Required Readings for Lectures
The following list contains readings assigned up to the most recent lecture, plus a tentative reading list for future lectures. Note that future readings are only suggestive, and may change up to the time of the lecture.
Note: AIMA is short for the course text, “Artificial Intelligence: A Modern Approach,” by Stuart Russell and Peter Norvig.
W Sept 5: Introduction to Autonomous Explorers
M Sep 10: Decision Theoretic Planning and Markov Decision Processes
AIMA Chapter 17, Sections 1 – 4.
Optional Advanced: Planning and Acting
in Partially Observable Stochastic Domains,
L. Kaebling, M. Littman and A. Cassandra, Elsevier (1998) 237-285.
W Sep 12: Reinforcement Learning
AIMA Chapter 20
Learning: a Survey,
L. Kaebling, M. Littman and A. Moore, Journal of Artificial Intelligence Research 4 (1996) 237-285.
M Sep 17: Student Holiday – No Class
W Sep 19: Model-based Agents
Remote Agent: to Boldy Go Where No AI
System Has Gone Before,
N.Muscettola, P. Nayak, B. Pell and B. Williams, Artificial Intelligence 103 (1998) 5-47.
Path Planning Using Lazy PRM,
R. Bohlin and L. Kavraki, ICRA 2000.
M Sep 24: Partial Order Planning
AIMA Chapter 11, (review unification if necessary Chapter 10, Section 2)
Suggested: An Introduction to Least Commitment Planning,
Daniel S. Weld, AI Magazine (1994) Summer/Fall.
W Sep 26: Planning for Advanced Student Lectures
No reading assignment
M Oct 1: Plan Execution and Conditional Planning
AIMA Chapter 13
AIMA Chapter 6.
W Oct 3: Temporal Planning
Bridging the Gap Between
Planning and Scheduling,
D. Smith, J. Frank and A. Jonsson, Knowledge Engineering Review, 15(1), 2000.
Planning in Interplanetary Space:
Theory and Practice,
A. Jonsson, P. Morris, N. Muscettola and K. Rajan. Proc. 5th Int. Conf. on AI Planning & Scheduling.
M Oct 8: Columbus Day – No Class
W Oct 10 Propositional Satisfiability and Model-based diagnosis
AIMA, Chapter 6.
M Oct 15: Propositional Inference: Systematic and Local Search
Generating hard satisfiability problems.
B. Selman, D. Mitchell and H. Levesque, Artificial Intelligence 81, 17-29, 1996.
Hard Instances of the Satisfiability Problem: A Survey
S. A. Cook and D. Mitchell, DIMACS Series in Discrete Mathematics and
Theoretical Computer Science, 1997.
W Oct 17: Model-based Diagnosis
R. Davis and W. C. Hamscher, in H. E. Shrobe (Ed.) Exploring Artificial Intelligence: Survey Talks from the National Conferences on Artificial Intelligence, 297-346, Morgan Kaufmann, San Mateo, CA, 1988.
Diagnosing multiple faults
J. de Kleer and B. Williams, Artificial Intelligence 32 (1): 97-130, April 1987.
Characterizing Diagnoses and Systems
Johan de Kleer, Alan K. Mackworth and Raymond Reiter, Artificial Intelligence 56 (1992).
M Oct 22: Multi-Agent Planning and Resource Allocation
Planning for Location Discovery,
presented by Goutam Reddy.
Cooperation for Allocating Resources in a Dynamic Environment,
presented by Emily Craparo, Josh Mc Connell and Erica Peterson.
Lynne E. Parker, ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation, IEEE Transactions on Robotics and Automation, 14 (2), 1998.
Lynne E. Parker, ALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots, Proceedings of the 1994 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS '94), September 1994
W Oct 24: Advanced Bayesian Inference
Inference for Identifying Hard Computational Problems,
presented by Paul Elliott and Chris Osborn.
Horvitz et al., A Bayesian Approach to Tackling Hard Computational Problems.
Parameteric and Non-Parameteric
Representations of System Belief States in Dynamic Bayesian Networks,
presented by Brenda Ng and John Bevilacqua.
M Oct 29: Multi-Agent Learning and Communication
presented by Jose Esparza and Brian Whitman.
presented by Nick Homer and Paola Nasser.
W Oct 31: Path Planning and Bayesian Inference
presented by Stano Funiak, Nathan Ickes and Aisha Walcott
Inference and First Order Logic
presented by Adam Glassman and Raj Krishnan.
M Nov 5: Bayesian Inference
Context Switching in Real-time Propositional Reasoning.
P. Pandurang Nayak and Brian C. Williams. In proceedings of AAAI-97.
W Nov 7: Graph-based Planning: GraphPlan and SatPlan
M Nov 12: Veterans Day – No Class
W Nov 14: Rod Brooks – Behavior-based and Humanoid Robots
M Nov 19: Trevor Darrell – Active Vision for Intelligent Spaces
W Nov 21: Daniel Jackson – Reasoning about Software Bugs with NitPick
M Nov 26: Monitoring Dynamical Systems: Combining Hidden Markov Models and Logic
A Model-based Approach to Reactive Self-Configuring
Brian C. Williams and P. Pandurang Nayak. In Proceedings of AAAI-96.
W Nov 28: Model-based Reactive Planning
Recent Advances in AI planning,
Daniel Weld. AI Magazine, 1999.
A Reactive Planner for a Model-based Executive.
Brian C. Williams and P. Pandurang Nayak. In Proceedings of IJCAI-97.
W Dec 3, 5, 10, 12: Final Project Presentations