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We address long-term coalitions that are formed of both customer and vendor agents. We present a coalition formation mechanism designed at the agent level as a decision problem. The proposed mechanism is analyzed at both system and agent levels. Our results show that the coalition formation mechanism is beneficial for both the system – it reaches an… (More)

We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, starting from a policy which makes no use of the available information. The incremental process constructs policies which includes more of the information available to the decision… (More)

In this paper we present a framework for dynamically constructing Bayesian networks. We introduce the notion of a background knowledge base of schemata, which is a collection of parameterized conditional probability statements. These schemata explicitly separate the general knowledge of properties an individual may have from the specific knowledge of… (More)

We outline a method to estimate the value of computation for a flexible algorithm using empirical data. To determine a reasonable trade-off between cost and value, we build an empirical model of the value obtained through computation , and apply this model to estimate the value of computation for quite different problems. In particular , we investigate this… (More)

We report on work towards flexible algorithms for solving decision problems represented as influence diagrams. An algorithm is given to construct a tree structure for each decision node in an influence diagram. Each tree represents a decision function and is constructed incremen-tally. The improvements to the tree converge to the optimal decision function… (More)

Programmers employing inference in Bayesian networks typically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inference engines require non-trivial amounts of space and are also difficult to implement. This limits their use in some applications that would otherwise benefit from probabilistic… (More)

We document a connection between constraint reasoning and probabilistic reasoning. We present an algorithm, called probabilistic arc consistency, which is both a generalization of a well known algorithm for arc consistency used in constraint reasoning, and a specialization of the belief updating algorithm for singly-connected networks. Our algorithm is… (More)

In this paper, we present subcaching, a method for reducing the size of the caches in the recursive decomposition while maintaining the runtime of recursive conditioning with complete caching. We also demonstrate a heuristic for constructing recursive decompositions that improves the effects of subcaching, and show empirically that the savings in space is… (More)

A conditioning graph is a form of recursive factorization which minimizes the memory requirements and simplifies the implementation of inference in Bayesian networks. The time complexity for inference in conditioning graphs has been shown to be O(n exp(d)), where d is the depth of the underlying elimination tree. We demonstrate in this paper techniques for… (More)