{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interactive-pomdp-lite-towards-practical","title":"Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents","arxiv_id":"1304.5159","date":"2013-04-18","proceeding":null,"authors":["Trong Nghia Hoang","Kian Hsiang Low"],"abstract":"A key challenge in non-cooperative multi-agent systems is that of developing\nefficient planning algorithms for intelligent agents to interact and perform\neffectively among boundedly rational, self-interested agents (e.g., humans).\nThe practicality of existing works addressing this challenge is being\nundermined due to either the restrictive assumptions of the other agents'\nbehavior, the failure in accounting for their rationality, or the prohibitively\nexpensive cost of modeling and predicting their intentions. To boost the\npracticality of research in this field, we investigate how intention prediction\ncan be efficiently exploited and made practical in planning, thereby leading to\nefficient intention-aware planning frameworks capable of predicting the\nintentions of other agents and acting optimally with respect to their predicted\nintentions. We show that the performance losses incurred by the resulting\nplanning policies are linearly bounded by the error of intention prediction.\nEmpirical evaluations through a series of stochastic games demonstrate that our\npolicies can achieve better and more robust performance than the\nstate-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1304.5159v1","url_pdf":"http://arxiv.org/pdf/1304.5159v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interactive-pomdp-lite-towards-practical","repo_url":"https://github.com/emiudeh/IPOMDP-Lite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1304.5159","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}