Explaining Unsolvability of Planning Problems in Hybrid Systems with Model Reconciliation - Sarwar, Mir Md Sajid; Ray, Rajarshi; Banerjee, Ansuman (Proceedings (ACM and IEEE International Conference on Formal Methods and Models for Co-Design), 2023)
A recent problem of interest in Explainable AI Planning is that of explaining the unsolvability of planning problems. Though there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains a largely open and understudied problem. Model reconciliation has been a popular approach for generating explanations for such problems in recent literature, which involves an AI agent and a human planner, who have different models of the planning domain and each explains to the other the differences they have in their domain representations and attempt to arrive at a consensus. More often than not, it is assumed that the AI agent has a correct and complete view of the domain, of which the human only has a partial view. Through reconciliation, the human domain is updated to be consistent with what the AI agent has. Most of the works in this direction are targeted toward classical planning problems on domains represented typically as discrete state transition systems or variants. In this paper, we provide an approach towards model reconciliation for planning problems in hybrid systems represented as a mix of discrete and continuous domains. We assume that the agent has a complete model of the environment, while the human has a partial or erroneous model and expects a plan for the planning problem when there is none. The explanation problem is presented as a process of continuous reconciliation between these two entities (agent and human) to make the human domain consistent with that of the agent. To this effect, we use a mix of graph traversal and path analysis, along with Linear programming to carry out the reconciliation process. In particular, we use the concept of Irreducible Infeasible Sets (IIS) to generate explanations. Experimental results on 2 representative hybrid domains show the efficacy of our approach.CCS Concepts:* Computing methodologies → Artificial intelligence; * Human-centered computing → Interactive systems and tools; * Theory of computation → Timed and hybrid models.