DELPHIC is a novel semantics for Dynamic Epistemic Logic based on an alternative representation of epistemic states called possibilities. DELPHIC ASP is an Answer Set Programming model of a DEL-based epistemic planner built on top of the DELPHIC semantics. Since the DELPHIC semantics comprises the full range of DEL actions, DELPHIC ASP generalizes the PLATO solver.
Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in “simple” domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents’ beliefs about themselves and about other agents’ beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
PLATO is an Answer Set Programming (ASP) model implementing an epistemic planner that is built on top a fragment of Dynamic Epistemic Logic called $m\mathcal{A}^*$. The fragment comprises (public and private) ontic actions and (public, private and semi-private) sensing and announcement actions. The planner implements both a Kripke semantics for the fragment and a novel alternative semantics based on objects called possibilities. PLATO is implemented using the clingo solver for ASP programs, which provides some useful APIs that allow for solving problems in an iterative fashion. In this way, we can try to compute plans of increasing length, akin to a BFS visit. The goal of PLATO is to investigate the practical usability of ASP in the implementation of epistemic planners.