DIRECTING GENETIC ALGORITHMS FOR PROBABILISTIC REASONING THROUGH REINFORCEMENT LEARNING

DOI10.1142/S0218488500000125zbMath1113.68543OpenAlexW2083840494MaRDI QIDQ3427875

Xiaomin Zhong, Eugene jun. Santos

Publication date: 27 March 2007

Published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218488500000125

zbMATH Keywords

belief revisiongenetic algorithmreinforcement learningprobabilistic reasoning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (2)

AN ARTIFICIAL NETWORK FOR REASONING IN THE CANCELLATION CLASS WITH APPLICATION TO THE DIAGNOSIS OF CELLS DIVISION ⋮ Partial abductive inference in Bayesian belief networks by simulated annealing

Cites Work

• Finding MAPs for belief networks is NP-hard
• Hybrid algorithms for approximate belief updating in Bayes nets
• The computational complexity of probabilistic inference using Bayesian belief networks

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