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.
@cite_4 aim to construct a principled framework, called , for buyers to choose the best seller based on some measure of reputation in a market consisting of autonomous agents: A selection of approaches to representing reputation using Dempster-Shafter Theory and Bayesian probability are surveyed and a model for collecting and using reputation is developed using a Partially Observable Markov Decision Process'' . It is noted that Advisor-POMDP models a single buyer agent, maintaining a vector of reputations of sellers. The buyer may ask for advice from advisors about a seller, who respond with (i.e., the buyer perceives) @math where @math is the reputation and the certainty factor @math is a measure of the epistemic uncertainty of seller @math . RepNets do not do not utilize such confidence levels.
@cite_4
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This paper examines approaches to representing uncertainty in reputation systems for electronic markets with the aim of constructing a decision theoretic framework for collecting information about selling agents and making purchase decisions in the context of a social reputation system. A selection of approaches to representing reputation using Dempster-Shafter Theory and Bayesian probability are surveyed and a model for collecting and using reputation is developed using a Partially Observable Markov Decision Process.