{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:58Z","timestamp":1723016098057},"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>Previous work on satisficing planning using greedy best-first search (GBFS) has shown that non-greedy, randomized exploration can help escape uninformative heuristic regions and solve hard problems faster. Despite their success when used with GBFS, such exploration techniques cannot be directly applied to bounded suboptimal algorithms like Weighted A* (WA*) without losing the solution-quality guarantees. In this work, we present Type-WA*, a novel bounded suboptimal planning algorithm that augments WA* with type-based exploration while still satisfying WA*'s theoretical solution-quality guarantee. Our empirical analysis shows that Type-WA* significantly increases the number of solved problems, when used in conjunction with each of three popular heuristics. Our analysis also provides insight into the runtime vs. solution cost trade-off.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/557","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"4047-4053","source":"Crossref","is-referenced-by-count":0,"title":["Type-WA*: Using Exploration in Bounded Suboptimal Planning"],"prefix":"10.24963","author":[{"given":"Eldan","family":"Cohen","sequence":"first","affiliation":[{"name":"University of Toronto"},{"name":"Vector Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Valenzano","sequence":"additional","affiliation":[{"name":"Ryerson University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheila","family":"McIlraith","sequence":"additional","affiliation":[{"name":"University of Toronto"},{"name":"Vector Institute"},{"name":"Schwartz Reisman Institute for Technology and Society"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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":"2021-08-11T11:04:02Z","timestamp":1628679842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/557"}},"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\/557","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}