{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T12:20:45Z","timestamp":1648556445781},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[1992,3]]},"abstract":"<jats:p> This paper presents a new paradigm for constructing a partially ordered plan network, referred to as \"resource-oriented parallel planning (RP<jats:sup>2<\/jats:sup>)\". In resource-oriented parallel planning, goal states are grouped into a collection of goal state subsets, each of which consists of those goal states associated with a particular resource. A subplan is generated for each goal state subset by planning the flow of the corresponding resource, and thus satisfying the constraints specified by each of the goal state subsets. A complete plan is then constructed by synthesizing individual subplans based on the synchronization among subplans. <\/jats:p><jats:p> A distinctive feature of RP<jats:sup>2<\/jats:sup> is that each subplanner generates a conflict-free subplan by controlling the flow of a particular resource while synthesizing a complete plan in cooperation with other subplanners. This can be compared with the conventional state-oriented parallel planning, where each subplanner generates a subplan achieving a goal state while resolving conflicts among subplans in cooperation with other subplanners. <\/jats:p><jats:p> RP<jats:sup>2<\/jats:sup> makes use of resource reasoning in distinguishing important and unimportant resources associated with a goal. This allows each subplanner to hierarchically construct a resource-flow plan and to dynamically coordinate among subplanners in achieving a goal associated with more than one resource. <\/jats:p><jats:p> The proposed scheme not only makes it possible to generate an optimal plan in terms of the maximum parallelism or the minimum depth of a plan network, but also makes it easier to implement the concurrent generation of parallel plans in a parallel and distributed processing environment. <\/jats:p>","DOI":"10.1142\/s0218213092000144","type":"journal-article","created":{"date-parts":[[2004,11,23]],"date-time":"2004-11-23T22:29:42Z","timestamp":1101248982000},"page":"85-115","source":"Crossref","is-referenced-by-count":1,"title":["RESOURCE-ORIENTED PARALLEL PLANNING"],"prefix":"10.1142","volume":"01","author":[{"given":"SUKHAN","family":"LEE","sequence":"first","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"},{"name":"Department of Electrical Engineering Systems, University of Southern California, Los Angeles, CA 90089-0782, USA"}]},{"given":"KYUSIK","family":"CHUNG","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Soongsil University, Seoul, Korea"}]}],"member":"219","published-online":{"date-parts":[[2012,1,25]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213092000144","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T19:10:48Z","timestamp":1565118648000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213092000144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1992,3]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2012,1,25]]},"published-print":{"date-parts":[[1992,3]]}},"alternative-id":["10.1142\/S0218213092000144"],"URL":"https:\/\/doi.org\/10.1142\/s0218213092000144","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[1992,3]]}}}