MIND Lab. Seminars: Reasoning and Planning Under Uncertainty
Logistics:
- Location: MindLab 8400 Baltimore Avenue, Ste. 200
- Time: Fridays, 2pm.
List of Topics (Tentative):
- Probabilistic Reasoning
- Probability Theory: A Quick Overview
- Bayesian Inference
- Two Representations for Probabilistic Uncertainty: Markov and Bayesian Networks
- Exact Inference in Probabilistic Networks
- Kim and Pearl's Message Passing Algorithm
- Clustering Methods
- Junction Trees
- Approximate Inference in Probabilistic Networks
- Logic Sampling
- Likelihood Weighting
- The Markov-Chain Monte-Carlo (MCMC) Method
- Decision and Control
- Basics of Utility Theory
- Decision Networks and Influence Diagrams
- Action Selection in Decision Networks
- Automated Planning under Uncertainty
- Planning in Artificial Intelligence, Traditionally
- Sources of Uncertainty: Nondeterminism, Partial-Observability, and Temporally-Extended Goals
- Two Uncertainty Models: Markov Decision Processes and Nondeterministic State-Transition Systems
- Planning with Markov Decision Processes
- Value- and Policy-Iteration Algorithms
- Real-Time Dynamic Programming
- Planning as Model Checking
References Might Be Of Interest (more will be added later):
Books
- J. Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers.
- M. Ghallab, D. Nau, and P. Traverso. 2004. Automated Planning: Theory and Practice. Morgan Kaufmann Publishers.
- S. J. Russell and P. Norvig. 2003. Aritifical Intelligence: A Modern Approach. Prentice Hall, Pearson Inc.
Papers
- E. Charniak, 1991.
Bayesian Networks without Tears.
AI magazine. [pdf]
- N. L. Zhang and D. Poole. 1994.
A Simple Approach to Bayesian Network Computations.
In Proc. of the Tenth Canadian Conference on Artificial Intelligence. pages 171--178.
- M. Henrion. 1988.
Propagation of Uncertainty in Bayesian Networks by Probabilistic Logic Sampling.
In J. F. Lemmer and L. N. Kanal, editors, Uncertainty in Artificial Intelligence 2, pages 149--163.
- Craig Boutilier, Thomas L. Dean, and S. Hanks. 1999.
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage.
JAIR, 11:1--94.
- B. Bonet and H. Geffner.2001.
Planning and Control in Artificial Intelligence: A Unifying Perspective.
Applied Intelligence, 14(3):237--252.
- B. Bonet and H. Geffner. 2003.
Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming.
In E. Giunchiglia, N. Muscettola, and D. Nau, editors,
ICAPS-03, pages 12--21.
- A. Cimatti, M. Pistore, M. Roveri, and P. Traverso. 2003.
Weak, Strong, and Strong-Cyclic Planning via Symbolic Model Checking.
Artificial Intelligence, 147(1-2):35--84.
Related AI Journals and Conference Proceedings
- Artificial Intelligence Journal (AIJ)
- Journal of Artificial Intelligence Research (JAIR)
- International Joint Conference on Artificial Intelligence (IJCAI)
- National Conference on Artificial Intelligence (AAAI)
- International Conference on Uncertainty in Artificial Intelligence (UAI)
- International Conference on Automated Planning and Scheduling (ICAPS)
Seminar Notes, Slides, and Discussions