  Many multi-agent systems (MASs) are situated in stochastic environments. Some
such systems that are based on the partially observable Markov decision process
(POMDP) do not take the benevolence of other agents for granted. We propose a
new POMDP-based framework which is general enough for the specification of a
variety of stochastic MAS domains involving the impact of agents on each
other's reputations. A unique feature of this framework is that actions are
specified as either undirected (regular) or directed (towards a particular
agent), and a new directed transition function is provided for modeling the
effects of reputation in interactions. Assuming that an agent must maintain a
good enough reputation to survive in the network, a planning algorithm is
developed for an agent to select optimal actions in stochastic MASs.
Preliminary evaluation is provided via an example specification and by
determining the algorithm's complexity.

Artificial Intelligence;Multiagent Systems