P-log) about \c{Bob}'s office.

\begin{figure}[tb]
  \vspace{-1em}
  \hspace*{-.5em}
  \begin{center}
    \includegraphics[width=0.48\textwidth]{distribution}
  \end{center}
  \vspace{-1em}
  \caption{Belief change in three dimensions (In order from the left: Items, Persons and Offices) over five turns in a human-robot dialog .

  }
  \label{fig:distribution}
  \vspace{-1.5em}
\end{figure}

\section{Conclusions and Future Work}
\label{sec:conclude}
In this work, we develop a KRR-RL framework that, for the first time, integrates computational paradigms of logical-probabilistic knowledge representation and reasoning (KRR), and model-based reinforcement learning (RL).
Our KRR-RL agent learns world dynamics (in the form of transition probabilities) via model-based RL, and then incorporates the learned dynamics into the logical-probabilistic reasoning module, which is used for dynamic construction of efficient run-time task-specific planning models.
Experiments were conducted using a mobile robot (simulated and physical) working on delivery tasks that involve both navigation and dialog.
Results suggested that the learned knowledge from RL can be represented and used for reasoning by the KRR component, enabling the robot to dynamically generate task-oriented action policies.

The integration of a KRR paradigm and model-based RL paves the way for  at least the following research directions.
We plan to study how to sequence source tasks to help the robot perform the best in the target task (i.e., a curriculum learning problem~\cite{narvekar2017autonomous}).
Balancing the efficiencies between service task completion and RL is another topic for further study -- currently the robot optimizes for task completions (without considering the potential knowledge learned in this process) once a task becomes available.

{
\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
Can-Spam backer to lose top exec
The Direct Marketing Association, which helped shape the first U.S. federal antispam law, announces that CEO Robert Wientzen is planning to retire.
Stefanie Olsen
Dec. 17, 2003 5:24 p.m. PT
An influential marketing trade group that helped shape and provided key political support for the United States' first federal antispam law is losing its top executive.
Robert Wientzen, president and CEO of the Direct Marketing Association, said on Tuesday that he plans to retire from the group in July, an announcement that came on the same day President George W. Bush signed the Controlling the Assault of Non-Solicited Pornography and Marketing (Can-Spam) Act.
Wientzen, named the association's chief in September 1996, said he will step down to spend more time with his family.
Current and former DMA board members will form an executive search committee to find a successor. Wientzen has been asked to continue to stay through the end of 2004 to make sure that the transition is smooth.
Wientzen said the timing of the announcement was a coincidence and was not meant as a comment on the DMA's role in the legislative process. But he said he was pleased with his group's efforts and the results.
"We've been working with the Burns-Wyden folks for two years now, big-time," he said, referring to legislation authors Sen. Conrad Burns, R-Mon., and Sen. Ron Wyden, D-Ore. "We had a great part of the process; we got a lot of lobbying in. I do feel very good about the fact that we got this law passed."
Legislators and lobbyists have battled over the wording of a federal antispam law in a stream of proposals since the late 1990s. Then, just more than a year after the DMA publicly announced its support for antispam legislation, the logjam broke.
The law aims to crack down on junk e-mail senders, but it has been criticized by antispam advocates for including some promarketing provisions. Among other things, the law overrides some tough state measures that allow individuals to sue alleged spammers.
"The DMA's fingerprints are all over this thing," said Jim Nail, a senior analyst at Forrester Research. "It's an early Christmas gift to marketers, actually."
For this reason, industry watchers applauded Wientzen for building the DMA into a powerful lobbying group in his eight years at its helm. He has also positioned the DMA at the center of the digital marketing sphere and elevated the profile of direct marketers during his tenure.
Wientzen said in an interview on Tuesday that for a time, the DMA had not publicly supported antispam legislation but that it had started working in the background with several Congressional representatives. When the group was presented with a bill it thought viable for direct marketers and consumers, it had backed the legislation. He said the DMA's priority was to come up with a law that would require the sender to identify itself and give law enforcement--not the public--the right to strike at fraudulent senders. The DMA also wanted strong penalties for convicted spammers, Wientzen said.
"We ended up believing that we had to have more legal status to help law enforcement and to raise the consciousness of the government about the problem," Wientzen said. "And in the process of getting a law, you get law enforcement to be much more focused."
The goal, he said, was to create a deterrent against spammers by giving law enforcement the means to jail junk e-mailers. The DMA does not support the Do Not E-mail list suggested in Can-Spam, but overall, it believes that the act is a good start, Wientzen said.
"The sight of a few spammers being led away in handcuffs could go a long way to reducing the problem," he said.
But in his tenure as head of the DMA, Wientzen has fielded many attacks from staunch antispammers who believe that the group is essentially pro-junk mail. They point to the DMA's endorsement of commercial e-mail and "opt out" laws, which let marketers e-mail consumers freely until the recipients ask to be removed from their list.
Wientzen called such critics shortsighted, because small businesses on the Internet would not have the means to reach new customers if not for e-mail. "That's the price we pay for American spirit," he said.
Forrester's Nail said the DMA's only disappointment came this year, when the Federal Trade Commission's National Do Not Call Registry was introduced.
Before becoming head of the DMA, Wientzen was a 20-year member of the organization, serving as a member of the board for six of those years. He started his career in direct marketing at Procter & Gamble, one of the DMA's founding members.
Under Wientzen's eight-year leadership, the DMA established a dominant presence in Internet marketing by buying two digital advertising trade groups--the Association for Interactive Marketing in 1998 and the Internet Alliance in 1999.
In the past eight years, the DMA's membership has grown by 47 percent, expanding to seven chapters across the United States from only one in 1997.
Discuss: Can-Spam backer to lose top exec

redpajama_set_name: RedPajamaCommonCrawl


text:
hecksinductionhour	in international, migrants, rights, war and peace	 December 6, 2016 December 7, 2016 3,638 Words
"Everything There Is like a Horror Film Now":
Young Refugees Talk about War, Fleeing Home, and Living in Russia
Filippo Valoti-Alebardi
Armed conflicts in the Middle East and instability in parts of Africa and South Asia have led to one of the largest immigrant crises since the Second World War. According to Frontex, 1.82 million refugees arrived in Europe in 2015, and another 173,761 people arrived in Europe by sea in the first part of 2016. Russia has found itself on the sidelines in terms of most migrant flows. Only one route, which runs through Russia's land borders with Norway and Finland, was used for the transit to Europe. According to RIA Novosti, around 6,000 people traversed this route between October and December 2015.
The Russian Federal Migration Service (FMS) claims there are very few people with refugee status in Russia, less than eight hundred. Basically, people who seek refuge here can count only on temporary refugee status, which is valid for one year. But if a person manages to obtain it, no one can guarantee it will be renewed in the future. Furfur met with four young refugees and wrote down their stories of fleeing their home countries and living in hiding in Russia.
Hasan, a 20-year-old refugee from Syria
I left Aleppo three years ago. In 2012, the civil war came to our city. All the state institutions closed, except for some hospitals. I stopped going to school and almost never left my house. Everything was topsy-turvy in Aleppo then: government troops might be in one district, while the opposition was in the next. Life was hard but bearable. The financial crisis was not as bad as now, and my family had some savings. We were bombed, but not like during the Islamic State's offensive. The electricity didn't work, but we had a generator. The water was severely rationed, but it was enough.
Almost as soon as fighting broke out, I was forbidden to leave the house. I was not yet threatened with conscription, but my parents feared I could be recruited, killed or kidnapped, since I was the oldest son in the family. The other members of the family also tried not to leave our flat without a very good reason. We just sat at home and waited for it to all be over.
In the summer of 2013, an acquaintance of my father's helped me get a work visa to Russia, and I left Syria. The person worked here in a sewing factory where there were many Syrians. He met me at the airport and took me to Losino-Petrovsky, where I still live. I immediately started worked in the sewing workshop. My father had been a tailor, so I already had some skills.
During the fifth month of my stay in Russia, I applied for refugee status. The [UNHCR] helped me prepare the papers for the FMS, where I had to have an interview. I was asked about my family's financial state, whether I had served in the army, and about my political stance. A few months later, I received temporary refugee status, but it lasted for only a year.
I lived in Moscow Region and worked in the workshop. I tried to keep in touch with my family and friends. One day, a friend called me and said our house had been bombed and everyone had been killed. So only two members of my family, which had consisted of eleven people, have survived: my sister, who got married and lives in Istanbul, and I.
When my refugee status ended, I went to the FMS and asked for an extension. This time round, my case was handled by a different officer. He also asked me questions about my origins, financial state, and political stance, but then he asked why I had not gone to Iran, Turkey or Europe. I said I liked it here. I also told him that, over the past year, my mother, brother, and all my brothers and sisters had died, except one. I was given a certificate, valid for one month, and then I was turned down. I was told the situation in Syria had normalized, that I was in no danger and could return home safely. But I had nowhere to return: my home and family were gone.
I was given three months to appeal against the refusal. I made four attempts to appeal it, but to no avail. Finally, I went to a Syrian man who said he had friends with pull. He promised to help me for 70,000 rubles [approx. 1,000 euros]. Ultimately, however, I was turned down once more, and never saw the guy again. Now I am in Russian illegally, and for the time being I have managed to avoid problems.
The police often stop me under the pretext of checking my papers, but they have a pretty good attitude to Syrians. Previously, when my papers were in order, they would haul me down to the precinct and take my fingerprints before letting me go. The situation has now become more complicated, and I often have to bribe them. It is usually not in Moscow where the police check my papers, but in Losino-Petrovsky itself. The local police are well aware of where the migrants live and work. They know our routes and when we get off work. So at least one or twice a month they detain one of us.
I rarely leave my own neighborhood. I work six days a week, twelve hours a day, and have almost no free time. But when I have the time and energy, I go play football with my friends, either in Noginsk or Moscow. I speak almost no Russian. At work, I get by with Arabic and a few words in Russian, since I work with Syrians, Tajiks, and Uzbeks. I sometimes chat on the phone with my sister in Turkey and with friends who have left Syria and gone to Turkey and Europe. I used to really miss my family and my home in Syria, but not anymore. I have lost my family and simply see no point in life. I even think it would have been better had I been with my family the day the bomb fell on my house. It would have been better to die with them than to hear about their deaths over the telephone.
Yasmin, an 18-year-old refugee from Yemen
This is the second war my parents have fled. My father is half Vietnamese, half Yemeni. My mother is a Vietnamese Muslim. When the war between the US and Vietnam ended, they found themselves in a refugee camp in Yemen, which is where they met. My mom was seventeen then, the same age I was when I came to Russia.
Life in Yemen had always been hard for our family. Because my father speaks Arabic poorly and cannot write it, he could never count on a good job. On the streets, people would always point at us and say, "Look! There go the Chinese." Everything got complicated after the 2011 revolution. Some government offices ceased to function, and foreign companies gradually left the country. A year later, the German firm for which my father worked as a driver closed its office, and he lost his job. It was hard to find another job. Ultimately, my older brother had to quit school to support us. He spoke the best Arabic in our family.
War broke out in Yemen in 2014, but we were affected by it only in 2015, when the heavy bombardment began. We lived in the city of Taiz, but our house was not far from a rebel camp, so the planes targeted our neighborhood. We took our things and left for Sanaa, the Yemeni capital, to stay with relatives. It was much safer there, and we livef peacefully for two weeks or so, but then the bombing recommenced.
In Sanaa, we lived near the Russian Embassy. After one of the bombing raids, we went there to ask for help. The embassy officials told us a Russian Emergencies Ministry (MChS) plane would be leaving [soon]. They explained where to go and when, but they did not promise we would be taken aboard. On the right day, we arrived at the appointed time at the airport, where we saw a team of [Russian] rescuers. They put our family on the plane. We had no visas, but we had passports. All the others who wanted to go had no papers and were left behind.
There were lots of Russian citizens on the plane with us, but there were also Yemenis, Syrians, and even a few Americans. We made a stopover in Djibouti, and there we were given the right to choose: stay behind or fly on to Russia. Since we had no family in Djibouti or other countries, we decided to fly to Russia. First, we were taken to a military airport, and then to a civil airport, where we had to wait for a consul. He gave us ten-day entry visas and ran off.
We did not know a word of Russian or English, we had no money, and we were hungry. I don't know what we would have done if it had not been for the Syrian who was on the plane with us. He spoke Russian and interpreted for us. Then he gave us two hundred dollars and ordered us a taxi to the Yemeni embassy. For some reason, the taxi driver took us to the Egyptian embassy, not the Yemeni embassy, and on top of that he made us pay him fifty dollars, not thirty dollars, as we had agreed. But it was a good thing the guard at the embassy spoke Arabic, since it was cold and we had no idea where we were. He called us a cab to take us to our embassy, and the next driver, an Egyptian, did not even charge us.
At the embassy, we were given a room where we lived for approximately two months. During this time we put together papers for obtaining refugee status, which we applied for at the [UNHCR] offices and the FMS. Later, the Vietnamese ambassador came to see us. He helped us get a room at the Hanoi Moscow Hotel, where we have been living ever since.
Our application for refugee status has been turned down twice. We have appealed the decision and are now awaiting the outcome. We need the status in order to be able to work and somehow organize our lives in a new place, because for over a year we have been living solely due to support from the Vietnamese. We have nowhere else to go. The war and bombing are still going on in Yemen, and there is almost nothing left of our home and neighborhood in Taiz. Everything there is like a horror film now.
Didier, a 23-year-old refugee from the Democratic Republic of the Congo
Before leaving Congo, I lived in Kinshasa, our country's capital, and was studying psychology. I left not because I was starving, but because I feared for my life. When my father died, I could not even go home to mourn and bury him. Instead, I am here, but I don't know how much time I have left in Russia.
In 2015, I attended a rally against changes to the electoral system that would have enabled the president to serve another term. It was a major protest rally, for which a thousand and a half students and staff from my university showed up. The authorities responded by sending in the army, police, and large army trucks to kettle the protesters. The police and soldiers shot to kill. At some point, we were herded into a corner where nothing was visible, everything was covered in flames. Twelve people were killed between January 19 and January 25. Two of them were my friends. We were at university together.
The best thing that happened to many people involved in the protests was that they simply disappeared. I managed to hide from the police at the place of some acquaintances. I could not go back home or to the university, because secret service officers were surveilling the demonstrators. They were especially interested in the people who had incited university students and staff to take part in the protests. I was a ringleader, and at the time I was already a member of Congo's second largest opposition party. I did not want to leave the country, but my parents insisted. They were worried about me, since a lot of people were disappearing at the time.
Why did I leave for Russia? I knew people who had friends with connections and helped me get the necessary papers. This took a while, but the situation got worse and I had to leave urgently. I left Congo on a night flight after convincing the police I was somewhere else. In Russia, I had the contacts of the people who had helped me get a student visa. The first six months, I lived in the place of a friend who had gone home, and then I lived at the People's Friendship University, where I met a lot of people and was advised to go to the Civic Assistance Committee. They helped me obtain temporary refugee status, which is issued for a year, and now I am trying to extend it.
There is a small Congolese diaspora in Moscow, but I do not communicate with them. I do not want to disseminate information about myself. I deliberately limit my dealings with other people, and I do not maintain contact with people from the Congolese opposition movement. I know that people in the Russian opposition are also detained, and I am scared my country's authorities might send an official extradition request. In Congo, I would definitely go to prison.
Russia is a "white" Africa. People here live in greater safety than back at home, but you are also unable to assemble and protest. You fear the police, who help implement the policy of dictatorship. Nevertheless, in Russia, you can find a job easily, you can buy a flat, and get a loan. The government thinks about its people at least a little, but not in Africa. The regime has complete forgotten about people. The president works only to benefity his own family. He stuffs his pockets and takes holidays in the States and Canada, while the populace suffers. Only officials, the people who stuff his pockets, live well. They should all be in prison. God needs to descend and free my people.
People in my country continue to protest, but they are few and the police arrest them, including members of our party, which they are trying to bleed to death. Some of my comrades have left the country, while many have been arrested.
I would like to go back to Congo to fight for human rights and give people back freedom of speech and the right to vote. I want to give them the ability to speak their own mind freely. I can tell you that right now in Congo women are being raped, people's heads are being cut off in markets, and people are being shot at.
More than ten million people have been killed in my country to date. It is the most dangerous country in the world for women: there are a huge number of rapes, and war is going on almost all the time. But if you dare talk about it, you are lost. Most of the people who can talk about it are in Europe. They upload short videos to the Internet and talk about the atrocities occurring in Congo, but if they went home they would be detained immediately.
And that is why I would like to tell Mr. Putin personally what is actually going on there. Our situation resembles the one in Syria right now, if it is not worse, but everyone talks only about Syria, and not about Congo. You white people in Russia, Europe, and the States, you are well aware of what is happening in Africa, but your governments would rather not doing anything about it. They only support the criminal regimes that rule our countries, getting money from them or investing in them. The whole world buys our diamonds: France, Belgium, and the US. Even you Russians are involved in diamond mining in Congo, which is always accompanied by war. Many people are afraid to talk about it, because they are afraid of disappearing. But I am not one of those people. I like telling the truth.
Muhammad, 28-year-old refugee from Syria
I am from the city of Kobanî, on the border with Turkey. I am a Kurd, and I left Syria five years ago, in 2011. I had just finished my military service when opposition rallies took place in Syria. It was all quite peaceful, and the situation in Kobanî was calm, but I sensed something serious was going to happen and decided it would be better to leave the country. I worked in the clothing industry, and a friend of mine invited me to Russia. I got help getting a yearlong business visa: that was how I ended up at the fabric in Noginsk. Initially, I came just to sit things out, but it has dragged on for five years, and there is no telling how much longer it will last. The first year I lived on a visa, and then I went to Egypt to extend it. Subsequently, Egypt changed the rules of entry for Syrians, and I was unable to do the same thing a second time.
Meanwhile, Syria has shifted into a state of war. One of my little brothers was captured by Islamic State when he was traveling with other schoolchildren to take exams. He was freed several days later, but I lost contact with my relatives when fighting broke out in the Kobanî area. There were heavy battles near the city, and my family were forced to flee to Turkey. Some of my second and third cousins stayed behind to fight Islamic State. Ten of them were killed, and my brother was seriously injured.
All this time, I was working and living in Russia, trying to formalizing my status as a refugee, but I was not having any luck. I would come somewhere with papers, but I would be sent first one place, and then another. An appointment would be made for me, but then it would be postponed: I would be told to come back in fifteen days, and then in ten days. I was once told to come at nine in the morning. I came half an hour early, but to no avail. I was told the queue was already too long and I had better come the next day. But they could not see me the next day, since I had been in the previous day's queue and had not shown up, allegedly. They toyed with me like this for several months. I decided to ask the [UNHCR] for help, but nothing changed. During the nine months I was going to the FMS, I was unable to file an application for refugee status. Finally, I gave up and stayed on illegally.
I met a Lebanese man who promised to help solve the problems with my papers if I went to work for him at a construction site. I went, but my problems were not solved. Instead, the police caught us. They beat us up right at the construction site. There were even some reporters with policemen, but they were told to turn their cameras off. We were thrown on the ground and beaten on the feet. They beat us so badly I could not walk normally for five days or so. They wanted us to sign some papers. We did not know what was in the papers, because they were in Russian, but we were forced to sign. After that, they stopped beating us and took us to court. We were not provided with an interpreter and so we did not understand most of the proceedings. I do remember, however, that the judge tried to find out what was up with us. He could see we were in a bad way. But we were unable to tell him what had happened, and the policemen told the judge that we were just tired from working.
After that, I returned to the factory and started working night shifts, since there are fewer chances the police will catch you. However, I am still sometimes detained on the streets anyway. I always try and have money with me to pay the police off. Usually, I take a five-hundred-ruble note with me: that way they cannot take too much. But I rarely go outside. I work almost seven days a week, and I have no energy to do anything else after a shift of twelve to fifteen hours. I only sleep and work, and the money I send to my family: they need it more. I would like to be near them, but we Syrians now need visas to get into Turkey, and I cannot get one anywhere. Nor can I return to Syria. I have no one in Kobanî, and there is almost nothing left of the city.
Furfur thanks the Civic Assistance Committee and translators Igor Farafonov, Alexander Khodunov, and Muhammad Haled for their help with this article. Translated by the Russian Reader. Thanks to Sergey Abashin for the heads-up
Read more about the treatment of Syrian and other refugees in Russia:
"Refugees from Yemen in Dead End," January 30, 2016
"Ripe for Exploitation: How Syrian Refugee Children Are Treated in Russia," August 24, 2015
Paul Canning, "Russian hypocrisy on refugees explained," September 27, 2015
Olga Tseitlina: 'Society does not understand why it should provide protection to Syrian refugees and refugees in general,'" September 12, 2015
Civic Assistance Committee
Emergencies Ministry
refugee status
Russian Federal Migration Service
Yemeni civil war
Kotlas: Russia's Bankruptcy Capital
#police #abuse #absurdity (Petersburg Police Abduct Alyona Romanova)
2 thoughts on "Refugees"
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CMG Financial Grows Pacific Leadership with Sherry Allen, Regional Vice President of Northern California and Hawaii
Roseville, CA – CMG Financial, a well-capitalized privately held mortgage banking firm headquartered in San Ramon, CA, announced today the hire of Sherry Allen, Regional Vice President of Northern California and Hawaii. Sherry has almost thirty years of mortgage banking experience and a diverse background of working at national and regional leading lenders around the country. In her new role, Sherry will uphold CMG's legacy in Northern California and chart new paths for growth.
Sherry started her mortgage banking career at First Union Bank/The Money Store, where she served as Assistant Vice President for eight years. She went on to serve as Specialty/Affinity Division Manager and Relationship Manager at ELoan, Regional Vice President at Pulte Mortgage and Del Webb Finance – Divisions of Pulte Home, Regional/Area Manager at Hillsborough Lending Corp, Area Services (NCAL/Hawaii Region at Elite – Mortgage Division, Managing Director at SecurityNational Mortgage Company, and Senior Market Manager at PNC. Most recently, she spent the past three years as Senior Vice President/Regional Manager Northern California and Hawaii at Guaranteed Rate. As Regional Vice President of Northern California and Hawaii, Sherry will leverage her industry experience and relationships to further establish CMG's brand, onboard new branches, and source new business.
"I chose to work with CMG Financial because my vision aligns with that of their leadership. The exceptional corporate culture and company reputation make CMG one of the best in the industry. I'm looking forward to sharing the unique proprietary products with my market," Sherry Allen said in a statement.
"Sherry Allen has managed large markets throughout California and Hawaii for many years, and it's an honor to finally have her on our team," Chip Larson, Western Divisional Vice President, CMG Financial, said in a statement. "Her experience with recruiting and building top-performing sales teams will help us further solidify our presence in the Pacific states."


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Q: Hyper-V Initial Replication over LAN seems capped at 50mbps I've ran the initial replication process over LAN multiple times with these exact two servers.
When I start the initial replication, typically I see my network adapters get floored with sending at like 500-600 mbps, and receiving the same on the replica server.
For reasons unknown to me, and the purpose of my question here, is that for some reason when I started a new initial replication a couple nights ago, the speed which it was transmitting the initial replica was bouncing between 50 and 60 mbps. Is there something I've missed or accidently enabled which is capping this?
Both Hypervisors are running Windows Server 2008 R2.
Thanks for any insight you can give me!

A: I believe I found the answer to my question. I moved one of the vhds to an external USB3 hard drive on the replica server, and it was running on the "Quick removal" policy. I thought I had already set it to the "Better Performance" policy when I originally set up the drive, but this was not the case.
So, I:

*

*Changed to "Better performace"

*Restarted the Server

*Started initial replication

I was suddenly seeing the 400-600 mbps speeds I'm used to.

redpajama_set_name: RedPajamaStackExchange


An electric folding bike you can take anywhere with you. Fold up and jump on the train. Unfold and make your next meeting. Use your Brompton Electric bicycle to hack the city's transport network and make it work for you. Feel unstoppable in the city with the Brompton Electric bike. Fly up hills. Feel like every ride has a tailwind. See more. Do more. And arrive fresh, and on time, every time.


In "Doryphoros", the sculpture explores, rhythm, commensurability, and proportion to a greater degree than almost any sculpture that predates it. This sculpture helps usher in the Classical Age in Greece.
A well-preserved Roman period copy of the Doryphoros of Polykleitos in the Naples National Archaeological Museum.


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DORM (2006) - a coming-of-age story from Thailand, with ghosts
(Updated September 2013)
My coverage of charming Thai ghost story Dorm (Dek Hor) is rather fragmented in Black Hole Reviews, because I used to publish news as it came in. Also, my most thorough review wasn't even in this site, but written for Twitch news. Because that's now mostly disappeared (except for one paragraph!), I'm republishing the original text below, together with my other notes on the main DVD releases of Dorm so far...
(2006, Thailand, Dek Hor)
Dorm is likely to get caught up among many other horror films from Thailand. But it's not so much horror, as a ghost story. Despite the young cast, it's certainly not childish - it has an uncomfortably dark side, reminiscent of Stand By Me, that makes it unsuitable for a younger audience. Anyhow, while I can't quite categorise it, I can say that it's a great Thai film, though not nearly as creepy as The Eye or Shutter.
While on holiday in Thailand, I was delighted to find that Dorm had just opened, a film I'd been looking forward to (having seen the trailer on Twitch) and better yet, half of the performances had English subtitles! Before the film started, in a large cinema (in Bangkok's MBK Shopping Centre), I was caught out by the whole audience suddenly standing up for the National Anthem accompanied by a photographic montage of their beloved King. We don't even do that in England!
The film opens with Chatree (Chalee Trairat), being sent to boarding school in his seventh grade. His father justifies the decision by saying it will enable him to study harder. Chatree is very angry, but we don't know why he's unable to complain. His life is made even more miserable because he's a 'new kid', joining the school halfway through term. The teacher who doubles as the school matron, Ms. Pranee (Chintara Sukapatana), does little to make him feel welcome.
All the boys in the school sleep in one huge dormitory. On the first night, a gang of four misfits decide to pick on the new kid by telling him ghost stories. Lying under the covers, they tell Chatree that the school, the dorm, Ms. Pranee, even the very bed he's sleeping in, all have a dark history. They tell him about the school worker who committed suicide, the deserted swimming pool, and a ghostly caretaker. The stories are shown like 'flashbacks', but just how much of it are they making up to scare him? Their ruse works better than expected - Chatree has to get up in the night and visit a deserted toilet. Outside, dozens of dogs are looking up at his window and howling. He's too scared to find out what's scaring them and he rushes back to the dorm. Next morning, everyone sees that he's wet the bed.
Humiliated, and now on the wrong side of Ms. Pranee, Chatree's not interested in schoolwork, or even talking to his Dad on the phone. But at least he makes a friend. Vichien (Sirachat Jiantaworn) shows him the ropes, but also tells him that some of the ghost stories he heard actually have an element of truth. But Chatree about to find out for himself...
Besides ghost stories, Dorm is about coming-of-age, with an atmosphere that changes with the central character's moods. As Chatree discovers more about the history of the school, he becomes less scared of it, and the film's horror tactics relax. The mood remains quite morbid, however, and the carefully coloured cinematography keeps us cautious.
As the lead, Chalee (or Charlie) Trairat ably carries the film, but I'd liked to have found out more about what he's thinking - he's kept too busy with moving the story forward. The gang of boys who scared him have far better, quirkier characters and provide much-needed comedy. It's Chintara Sukapatana, as the matron, who impresses the most, though I failed to recognise her from a major role in Good Morning, Vietnam.
The young director, Songyos Sugmakanan (above left) previously co-directed another Thai hit, My Girl (Fan Chan) also starring Chalee Trairat, but Dorm is his first solo directorial effort. The story is partly based on his own bittersweet experiences of boarding school, and he's taken great pains to capture the atmosphere of the time (around 1985). In interviews, Sugmakanan also voiced his concerns about how the film has been advertised. I think the posters make it look no different from gory Thai horrors out there, such as Hell or Scared.
Though the story may be too much for an audience as young as Chatree. Maybe I'm being over-protective, but I'll warn you that there's a lot of urinating in the film! Close-ups of kids wetting themselves, boys peeing in bushes, lots of scenes in toilets - you don't get this in Harry Potter. (With a Thai audience, all the pee didn't seem to be a problem, but the schoolboys swearing seemed to be unusual.)
Throw in a brief sex scene (not involving the boys) and you've got yourself a censor's headache. There's also a vivid depiction of hanging that would certainly be a problem in the UK. Incidentally, I swear that the long shot of the hanging (glimpsed in the trailer) wasn't in the print I saw in Thailand. A clue to this could be in the two different versions of the film on release in Singapore, each with a different rating.
While it's not my problem to sell the film or decide whether kids should see it, I can recommend it as a beautifully-shot film, with finely-judged performances, and an unusual story set in quite a different culture. It's scary, but also funny, dramatic, suspenseful, sad... but all-round satisfying.
Dorm opened in Thailand in February 2006 and coincidentally had a Special Presentation at the Bangkok International Film Festival the same week. It's been a box office hit domestically and I hope it finds an international audience.
Dorm was then released in the US in 2007, but with a completely misleading cover - this scene doesn't appear in the film, and it's not an 'extreme' movie either. This region 1 DVD has audio commentary, deleted scenes, and a featurette.
A reasonably-priced deluxe 2-disc DVD boxset (pictured) was released in Thailand, but region 3, and without any English subtitles. Best of the deluxe DVD extras is the uncut six-minute of 'clips' from Mr Vampire. A specially-filmed recreation that plays during the school's 'movie night' scene. It uses lookalike actors and recreates footage of the best gags from Ricky Lau's superb 1985 Hong Kong comedy horror. There is also plenty of behind-the-scenes footage, interviews, a Chalee Trairat pop video, and most of the original soundtrack.
My original March 2006 cinema review of Dorm is here
DVD Active review the US DVD
Ivan 11 September, 2013 18:25
I watched this film expecting a horror story.
It was a great disappointment, because, although it was not what I expected, it's a beautiful movie (both the script and the visual).
VOICE (WHISPERING CORRIDORS 4) Thai DVD review
MUSHISHI (2005) - a wonderful new anime series
DORM (2006) - a coming-of-age story from Thailand,...
DRAGONHEAD (2003) a realistic Japanese disaster movie
SHARK SKIN MAN AND PEACH HIP GIRL (1998) Hip Japan...
VILLAGE OF EIGHT GRAVESTONES (1977) DVD review
The DAUGHTER - a Thailand DVD horror
HAAN (2005) Pearl Harbor double-agent
SPECTREMAN (1971) - Gorilla TV
Update: TETSUJIN 28 - Region 2 UK DVD


Ing groep n.v is listed under symbol ING.
ING is going down according our predicted trendThe stochastic gives the following signals: stochastic is going down | 30. Moving average 5 is crossing below moving average 20 | 30.


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To Begin – What Business are Instructional Designers in?
Your top-most goal is Knowledge. And Knowledge is the newest Natural Resource.
Let me explain. Our species has been propelled forwards by the exploitation of pre-existing natural resources since the time of our distant ancestors. A couple of million years ago an ancestor, Homo habilis, turned a natural resource – a piece of obsidian that had been lying around for a few billion years unnoticed – into a razor-sharp tool. This sudden and evolutionary action, sparked by an idea, became the Tool Age. And we have never looked back. This ancient hominid then had to explain how his discovery worked and in doing so became the first instructional designer.
Since then it's always been people who had ideas that revolutionized and evolutionized life for us by invention and instruction. Gradually at first and speeding up to the point now where revolutions and evolutions take place within a single life span. Those under thirty take this speedy unfolding of ideas for granted – they have never known anything else. My six year old grandson was recently taken aback when handed a phone at a friend's house without a video screen!
Knowledge has always been the key to revolutions. After the Age of the Tool, which allowed us to move from scavenger to predatory food hunter, came the Age of Fire, which allowed us pre-digest food so that we did not spend so much energy chewing and digesting raw meat; and we could also light up the cave and scare away predators. Tools and fire led us to a settled agrarian existence.
The Age of Hydropower arrived when the Greeks, Romans, Egyptian and Chinese harnessed falling or fast-running water to power grinding wheels to make flour and irrigate crops. In the late eighteen hundreds hydropower began to make electrical current.
However, it was the harnessing of water in the form of steam in 1781 that drove us into the First Industrial Revolution.
In the late 19th century the greatest progress in electrical engineering took place. Bell, Edison, Kelvin, Tesla and Westinghouse turned electricity from a curiosity into the engine for the Electrical Age – the Second Industrial Revolution, helped along by that other naturally occurring, but irreplaceable, and ultimately deadly natural resource – fossil fuel.
Computer power drove the Third Industrial Revolution. Are computers a natural resource in the same sense of water, steam or electricity? Yes. Vacuum tubes were replaced by transistors which in turn gave way to electronic transistors and then integrated circuits – but all were made from naturally occurring minerals such as sand. The average computer has 66 natural resources; minerals including rare earth minerals and some nasty stuff like lead and arsenic. Integrated circuits are reaching the end of their useful life. But whatever comes next– carbon nanotubes? – will derive also from natural mineral resources.
According to the World Economic Forum and its founder Klaus Schwab, we are now entering the Fourth Industrial Revolution. And once again this revolution is fueled by natural resources. In this case the expression of human thought through software, which is only useful as a product when human engineering and design talent takes control; as with all previous revolutions.
This all brings me to the Instructional Designer. It is important to understand that the raw material of knowledge is data. And we are drowning in it. And, significantly, it does not go away. Data that was not born digital is swiftly being digitized, and data that is born digital stays that way – forever.
My goal in this post is to convince Instructional Designers to view data as a new natural resource, which means your job is to teach people how to adapt data and transform it into actionable intelligence. That is the key to the Fourth Industrial Revolution – sometimes called the Cyber-Physical Revolution – and the key is in the hands of the Instructional Designer community.
Usefulness is the key. Oil and coal or wind, solar, nuclear, electric, water and biomass for that matter, have little value without identification and extraction technologies, refining techniques, distribution systems and willing customers.
Treating data like geology helps us understand how to identify, extract, refine, distribute and make data useful by metamorphosizing it from raw data into information and then into knowledge and then into actionable intelligence.
Viewing data as knowledge is a big mistake, however. That is like equating gushing or oozing oil with jet fuel. Data is the lowest strata. It is the equivalent of sedimentary rock formed by deposits of dust and the remains of ocean-dwelling creatures laid down over aeons of time, and it will not become useful until a metamorphosis takes place as a result of a series of human actions.
So, Instructional Designers, first, identify the location of the data. Second, extract the relevant data. Next, refine it into information – Ideation. Then, distribute the information to those who need it as part of their knowledge-acquisition process. And finally you have the finished product – actionable intelligence that fills a need or solves a problem and thus allows innovation.
In other words Google finds data through Boolean logic on behalf of an inquirer. The inquirer combines these data using intellectual abilities to refine the data into information and then combines – synthesizes – the information, using intellectual skills, into something that did not exist before.
This is all great news because humans are only really happy when they are creating something. Data is all around us, and so is the opportunity to create something useful from the data. But, to do this we need to THINK and that's the purpose of Instructional Design.