Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Most approaches to learning action planning models heavily rely on a significantly large volume of training samples or plan observations.
In this paper, we adopt a different approach based on deductive learning from domain-specific knowledge, specifically from logic formulae that specify constraints about the possible states of a given domain.
The minimal input observability required by our approach is a single example composed of a full initial state and a partial goal state.
We will show that exploiting specific domain knowledge enables to constrain the space of possible action models as well as to complete partial observations, both of which turn out helpful to learn good-quality action models.
The learning of action models in planning has been typically addressed with inductive learning data-intensive approaches.
From the pioneer learning system ARMS BID13 ) to more recent ones BID8 Zhuo and Kambhampati 2013; Kucera and Barták 2018) , all of them require thousands of plan observations or training samples, i.e., sequences of actions as evidence of the execution of an observed agent, to obtain and validate an action model.
These approaches return the statistically significant model that best explains the plan observations by minimizing some error metric.
A model explains an observation if a plan containing the observed actions is computable with the model and the states induced by this plan also include the possibly partially observed states.
The limitation of posing model learning and validation as optimization tasks over a set of observations is that it neither guarantees completeness (the model may not explain all the observations) nor correctness (the states induced by the execution of the plan generated with the model may contain contradictory information).Differently
, other approaches rely on symbolic-via learning. The Simultaneous
Learning and Filtering (SLAF) approach BID2 exploits logical inference and builds a complete explanation through a CNF formula that represents the initial belief state, and a plan observation. The formula is updated
with every action and state of Copyright c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. the
observation, thus representing
all possible transition relations consistent with it. SLAF extracts all satisfying models
of the learned formula with a SAT solver although the algorithm cannot effectively learn the preconditions of actions. A more recent approach addresses the
learning of action models from plan observations as a planning task which searches the space of all possible action models BID0 . A plan here is conceived as a series
of steps that determine the preconditions and effects of the action models plus other steps that validate the formed actions in the observations. The advantage of this approach is that
it only requires input samples of about a total of 50 actions.This paper studies the impact of using mixed input data, i.e, automatically-collected plan observations and humanencoded domain-specific knowledge, in the learning of action models. Particularly, we aim to stress the extreme
case of having a single observation sample and answer the question to whether the lack of training samples can be overcome with the supply of domain knowledge. The question is motivated by (a) the assumption
that obtaining enough training
observations is often difficult and costly, if not impossible in some domains (Zhuo 2015); (b) the fact that although the physics of the real-world
domain being modeled are unknown, the user may know certain pieces of knowledge about the domain; and (c) the desire for correct action models that are usable
beyond their fitness to a set of testing observations. To this end, we opted for checking our hypothesis in the
framework proposed in BID0 since this planning-based satisfiability approach allows us to configure additional constraints in the compilation scheme, it is able to work under a minimal set of observations and uses an off-the-shelf planner 1 . Ultimately, we aim to compare the informational power of
domain observations (information quantity) with the representational power of domainspecific knowledge (information quality). Complementarily, we restrict our attention to solely observations
over fluents as in many applications the actual actions of an agent may not be observable BID11 .Next section summarizes basic planning concepts and outlines the baseline
learning approach BID0 ). Then we formalize our one-shot learning task with domain knowledge and subsequently
we explain the task-solving process. Section 5 presents the experimental evaluation and last section concludes.
We present an approach to learn action models that builds upon a former compilation-to-planning learning system BID0 .
Our proposal studies the gains of using domain-specific knowledge when the availability (amount and observability) of learning examples is very limited.
Introducing domain knowledge encoded as schematic mutexes allows to narrow down the search space of the learning task and improve overall the performance of the learning system to the point that it offsets the lack of learning examples.
In a theoretical work that analyzes the relation between the number of observed trajectory plans and the guarantee for a learned action model to achieve the goal BID12 , authors conclude that the number of trajectories needed scales gracefully and the guarantee grows linearly with the number of predicates and quasi-linearly with the number of actions.
This evidences that learning accurate models is heavily dependent on the number and quality (observability) of the learning examples.
In this sense, our proposal comes to alleviate this dependency by relying on easily deducible domain knowledge.
It is not only capable of learning from a single non-fully observable learning example but also proves that learning from a 30%-observable example with domain-specific knowledge is comparable to learning from a complete plan observation.
Hybrid approach to model acquisition that compensates a lack of available data with domain specific knowledge provided by experts