Risk-aware Planning in BDI Agents

Ronan Killough, Kim Bauters, Kevin McAreavey, Weiru Liu, Jun Hong

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

3 Citations (Scopus)

Abstract

The ability of an autonomous agent to select rational actions is vital in enabling it to achieve its goals. To do so effectively in a high-stakes setting, the agent must be capable of considering the risk and potential reward of both immediate and future actions. In this paper we provide a novel method for calculating risk alongside utility in online planning algorithms. We integrate such a risk-aware planner with a BDI agent, allowing us to build agents that can set their risk aversion levels dynamically based on their changing beliefs about the environment. To guide the design of a risk-aware agent we propose a number of principles which such an agent should adhere to and show how our proposed framework satisfies these principles. Finally, we evaluate our approach and demonstrate that a dynamically risk-averse agent is capable of achieving a higher success rate than an agent that ignores risk, while obtaining a higher utility than an agent with a static risk attitude.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART'16)
PublisherSciTePress
Pages322-329
ISBN (Print)978-989-758-172-4
DOIs
Publication statusPublished - 2016
Event8th International Conference on Agents and Artificial Intelligence - Rome, Italy
Duration: 24 Feb 201626 Feb 2016
http://www.icaart.org/?y=2016 (Link to event details online)

Conference

Conference8th International Conference on Agents and Artificial Intelligence
CountryItaly
CityRome
Period24/02/201626/02/2016
Internet address

Bibliographical note

accepted as short paper

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  • Cite this

    Killough, R., Bauters, K., McAreavey, K., Liu, W., & Hong, J. (2016). Risk-aware Planning in BDI Agents. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART'16) (pp. 322-329). SciTePress. https://doi.org/10.5220/0005703103220329