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Part A: Robots that Plan and Act in the World
1 Introduction to Cognitive Robots

Remote Explorers and Human Interation Systems
Muscettola, N., P. Nayak, B. Pell, and B. Williams. "Remote Agent: To Boldly Go Where No AI System Has Gone Before." Artificial Intelligence 103 (1998): 5-47.

Pineau, J., M. Montemerlo, M. Pollack, N. Roy, and S. Thrun. "Towards Robotic Assistants in Nursing Homes: Challenges and Results." Robotics and Autonomous Systems 42 (2003): 271-281.
A1: Robots that Deftly Navigate
2 Planning Routes by Generating Maps

Configuration Spaces, Visibility Graphs, Voronai Diagrams, Potential Fields, and Cell Decomposition
Bohlin, R., and L. Kavraki. "Path Planing Using Lazy PRM." ICRA (2000).

Latombe, Jean-Claude. Robot Motion Planning. Boston, MA: Kluwer Academic Publishers, 1991. ISBN: 079239206X.

Rimon, E., and D. E. Koditschek. "Exact Robot Navigation Using Artificial Potential Functions." IEEE Transactions on Robotics and Automation 8, no. 5 (October 1992): 501518.

Thrun, S. "Learning Metric-topological Maps for Indoor Mobile Robot Navigation." Artificial Intelligence 99, no. 1 (1998): 21-71.
3 Randomized Path Planning

Kino-dynamic Planning, Planning with Moving Obstacles, Probabilistic Roadmaps (PRMs), Rapidly Exploring Random Trees (RRTs)
Hsu, D., R. Kindel, J.C. Latombe, and S. Rock. "Randomized Kinodynamic Motion Planning with Moving Obstacles." Fourth International Workshop on Algorithmic Foundations of Robotics, 2000.

LaValle, Steven M. "Rapidly-Exploring Random Trees: A New Tool for Path Planning." Technical Report No. 98-11, Dept. of Computer Science, Iowa State University, October 1998.
A2: Planning and Executing Complex Missions
4 Path Planning in Unknown Environments: An Overview Stentz, A. "Optimal and Efficient Path Planning for Partially-Known Environments." Proceedings IEEE International Conference on Robotics and Automation (May 1994).
5 Incremental Path Planning

Single Source Shortest Path, D*, LRTA*
Koenig, S., and M. Likhachev. "Incremental A*." In Advances in Neural Information Processing Systems (NIPS). Vol. 14. Cambridge, MA: MIT Press, 2002. ISBN: 0262042088.
6 Mission-level Task Planning

Partial Order Planning, Constraint-based Interval Planning, and Simple Temporal Networks (STNs)
Smith, David E., Jeremy Frank, and Ari Jonsson. "Bridging the Gap between Planning and Scheduling." Knowledge Engineering Review 15, no. 1 (2000).

Kim, P., B. Williams, and M. Abramson. "Executing Reactive, Model-based Programs through Graph-based Temporal Planning." IJCAI (2001): 487-493.
Part B: Robots that are State-Aware
7 Foundations of Estimation

Bayes Filters, Kalman Filters, and HMMs
Welch, G., and G. Bishop. "An Introduction to the Kalman Filter." University of North Carolina at Chapel Hill, Dept. of Computer Science, TR 95-041.

Leonard, J. J., and H. F. Durrant-Whyte. "Mobile Robot Localization by Tracking Geometric Beacons." IEEE Trans Robotics and Automation 7, no. 3 (June 1991): 376-382.

Rabiner, L. R. "A Tutorial on Hidden Markov Models." Proceedings of the IEEE 77 (1989): 257-286.
B1: Robots that Find Their Way in the World
8 Determining Location Through Particle Filters

MCMC Methods, Rejection Sampling, Importance Sampling, Metropolis, Particle Filters for Localization
Rabiner, L. R. "A Tutorial on Hidden Markov Models." Proceedings of the IEEE 77 (1989): 257-286.

Doucet, A., N. de Freitas, N. Gordon, eds.Sequential Monte Carlo Methods in Practice. New York, NY: Springer-Verlag, 2001. ISBN: 0387951466.

Neal, Radford M. Probabilistic Inference Using Markov Chain Monte Carlo Methods. University of Toronto CS Tech Report, 1993.

Thrun, S., D. Fox, W. Burgard, and F. Dellaert. "Robust Monte Carlo Localization for Mobile Robots." Artificial Intelligence 128 (2001): 1-2 and 99-141.
9 Learning Maps

Scan-matching, ICP, SLAM using Kalman Filters, Topological maps, Fast-Slam
Leonard, J. J., I. J. Cox, and H. F. Durrant-Whyte. "Dynamic Map Building for an Autonomous Mobile Robot." Int. J. Robotics Research 11, no. 4 (August 1992): 286-298.

Kuipers, B. J., and Y. T. Byun. "A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations." Journal of Robotics and Autonomous Systems 8 (1991): 47-63.
B2: Robots that Deduce and Control Their Internal State
10 Model-based Programming and Model-based Diagnosis

Model-based Diagnosis
Davis, R. and W. C. Hamscher. "Model-based Reasoning: Troubleshooting." InExploring Artificial Intelligence: Survey Talks from the National Conferences on Artificial Intelligence. Edited by Shrobe, H. E. San Mateo, CA: Morgan Kaufmann, 1988, pp. 297-346. ISBN: 0934613699.

de Kleer, J., and B. Williams. "Diagnosing Multiple Faults." Artificial Intelligence 32, no. 1 (April 1987): 97-130.
11 Conflict-directed Diagnosis & Probabilistic Mode Estimation

Consistency-based Diagnosis
de Kleer, Johan, Alan K. Mackworth, and Raymond Reiter. "Characterizing Diagnoses and Systems." Artificial Intelligence 56 (1992).

de Kleer, Johan, and Brian C. Williams. "Diagnosis with Behavioral Modes." In Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, MI, 1989, pp. 1324-1330. Also in Hamscher et al., Eds. Readings in Model-based Diagnosis. Morgan Kaufman, 1992, pp. 124-130.
12 Incremental Mode Estimation and Hybrid Systems

Incremental Logical Inference, Trajectory Tracking for Constraint-based, Gaussian Filtering for Hybrid HMMs (K-Best and Rao-Blackwell Particle Filtering)
Williams, Brian C., Michel Ingham, Seung H. Chung, and Paul H. Elliott. "Model-based Programming of Intelligent Embedded Systems and Robotic Space Explorers." Invited paper in Proceedings of the IEEE: Special Issue on Modeling and Design of Embedded Software 9, no. 1 (January 2003): 212-237.

Kurien, J., and P. Nayak. "Back to the Future for Consistency Based Trajectory Tracking." Proceedings from the 17th National Conference on Artificial Intelligence, Austin, TX, August 2000. pp. 370377.

Hofbaur, M. W., and B. C. Williams. "Mode Estimation of Probabilistic Hybrid Systems." In Hybrid Systems: Computation and Control (2002).

Funiak, S., and B. C. Williams. "Multi-modal Particle Filtering for Hybrid Systems with Autonomous Mode Transitions." In: DX-2003, SafeProcess 2003.
13 Optimal CSPs and Conflict-directed A*

Constraint Satisfaction Problems and Conflict-directed A* Search
Williams, Brian C., and Robert Ragno. "Conflict-directed A* and its Role in Model-based Embedded Systems." To appear in the Special Issue on Theory and Applications of Satisfiability Testing, Journal of Discrete Applied Math.
14 Context-based Vision

Bill Freeman Guest lecture
Murphy, K., A. Torralba, and W. Freeman. "Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes." NIPS (2003).
Fast Planning
15 Planning as Heuristic Forward Search

FF Planning

Student Advanced Lectures

LPG: Local Search for Planning Graphs (Seung Chung)
Hoffmann, Jorg, and Bernhard Nebel. "The FF Planning System: Fast Plan Generation Through Heuristic Search." Journal of Artificial Intelligence Research (2001).

Gerevini, A., A. Saetti, and I. Serina. "Planning through Stochastic Local Search and Temporal Action Graphs." To appear in Journal of Artificial Intelligence Research (JAIR).

Bonet, Blai, and Hector Geffner. "Planning as Heuristic Search." Artificial Intelligence Journal (2001).
16 Student Advanced Lectures (cont.)

Fast Solutions to Constraint Satisfaction Problems (Robert Effinger & Dan Lovell)
Ginsberg, Matthew L. "Dynamic Backtracking." Journal of Artificial Intelligence Research 1 (1993): 25-46.

Prosser, P. "Hybrid Algorithms for the Constraint Satisfaction Problem." Computational Intelligence 9 (1993): 268-299.
Cooperative Planning
17 Student Advanced Lectures (cont.)

Distributed CSPs and Task Assignment (Thomas Leaute & Justin Werfel)
Yokoo, M., E. Durfee, T. Ishida, and K. Kuwabara. "The Distributed Constraint Satisfaction Problem: Formalization and Algorithms." IEEE Transactions on Knowledge and Data Engineering 10, no. 5 (September/October 1998).

Zlot, R., A. Stentz, M. Dias, and S. Thayer. "Multi-Robot Exploration Controlled by a Market Economy." IEEE International Conference on Robotics and Automation (May 2002).
18 Student Advanced Lectures (cont.)

Distributed Reinforcement Learning and MDPs (Lars Blackmore & Steve Block)
Tan. M. "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents." Proceedings of the Tenth International Conference on Machine Learning (1993): 330-337. Amherst, MA.

Ahmadabadi, M., and M. Asadpour. "Expertness Based Cooperative Q-Learning." IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 32, no. 1 (February 2002).

Eshgh, S., and M. Ahmadabadi. "An Extension of Weighted Strategy Sharing in Cooperative Q-Learning for Specialized Agents." Proceedings of the 9th International Conference on Neural Information Processing (ICONIP02) 1 (2002).
Vision-based Exploration
19 Student Advanced Lectures (cont.)

Vision-based SLAM (Soren Riisgaard)
Se, S., D. Lowe, and J. Little. "Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks." The International Journal of Robotics Research 21, no. 8 (August, 2002): 735-758.
20 Student Advanced Lectures (cont.)

Information Based Adaptive Robotic Exploration (Morten Rufus Blas) & Uncertainty and Visual Exploration Alexander Omelchenko)
Bourgault, F., A. Makarenko, S. B. Williams, B. Grocholsky, and H. F. Durrant-Whyte. "Information Based Adaptive Robotic Exploration." IEEE/RSJ Intl. Workshop on Intelligent Robots and Systems (2002).

Whaite, P., and F. P. Ferrie. "From Uncertainty to Visual Exploration." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 10 (October 1991).
Part C: Robots that Preplan for an Uncertain Future
21 Reactive Planning in Large State Spaces Through Decomposition and Serialization

Student Advanced Lectures (cont.)

SIFT SLAM Vision Details (Vikash Mansinghka)
Se, S., D. Lowe, and J. Little. "Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks." The International Journal of Robotics Research 21, no. 8.

Williams, Brian C., and P. Pandurang Nayak. "A Reactive Planner for a Model-based Executive." In Proceedings of the International Joint Conference on Artificial Intelligence (1997).

Chung, S., and B. C. Williams. "A Factored Symbolic Approach to Reactive Planning." In International Conference on Self-Adaptive Software, Washington DC (June 2003).
22 The Linear Programming Approach to Approximate Dynamic Programming (Guest Lecturer: Daniela Pucci de Farias)

Markov Decision Processes, Approximate Dynamic Programming and Linear Programming, Performance and Error Analysis, and Constraint Sampling
23 Partially Observable Markov Decision Processes

POMDPs, Policy Trees and Value Iteration
Kaelbling, L., M. Littman, and A. Cassandra. "Planning and Acting in Partially Observable Stochastic Domains." Artificial Intelligence 101 (1998).

Pineau, J., G. Gordon, and S. Thrun. "Point-based value iteration: An anytime algorithm for POMDPs." In International Joint Conference on Artificial Intelligence (IJCAI). Acapulco, Mexico. August 2003.
24 Approximate Solutions to POMDPs

Heuristics, Coastal Navigation, and Real World Apps
Cassandra, Anthony R. "Exact and Approximate Algorithms for Partially Observable Markov Decision Processes." Chapter 6, Ph.D. Thesis. Brown University, Department of Computer Science, Providence, RI, 1998.
25 Dynamic Scheduling and Execution

Temporal Plan Execution, Dynamic Scheduling, and Simple Temporal Networks
Dechter, R., I. Meiri, and J. Pearl. "Temporal Constraint Networks." Artificial Intelligence 49 (May 1991): 61-95.

Muscettola, N., P. Morris, and I. Tsamardinos. "Reformulating Temporal Plans for Efficient Execution." In Proc. Of Sixth Int. Conf. on Principles of Knowledge Representation and Reasoning (KR 98), 1998.
26 Project Demonstrations

10 minute Student Presentations