There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users. One aspect of the explanation generation problem that has yet to receive much attention is the task of explaining plans to users whose level of expertise differ from that of the explainer. We propose an approach for addressing this problem by representing the user's model as an abstraction of the domain model that the planner uses. We present algorithms for generating minimal explanations in cases where this abstract human model is not known. We reduce the problem of generating explanation to a search over the space of abstract models and investigate possible greedy approximations for minimal explanations. We also empirically show that our approach can efficiently compute explanations for a variety of problems.
Many abstraction schemes have been proposed for planning tasks (starting with @cite_10 ), but in this paper, we mainly focused on state abstractions and based our formulation on previous works like @cite_1 and @cite_8 . It would be interesting to see how we can extend the approaches discussed in this paper to handle temporal and procedural abstractions (e.g., HLAs @cite_14 ).
@cite_1 @cite_14 @cite_10 @cite_8
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State abstraction (or state aggregation) has been extensively studied in the fields of artificial intelligence and operations research. Instead of working in the ground state space, the decision maker usually finds solutions in the abstract state space much faster by treating groups of states as a unit by ignoring irrelevant state information. A number of abstractions have been proposed and studied in the reinforcement-learning and planning literatures, and positive and negative results are known. We provide a unified treatment of state abstraction for Markov decision processes. We study five particular abstraction schemes, some of which have been proposed in the past in different forms, and analyze their usability for planning and learning. Abstract Metric temporal planning involves both selecting and organising actions to satisfy the goals and also assigning to each of these actions its start time and, where necessary, its duration. The assignment of start times to actions is a central concern of scheduling. In pddl 2.1, the widely adopted planning domain description language standard, metric temporal planning problems are described using actions with durations. A large number of planners have been developed to handle this language, but the great majority of them are fundamentally limited in the class of temporal problems they can solve. In this paper, we review the source of this limitation and present an approach to metric temporal planning that is not so restricted. Our approach links planning and scheduling algorithms into a planner, Crikey , that can successfully tackle a wide range of temporal problems. We show how Crikey can be simplified to solve a wide and interesting subset of metric temporal problems, while remaining competitive with other temporal planners that are unable to handle required concurrency. We provide empirical data comparing the performance of this planner, CRIKEY SHE , our original version, Crikey , and a range of other modern temporal planners. Our contribution is to describe the first competitive planner capable of solving problems that require concurrent actions. Temporal abstraction has long been recognized as key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions in the options framework is well understood, learning such abstractions autonomously is still challenging. We present recent results extending the policy gradient theorem to options, and propose a new option-critic architecture. Temporal abstraction is key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions is well understood, creating such abstractions autonomously from data has remained challenging. We tackle this problem in the framework of options [Sutton, Precup & Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options and propose a new option-critic architecture capable of learning both the internal policies and the termination conditions of options, in tandem with the policy over options, and without the need to provide any additional rewards or subgoals. Experimental results in both discrete and continuous environments showcase the flexibility and efficiency of the framework.