In this work, we formulate the process of generating explanations as model reconciliation for planning problems as one of planning with explanatory actions. We show that these problems could be better understood within the framework of epistemic planning and that, in fact, most earlier works on explanation as model reconciliation correspond to tractable subsets of epistemic planning problems. We empirically show how our approach is computationally more efficient than existing techniques for explanation generation and we end the paper with a discussion of how this formulation could be extended to generate novel explanatory behaviors.
Epistemic planning is a very expressive framework that extends automated planning by the incorporation of dynamic epistemic logic (DEL). We provide complexity results on the plan existence problem for multi-agent planning tasks, focusing on purely epistemic actions with propositional preconditions. We show that moving from epistemic preconditions to propositional preconditions makes it decidable, more precisely in EXPSPACE. The plan existence problem is PSPACE-complete when the underlying graphs are trees and NP-complete when they are chains (including singletons). We also show PSPACE-hardness of the plan verification problem, which strengthens previous results on the complexity of DEL model checking. Single-agent planning in partially observable settings is a well understood problem and existing planners can represent and solve a wide variety of meaningful instances. In the most common formulation, the problem is cast as a non-deterministic search problem in belief space where beliefs are sets of states that the agent regards as possible. In this work, we build on the methods developed for representing beliefs in single-agent planning to introduce a simple but expressive formulation for handling beliefs in multi-agent settings. The resulting formulation deals with multiple agents that can act on the world (physical or ontic actions), and can sense either the state of the world (truth of objective formulas) or the mental state of other agents (truth of epistemic formulas). The formulation captures and defines a fragment of dynamic epistemic logics that is simple and expressive but which does not involve event models or product updates, and has the same complexity of belief tracking in the single agent setting and can benefit from the use of similar techniques. We show indeed that the problem of computing multiagent linear plans can be actually compiled into a classical planning problem using the techniques that have been developed for compiling conformant and contingent problems in the single agent setting and report experimental results.   In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results. To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternating cover disjunctive formulas (ACDFs). We propose basic revision and update algorithms for ACDFs. We also handle static propositional common knowledge, which we call constraints. Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach. Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.  Attribution theorists typically have conceived the attribution process in terms of universal laws of cognitive functioning, independent of social interaction. In this paper we argue for the notion, grounded in recent ordinary language philosophy, that any consideration of the form of everyday explanation must take into account its function as an answer to a ‘why’ question within a conversational framework. Experiment 1 provides support for the idea that speakers should identify as causally relevant that necessary condition for the occurrence of an event about which the enquirer is ignorant. Experiment 2 replicates this basic finding and further demonstrates that speakers will change their explanations to enquirers believed to be sharing different knowledge about the same target event. Experiment 2 also assessed the role of individual differences in conversational rule-following, and found in apparent contrast some previous predictions that high self-monitoring individuals were no more likely than lows to tailor their explanations to suit the enquirer's knowledge state. If anything, the reverse occurred. Taken together, these experiments support the central contention of the abnormal conditions focus model (Hilton and Slugoski, 1986), that the common sense criterion of causality is that of an ‘abnormal condition’ rather than constant conjunction as instantiated in the ANOVA model of causal attribution (Kelley, 1967, 1973).