Abstract
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 this work, we propose the \(\text {AgentSpeak}^+\) framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an \(\text {AgentSpeak}^+\) 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.
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Notes
- 1.
A variable is a term, a constant is a term, and from every n terms \(t_1, t_2, \ldots , t_n\) and every n-ary predicate p a new term \(p(t_1, t_2, \ldots , t_n)\) can be created.
- 2.
Not to be confused with the belief base of an agent, which we see later.
- 3.
For simplicity, we restrict ourselves in this work to the case where each input is relevant to only one epistemic state.
- 4.
By the author Yingke Chen.
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Bauters, K. et al. (2016). Probabilistic Planning in AgentSpeak Using the POMDP Framework. In: Hatzilygeroudis, I., Palade, V., Prentzas, J. (eds) Combinations of Intelligent Methods and Applications. Smart Innovation, Systems and Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-26860-6_2
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DOI: https://doi.org/10.1007/978-3-319-26860-6_2
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