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IE 704: Selected Topics in A.I. for Operations Research

Prerequisite:  Instructor's permission


Overview of some problem types encountered in O.R. where A.I. approaches have proved useful: combinatorial optimization and Planning, clustering & pattern recognition, prediction and control of stochastic systems.

Constraint Satisfaction Problems; backtracking search, local search, intelligent backtracking. Strategies and algorithms for automated planning. Constraint directed scheduling.

Bayesian networks; simulation based algorithms for inference. Dynamic Bayesian networks; filtering and prediction; smoothing. Hidden Markov models. Kalman filters. Approximate inference in Dynamic Bayesian Networks; Particle Filtering. Learning Bayes Net structures and parameters. Learning with Hidden Variables. EM algorithm.

Feedforward neural networks and Radial Basis Function networks for function approximation.

Fuzzy systems for mapping; fuzzy associative memory and applications in control. Adaptive FAM and Neuro-fuzzy systems.

Learning Vector Quantization, Support Vector Machines, Self Organizing Feature Maps for pattern classification & recognition. Adaptive Resonance Theory networks.

Hopfield Network applications in combinatorial optimization. Simulated Annealing. Boltzmann Machine; applications in combinatorial optimization and pattern recognition.

Use of neural function approximations in neuro-dynamic programming and Reinforcement Learning of control policies.



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