Deepak Khemani

The AI community has always been interested in designing intelligent agents which function in a multi-agent arrangement or a man-machine scenario. More often than not, such settings may require agents to work autonomously (or under intermittent supervision at the least) in partially observable environments. Over the last 10 years or so, the planning community has started looking at this interesting class of problems from an epistemic standpoint, by augmenting the notions of knowledge and beliefs to AI planning. In this paper, we present a system that synthesizes plans from the primary agent's perspective, based on its subjective knowledge, in a multi-agent environment. We adopt a semantic approach to represent the mental model of the primary agent whose uncertainty about the world is represented using Kripke's possible worlds interpretation of epistemic logic. Planning in this logical framework is computationally challenging, and, to the best of our knowledge, most of the existing planners work with the notion of knowledge, instead of an agent's subjective knowledge. We demonstrate the system's capability of projecting beliefs of the primary agent on to others, reasoning about the role of other agents in the prospective plans, and preferring the plans that hinge on the primary agent's capabilities to those which demand others' cooperation. We evaluate our system on the problems discussed in the literature and show that it takes fractions of seconds to search for a plan for a given problem. We also discuss the issues that arise in modeling dynamic domains with the representation our system employs.

The ability of an agent to distinguish the ramification effects of an action from its direct effects adds value to the explainability of its decisions. In this work, we propose to encode the ramification effects of ontic and epistemic actions as single-point update models in an epistemic planning domain modeled with Kripkean semantics of Knowledge and Belief. We call them “mental actions”. We discuss a preliminary approach to realize our idea, and we conclude by pointing out some optimizations as our ongoing pursuit.