{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:37:47Z","timestamp":1723016267142},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>A common approach to planning with partial information is replanning: compute a plan based on assumptions about unknown information and replan if these \nassumptions are refuted during execution. To date, most planners with incomplete information have been designed to provide guarantees on completeness and soundness for the generated plans. Switching focus to performance, we measure the robustness of a plan, which quanti\ufb01es the plan\u2019s ability to avoid failure. Given a plan and an agent\u2019s belief, which describes the set of states it deems as possible, robustness counts the number of world states in the belief from which the plan will achieve the goal without the need to replan. We formally describe the trade-off between robustness and plan cost and offer a solver that is guaranteed to produce plans that satisfy a required level of robustness. By evaluating our approach on a set of standard benchmarks, we demonstrate how it can improve the performance of a partially informed agent.<\/jats:p>","DOI":"10.24963\/kr.2020\/55","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T04:39:16Z","timestamp":1597898356000},"page":"550-559","source":"Crossref","is-referenced-by-count":0,"title":["Reasoning About Plan Robustness Versus Plan Cost for Partially Informed Agents"],"prefix":"10.24963","author":[{"given":"Sarah","family":"Keren","sequence":"first","affiliation":[{"name":"School of Engineering and Applied Sciences, Harvard University"},{"name":"Center for Research on Computation and Society at Harvard University"}]},{"given":"Sara","family":"Bernardini","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Royal Holloway University of London"}]},{"given":"Kofi","family":"Kwapong","sequence":"additional","affiliation":[{"name":"School of Engineering and Applied Sciences, Harvard University"}]},{"given":"David C.","family":"Parkes","sequence":"additional","affiliation":[{"name":"School of Engineering and Applied Sciences, Harvard University"}]}],"member":"10584","event":{"number":"17","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Association for Logic Programming","Center for Perspicuous Computing","European Association for Artificial Intelligence","Ontopic - The Virtual Knowledge Graph Company"],"acronym":"KR-2020","name":"17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}","start":{"date-parts":[[2020,9,12]]},"theme":"Artificial Intelligence","location":"Rhodes, Greece","end":{"date-parts":[[2020,9,18]]}},"container-title":["Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T21:18:42Z","timestamp":1604611122000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2020\/55"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2020\/55","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}