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_3 develop an (uncertain) evidential model of reputation management based on the Dempster-Shafer theory. If an agent @math has had sufficient interactions with agent @math , @math bases its opinions on the quality of services received during those interactions. If @math has had insufficient interactions with @math , it will seek referrals from (trusted) neighbours, who may be witnesses of interaction with @math . If the neighbours are not witnesses, they will request witnesses from their neighbours, and so on. A limitation of this approach is that it models only the uncertainty in the services received and in the trustworthiness of neighbours who provide referrals. It does not model dynamical systems, nor does it allow for stochastic actions and observations.
@cite_3
12936406 1922376695 2068143054 1993853885
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. This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems in particular. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. We consider a new problem in multiterminal source coding motivated by the following decentralized communication estimation task. A firm's Chief Executive Officer (CEO) is interested in the data sequence X(t) sub t=1 sup spl infin which cannot be observed directly, perhaps because it represents tactical decisions by a competing firm. The CEO deploys a team of L agents who observe independently corrupted versions of X(t) sub t=1 sup spl infin . Because X(t) is only one among many pressing matters to which the CEO must attend, the combined data rate at which the agents may communicate information about their observations to the CEO is limited to, say, R bits per second. If the agents were permitted to confer and pool their data, then in the limit as L spl rarr spl infin they usually would be able to smooth out their independent observation noises entirely. Then they could use their R bits per second to provide the CEO with a representation of X(t) with fidelity D(R), where D( spl middot ) is the distortion-rate function of X(t) . In particular, with such data pooling D can be made arbitrarily small if R exceeds the entropy rate H of X(t) . Suppose, however, that the agents are not permitted to convene, Agent i having to send data based solely on his own noisy observations Y sub i (t) . We show that then there does not exist a finite value of R for which even infinitely many agents can make D arbitrarily small. Furthermore, in this isolated-agents case we determine the asymptotic behavior of the minimal error frequency in the limit as L and then R tend to infinity. Abstract We present a measure of performance (MOP) for identification algorithms based on the evidential theory of Dempster–Shafer. As an MOP, we introduce a principled distance between two basic probability assignments (BPAs) (or two bodies of evidence) based on a quantification of the similarity between sets. We give a geometrical interpretation of BPA and show that the proposed distance satisfies all the requirements for a metric. We also show the link with the quantification of Dempster's weight of conflict proposed by George and Pal. We compare this MOP to that described by Fixsen and Mahler and illustrate the behaviors of the two MOPs with numerical examples.