{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:50Z","timestamp":1758672890504,"version":"3.44.0"},"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":[[2025,9]]},"abstract":"<jats:p>Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome limitations of traditional constraint reinforcement learning (CRL) approaches. We evaluate SCRL across two scenarios: medical resource allocation during a pandemic and pesticide distribution in agriculture. Experiments demonstrate that SCRL outperforms existing baselines in satisfying constraints while maintaining high resource efficiency, showcasing its potential for real-world, context-sensitive decision-making tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1014","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9121-9129","source":"Crossref","is-referenced-by-count":0,"title":["Situational-Constrained Sequential Resources Allocation via Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Libo","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China"},{"name":"The University of Auckland"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"UNSW"}]},{"given":"Toru","family":"Takisaka","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}]},{"given":"Kaiqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"The University of Auckland"}]},{"given":"Weidong","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Auckland"}]},{"given":"Jiamou","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Auckland"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:51Z","timestamp":1758627351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1014"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1014","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}