Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous wo...

In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitrary networks (the so called, iterative or loopy belief propagation (IBP)) it is identical to an arc-consistency algorithm relative to zero-belief queries (namely assessing zero posterior probabilities). This implies th...

The abstraction of probability inference is a process of searching for a representation in which only the desirable properties of the solutions are preserved. Simplification is one of such abstraction which reduces the size of large databases and speeds transmission and processing of probabilistic information[Sy & Sher, 941. Given a set of evidence S,, human beings are often interested in findi...

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of unfusion is the opposite of fusion,...

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of fission is the opposite of fusion, ...

We consider a multi-agent system where each agent is equipped with a Bayesian network, and present an open framework for the agents to agree on a possible consensus network. The framework builds on formal argumentation, and unlike previous solutions on graphical consensus belief, it is sufficiently general to allow for a wide range of possible agreements to be identified.

We consider a multi-agent system where each agent is equipped with a Bayesian network, and present an open framework for the agents to compromise on a possible consensus network. The framework builds on formal argumentation, and unlike previous solutions on graphical consensus belief, it is sufficiently general to allow for a wide range of compromises to be identified.

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We primarily summarize [4]. When we think that it is appropriate, we comment on additional facts and more recent developments. 1 Abstract and Introduction The advantages of graphical modelling include • easy handling of missing data • easy modelling of causal relationships • easy combination of prior information and data • easy to avoid overfitting 2 Bayesian Approach • Degree of belief • Rules...

This paper presents the development of a Bayes net classifier for prediction of a victimization attribute value for the National Crime Victimization Survey dataset. The National Crime Victimization Survey dataset has over 250 attributes and 216,000 data points, and as such poses a large-scale problem context for classifier development. The classifier was developed using the Weka machine learnin...