Probabilistic Agent Programs

TL;DR

This paper introduces probabilistic agent programs that enable decision making under uncertainty, extending previous deterministic frameworks by supporting probabilistic reasoning and providing algorithms for their semantics.

Contribution

It proposes a new framework for probabilistic agent programs built on arbitrary imperative code, with two semantics and algorithms for positive programs.

Findings

Two alternative semantics for probabilistic agent programs.

Algorithms for computing semantics of positive probabilistic agent programs.

Extension of agent frameworks to handle uncertainty.

Abstract

Agents are small programs that autonomously take actions based on changes in their environment or ``state.'' Over the last few years, there have been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts, Eiter, Subrahmanian amd Pick (AIJ, 108(1-2), pages 179-255) 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 \emph{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 \emph{positive} agent programs.