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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)

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

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)

- 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)

- 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)

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
- 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 incremen-tally. The improvements to the tree converge to the optimal decision function… (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)

- Michael C. Horsch, William S. Havens, Aditya K. Ghose
- Canadian Conference on AI
- 2002

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

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)