2011
NeurIPS
NeurIPS 2011
Solving Decision Problems with Limited Information
Abstract
We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the usual assumptions of no forgetting and regularity, which allows us to solve problems with limited information. The algorithm, which implements a sophisticated variable elimination procedure, is empirically shown to outperform a state-of-the-art algorithm in randomly generated problems of up to 150 variables and $10^{64}$ strategies.
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Interdisciplinary Bridge
- Artificial Intelligence and Knowledge & Reasoning
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Trend Setter
- Bayesian Networks
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Hot Topic Early Bird
- probabilistic graphical model
Authors
Topics
Artificial Intelligence > Core AI > Planning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Knowledge & Reasoning > Reasoning > Automated Reasoning
Knowledge & Reasoning > Reasoning > Causal Inference
Machine Learning > Bayesian & Probabilistic > Bayesian Networks
Artificial Intelligence > Core AI > Decision Making
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Networks