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.
|Title of host publication||Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART'16)|
|Publication status||Published - 2016|
|Event||8th International Conference on Agents and Artificial Intelligence - Rome, Italy|
Duration: 24 Feb 2016 → 26 Feb 2016
http://www.icaart.org/?y=2016 (Link to event details online)
|Conference||8th International Conference on Agents and Artificial Intelligence|
|Period||24/02/2016 → 26/02/2016|