You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this inferred intent to predict, imitate, or assist the human user. However, a central assumption in inverse reinforcement learning is that the demonstrator is close to optimal. While models of suboptimal behavior exist, they typically assume that suboptimal actions are the result of some type of random noise or a known cognitive bias, like temporal inconsistency. In this paper, we take an alternative approach, and model suboptimal behavior as the result of internal model misspecification: the reason that user actions might deviate from near-optimal actions is that the user has an incorrect set of beliefs about the rules -- the dynamics -- governing how actions affect the environment. Our insight is that while demonstrated actions may be suboptimal in the real world, they may actually be near-optimal with respect to the user's internal model of the dynamics. By estimating these internal beliefs from observed behavior, we arrive at a new method for inferring intent. We demonstrate in simulation and in a user study with 12 participants that this approach enables us to more accurately model human intent, and can be used in a variety of applications, including offering assistance in a shared autonomy framework and inferring human preferences.

Cited Abstract(s):
[1]: In many settings, people exhibit behavior that is inconsistent across time ' we allocate a block of time to get work done and then procrastinate, or put effort into a project and then later fail to complete it. An active line of research in behavioral economics and related fields has developed and analyzed models for this type of time-inconsistent behavior. Here we propose a graph-theoretic model of tasks and goals, in which dependencies among actions are represented by a directed graph, and a time-inconsistent agent constructs a path through this graph. We first show how instances of this path-finding problem on different input graphs can reconstruct a wide range of qualitative phenomena observed in the literature on time-inconsistency, including procrastination, abandonment of long-range tasks, and the benefits of reduced sets of choices. We then explore a set of analyses that quantify over the set of all graphs; among other results, we find that in any graph, there can be only polynomially many distinct forms of time-inconsistent behavior; and any graph in which a time-inconsistent agent incurs significantly more cost than an optimal agent must contain a large 'procrastination' structure as a minor. Finally, we use this graph-theoretic model to explore ways in which tasks can be designed to help motivate agents to reach designated goals.

[2]: This paper describes three heuristics, or mental operations, that are employed in judgment under uncertainty. (i) An assessment of representativeness or similarity, which is usually performed when people are asked to judge the probability that an object or event A belongs to a class or process B. (ii) An assessment of the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development. (iii) An adjustment from a starting point, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.

[3]: 

[4]: In this lecture I shall focus on situations involving repeated decisions with time inconsistent behavior. Although each choice may be close to maximizing and therefore result in only small losses, the cumulative effect of a series of repeated errors may be quite large. Thus, in my examples, decision makers are quite close to the intelligent, well-informed individuals usually assumed in economic analysis, but cumulatively they make seriously wrong decisions that do not occur in standard textbook economics. This lecture discusses and illustrates several "pathological" modes of individual and group behavior: procrastination in decision making, undue obedience to authority, membership of seemingly normal individuals in deviant cult groups, and escalation of commitment to courses of action that are clearly unwise. In each case, individuals choose a series of current actions without fully appreciating how those actions will affect future perceptions and behavior. The standard assumption of rational, forwardlooking, utility maximizing is violated. The nonindependence of errors in decision making in the series of decisions can be explained with the concept from cognitive psychology of undue salience or vividness. For example, present benefits and costs may have undue salience relative to future costs
Modeling human error has a rich history in the behavioral sciences. Procrastination and other time-inconsistent human behaviors have been characterized as rational with respect to a cost model that discounts the cost of future action relative to that of immediate action [4] [1] . Systematic errors in human predictions about the future have been partially explained by cognitive biases like the availability heuristic and regression to the mean [2] . Imperfect intuitive physics judgments have been characterized as approximate probabilistic inferences made by a resource-bounded observer [3] . We take an orthogonal approach in which we assume that suboptimal behavior is primarily caused by incorrect beliefs of the dynamics, rather than uncertainty or biases in planning and judgment.