16.412 - 6.898 Intelligent Embedded Systems


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.


Group Cooperation for Allocating Resources in a Dynamic Environment,
presented by Emily Craparo, Josh Mc Connell and Erica Peterson.

 tutorial outline.

 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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