{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T22:00:54Z","timestamp":1747173654682,"version":"3.40.5"},"reference-count":28,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T00:00:00Z","timestamp":1600646400000},"content-version":"unspecified","delay-in-days":20,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 \u201csimple\u201d domains the agents can solely rely on facts about the world, in several contexts,<jats:italic>e.g.<\/jats:italic>, economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios,<jats:italic>epistemic<\/jats:italic>reasoning,<jats:italic>i.e.<\/jats:italic>, reasoning about agents\u2019 beliefs about themselves and about other agents\u2019 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<jats:italic>Answer Set Programming<\/jats:italic>-based planner that can reason in multi-agent epistemic settings, called PLATO (e<jats:bold>P<\/jats:bold>istemic mu<jats:bold>L<\/jats:bold>ti-agent<jats:bold>A<\/jats:bold>nswer se<jats:bold>T<\/jats:bold>programming s<jats:bold>O<\/jats:bold>lver). 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.<\/jats:p>","DOI":"10.1017\/s1471068420000289","type":"journal-article","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T05:25:42Z","timestamp":1600665942000},"page":"593-608","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":7,"title":["Modelling Multi-Agent Epistemic Planning in ASP"],"prefix":"10.1017","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9977-6735","authenticated-orcid":false,"given":"ALESSANDRO","family":"BURIGANA","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"FRANCESCO","family":"FABIANO","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-8593","authenticated-orcid":false,"given":"AGOSTINO","family":"DOVIER","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7753-1737","authenticated-orcid":false,"given":"ENRICO","family":"PONTELLI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2020,9,21]]},"reference":[{"unstructured":"1. 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