Michael C. Horsch

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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 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)
We propose a fast algorithm for on-line path search in grid-like undirected planar graphs with real edge costs (aka terrains). Our algorithm depends on an off-line analysis of the graph, requiring poly-logarithmic time and space. The off-line preprocessing constructs a hierarchical representation which allows detection of features specific to the terrain.(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)
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)
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)