Sciweavers

AMAI
2004
Springer
13 years 10 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi
AMAI
2004
Springer
13 years 10 months ago
Generalized Opinion Pooling
In this paper we analyze the problem of opinion pooling. We introduce a divergence minimization framework to solve the problem of standard opinion pooling. Our results show that v...
Ashutosh Garg, T. S. Jayram, Shivakumar Vaithyanat...
AMAI
2004
Springer
13 years 10 months ago
Approximate Probabilistic Constraints and Risk-Sensitive Optimization Criteria in Markov Decision Processes
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Dmitri A. Dolgov, Edmund H. Durfee
AMAI
2004
Springer
13 years 10 months ago
Using the Central Limit Theorem for Belief Network Learning
Learning the parameters (conditional and marginal probabilities) from a data set is a common method of building a belief network. Consider the situation where we have known graph s...
Ian Davidson, Minoo Aminian
AMAI
2004
Springer
13 years 10 months ago
The Expressive Rate of Constraints
In reasoning tasks involving logical formulas, high expressiveness is desirable, although it often leads to high computational complexity. We study a simple measure of expressiven...
Hubie Chen
AMAI
2004
Springer
13 years 10 months ago
Production Inference, Nonmonotonicity and Abduction
We introduce a general formalism of production inference relations that posses both a standard monotonic semantics and a natural nonmonotonic semantics. The resulting nonmonotonic...
Alexander Bochman
AMAI
2004
Springer
13 years 10 months ago
Bayesian Model Averaging Across Model Spaces via Compact Encoding
Bayesian Model Averaging (BMA) is well known for improving predictive accuracy by averaging inferences over all models in the model space. However, Markov chain Monte Carlo (MCMC)...
Ke Yin, Ian Davidson