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.
In this paper we examine the general problem of generating preferred explanations for observed behavior with respect to a model of the behavior of a dynamical system. This problem arises in a diversity of applications including diagnosis of dynamical systems and activity recognition. We provide a logical characterization of the notion of an explanation. To generate explanations we identify and exploit a correspondence between explanation generation and planning. The determination of good explanations requires additional domain-specific knowledge which we represent as preferences over explanations. The nature of explanations requires us to formulate preferences in a somewhat retrodictive fashion by utilizing Past Linear Temporal Logic. We propose methods for exploiting these somewhat unique preferences effectively within state-of-the-art planners and illustrate the feasibility of generating (preferred) explanations via planning. In various social, work-related, or educational contexts, an increasing demand for intelligent assistance systems can be observed. In this paper, we present a domain-independent approach that combines a number of planning and interaction components to realize advanced user assistance. Based on a hybrid planning formalism, the components provide facilities including the generation, execution, and repair as well as the presentation and explanation of plans. We demonstrate the feasibility of our approach by means of a system that aims to assist users in the assembly of their home theater. An empirical evaluation shows the benefit of such a supportive system, in particular for persons with a lack of domain expertise. In this paper we present a new approach to plan understanding that explains observed actions in terms of domain knowledge. The process operates over hierarchical methods and utilizes an incremental form of data-driven abductive inference. We report experiments on problems from the Monroe corpus that demonstrate a basic ability to construct plausible explanations, graceful degradation of performance with reduction of the fraction of actions observed, and results with incremental processing that are comparable to batch interpretation. We also discuss research on related tasks such as plan recognition and abductive construction of explanations. Human users who execute an automatically generated plan want to understand the rationale behind it. Knowledge-rich plans are particularly suitable for this purpose, because they provide the means to give reason for causal, temporal, and hierarchical relationships between actions. Based on this information, focused arguments can be generated that constitute explanations on an appropriate level of abstraction. In this paper, we present a formal approach to plan explanation. Information about plans is represented as first-order logic formulae and explanations are constructed as proofs in the resulting axiomatic system. With that, plan explanations are provably correct w.r.t. the planning system that produced the plan. A prototype plan explanation system implements our approach and first experiments give evidence that finding plan explanations is feasible in real-time.  When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms. 