Probabilistic Planning in AgentSpeak using the POMDP framework.

Kim Bauters, Kevin McAreavey, Jun Hong, Yingke Chen, Weiru Liu, Lluis Godo, Carles Sierra

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention (BDI) 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 this paper, 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 facing uncertainty.
Original languageEnglish
Title of host publicationCombinations of Intelligent Methods and Applications (An invited, extended version of CIMA'14 paper)
EditorsI Hatzilygeroudis, V. Palade, J. Prentzas
Number of pages19
Publication statusAccepted - 2015


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