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- Julita Vassileva, Silvia Breban, Michael C. Horsch
- Computational Intelligence
- 2002

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)

- Michael C. Horsch, William S. Havens
- UAI
- 2000

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 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)

- Michael C. Horsch, David L. Poole
- UAI
- 1998

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)

- David Mould, Michael C. Horsch
- PRICAI
- 2004

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

- Michael C. Horsch, David L. Poole
- UAI
- 1996

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 incrementally. The improvements to the tree converge to the optimal decision function… (More)

- Kevin Grant, Michael C. Horsch
- Australian Conference on Artificial Intelligence
- 2005

Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions. The standard approach to probabilistic inference in Bayesian… (More)

- Kevin Grant, Michael C. Horsch
- FLAIRS Conference
- 2006

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)

- Michael C. Horsch, David L. Poole
- UAI
- 1999

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)