{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:56Z","timestamp":1723016096968},"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":[[2021,8]]},"abstract":"<jats:p>The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/554","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:00:49Z","timestamp":1628665249000},"page":"4024-4031","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Black-Box Planning Using Macro-Actions with Focused Effects"],"prefix":"10.24963","author":[{"given":"Cameron","family":"Allen","sequence":"first","affiliation":[{"name":"Brown University"},{"name":"IBM Research"}]},{"given":"Michael","family":"Katz","sequence":"additional","affiliation":[{"name":"IBM Research"}]},{"given":"Tim","family":"Klinger","sequence":"additional","affiliation":[{"name":"IBM Research"}]},{"given":"George","family":"Konidaris","sequence":"additional","affiliation":[{"name":"Brown University"}]},{"given":"Matthew","family":"Riemer","sequence":"additional","affiliation":[{"name":"IBM Research"}]},{"given":"Gerald","family":"Tesauro","sequence":"additional","affiliation":[{"name":"IBM Research"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:04:01Z","timestamp":1628665441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/554"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/554","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}