Probabilistic Agent Programs
Probabilistic Agent Programs
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Date
1999-10-22
Authors
Dix, Juergen
Nanni, Mirco
Subrahmanian, VS
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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.
(Also cross-referenced as UMIACS-TR-99-50)