Required Readings for Lectures
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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
No
Reading
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
Reinforcement
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)
Optional
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.
Finding
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
Model-based
Reasoning: Troubleshooting
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
Multi-agent
Planning for Location Discovery,
presented by Goutam Reddy.
Cancelled.
Group
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
Bayesian
Inference for Identifying Hard Computational Problems,
presented by Paul Elliott and Chris Osborn.
Tutorial
TBA
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.
tutorial 1, tutorial
2 and tutorial 3.
Readings
TBA
M
Oct 29: Multi-Agent Learning and Communication
Multi-Agent
Reinforcement Learning,
presented by Jose Esparza and Brian Whitman.
Tutorial
TBA
Readings TBA
Multi-Agent
Adaptive Communication,
presented by Nick Homer and Paola Nasser.
Tutorial
TBA
Readings
TBA
W
Oct 31: Path Planning and Bayesian Inference
Kino-dynamic
Path Planning,
presented by Stano Funiak, Nathan Ickes and Aisha Walcott
Tutorial
TBA
Readings TBA
Bayesian
Inference and First Order Logic
presented by Adam Glassman and Raj Krishnan.
Tutorial
TBA
Readings TBA
M
Nov 5: Bayesian Inference
Paper
TBA
Fast
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
Reading
TBA.
M
Nov 19: Trevor Darrell – Active Vision for Intelligent Spaces
Reading
TBA.
W
Nov 21: Daniel Jackson – Reasoning about Software Bugs with NitPick
Reading
TBA.
M
Nov 26: Monitoring Dynamical Systems: Combining Hidden Markov Models and Logic
A Model-based Approach to Reactive Self-Configuring
Systems.
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.
Reading TBA.
W
Dec 3, 5, 10, 12: Final Project Presentations
Readings
TBA.
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