Efficient Intention Selection in BDI Agents via Decision-Theoretic Task Scheduling

摘要

This paper shows how to use a decision-theoretic task scheduler in order to automatically generate efficient intention selection functions for BDI agent-oriented programming languages. We concentrate here on the particular extensions to a known BDI language called AgentSpeak(L) and its interpreter which were necessary so that the integration with a task scheduler was possible. The proposed language, called AgentSpeak(XL), has several other features which increase its usability; some of these are indicated briefly in this paper. We assess the extended language and its interpreter by means of a factory plant scenario where there is one mobile robot that is in charge of packing and storing items, besides other administrative and security tasks. This case study and its simulation results show that, in comparison to AgentSpeak(L), AgentSpeak(XL) provides much easier and efficient implementation of applications that require quantitative reasoning, or require specific control over intentions (e.g., for giving priority to certain tasks once they become intended).