Using stochastic-dominance relationships for bounding travel times in stochastic networks
C-L Liu and MP Wellman
International Conference on Intelligent Transportation Systems, pages 55–60, Tokyo, Oct 1999.
Abstract
We consider stochastic networks in which link travel times are dependent, discrete random variables. We present…
A trading agent competition for the research community
MP Wellman and PR Wurman
IJCAI-99 Workshop on Agent-Mediated Electronic Commerce, 1999.
Abstract
We discuss the design of a trading agent competition to be held in conjunction with ICMAS-00. This design will be revised based on deliberations…
Graphical representations of consensus belief
Graphical models based on conditional independence support concise encodings of the subjective belief of a single agent. A natural question is whether the consensus belief of a group of agents can be represented with equal parsimony. We prove, under relatively mild assumptions, that even if everyone agrees on a common graph topology, no method of combining beliefs can maintain that structure. Even weaker conditions rule out local aggregation within conditional probability tables. On a more positive note, we show that if probabilities are combined with the logarithmic opinion pool (LogOP), then commonly held Markov independencies are maintained. This suggests a straightforward procedure for constructing a consensus Markov network. We describe an algorithm for computing the LogOP with time complexity comparable to that of exact Bayesian inference.