Probabilistic Agent Programs

Agents are small programs that autonomously take actions based on changes in their environment or “state.” Over the last few years, there has been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts Eiter et al. [1999] have shown how agents may be built on top of legacy code. However, their framework assumes that agent states are completely determined, and there is no uncertainty in an agent's state. Thus, their framework allows an agent developer to specify how his agents will react when the agent is 100% sure about what is true/false in the world state. In this paper, we propose the concept of a probabilistic agent program and show how, given an arbitrary program written in any imperative language, we may build a declarative “probabilistic” agent program on top of it which supports decision making in the presence of uncertainty. We provide two alternative semantics for probabilistic agent programs. We show that the second semantics, though more epistemically appealing, is more complex to compute. We provide sound and complete algorithms to compute the semantics of positive agent programs.