A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Ivan Palomares, Ronan Killough, Kim Bauters, Weiru Liu, Jun Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
295 Downloads (Pure)


Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions. 
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Tools with Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
Publication statusPublished - 16 Jan 2017
EventICTAI 2016: 28th IEEE International Conference on Tools with Artificial Intelligence - USA, San Jose, United States
Duration: 06 Nov 201608 Nov 2016


ConferenceICTAI 2016
Country/TerritoryUnited States
CitySan Jose
Internet address


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