Probabilistic planning in agentspeak using the POMDP framework

Godo, Llu�s; Bauters, Kim; McAreavey, Kevin; Hong, Jun; Chen, Yingke; Liu, Weiru; Godo, Llu\'\is; Sierra, Carles

Abstract

© Springer International Publishing Switzerland 2016. AgentSpeak is a logic-based programming language, based on the Belief- Desire-Intention paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In thiswork, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when faced with uncertainty.

• Presentation Conference Type: Conference Paper (published)
• Publication Date: Jan 1, 2016
• Deposit Date: Feb 17, 2017
• Journal: Smart Innovation, Systems and Technologies
• Electronic ISSN: 2190-3026
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Pages: 19-37
• Book Title: Combinations of Intelligent Methods and Applications
• ISBN: 9783319268583
• DOI: https://doi.org/10.1007/978-3-319-26860-6_2
• Keywords: probabilistic planning, AgentSpeak, POMDP
• Public URL: https://uwe-repository.worktribe.com/output/914774
• Publisher URL: http://rd.springer.com/chapter/10.1007/978-3-319-26860-6_2
• Contract Date: Feb 17, 2017