Intro to AI

Description

Schedule and Readings

Module 1. AI and agents.

Lectures:

• Sep 2. Introduction. Lecture Notes
• Sep 7. Agents. Lecture Notes
• Sep 9. Symbols. Lecture Notes
• Sep 14. Reactive agents, memory and state. Guest lecture by Ken Shan. Demos and discussion of ant game agents here.
• Sep 16. Interacting agents and game applications. Lecture Notes. Guest lecture by Andy Nealen.
• Sep 21. Strategy, teamwork and communication. Lecture Notes.

Readings:

• Russell and Norvig. Artificial Intelligence: A Modern Approach. 3rd Edition. Chapter 2, pages 34-61. Scanned copy
• Bourg and Seemann. AI for Game Developers. Chapter 2, Chasing and Evading, pages 6-26. Scanned copy
• Bourg and Seemann. AI for Game Developers. Chapter 4, Flocking, pages 52-79. Scanned copy
• Braitenberg. Vehicles: Experiments in Synthetic Psychology. MIT 1984. Chapters 1-5, pages 1-25. Scanned copy
• Reynolds. Flocks, Herds, and Schools: A Distributed Behavioral Model. Proceedings of SIGGRAPH 1987. Electronic copy

Project:

• Project 1 Complete description of Predators, Grazers, and Flockers components
• Skeleton code as tarball with all files together or as directory with individual java files
• Example specifications as tarball with all files together or as directory with individual XML files

Module 2. Problem solving and search.

Lectures:

• Sep 23. Search: Problem spaces Lecture Notes.
• Sep 28. Search: Combinatorics and methodology Lecture Notes
• Sep 30. Search: Simple strategies Lecture Notes.
• Oct 5. Search and representation Lecture Notes.
• Oct 7. Best-first and A* search Lecture Notes.
• Oct 12. Search as simulation Lecture Notes.

Readings:

• Russell and Norvig. Artificial Intelligence: A Modern Approach. 3rd Edition. Chapter 3, pages 64-112. Scanned copy
• Russell and Norvig. Artificial Intelligence: A Modern Approach. 3rd Edition. From Chapter 4, pages 120-129. Scanned copy
• Bourg and Seeman. AI for Game Developers. Chapter 6, A* Pathfinding, pages 126-148. Scanned copy

Demos:

• A* pathfinding search. Interactive demo

Project:

• Project 2 Complete description of search assignment
• Skeleton code as tarball with all files together or as directory with individual java files

Module 3. Perception and learning.

Lectures:

• Oct 14. Perception and categorization Lecture Notes.
• Oct 19. Probabilistic categorization Lecture Notes
• Oct 21. Naive Bayes Lecture Notes
• Oct 26. Spam Filtering and Review Lecture Notes
• Oct 28. Midterm
• Nov 2. Clustering and unsupervised learning Lecture Notes
• Nov 4. Learning quantitative relationships Lecture Notes
• Nov 9. Similarity, recommendation Lecture Notes
• Nov 11. Perception and representation, vision Lecture Notes

Readings:

• Segaran. Programming Collective Intelligence. Chapter 6: Document Filtering, pages 117-141. Scanned Copy
• Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. From Chapter 13: Quantifying Uncertainty, pages 480-499. Scanned Copy
• Segaran. Programming Collective Intelligence. Chapter 2: Making Recommendations, pages 7-28. Scanned Copy

Supplementary material:

• Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. Chapter 18: Learning from Examples, pages 683-758. Scanned Copy

Demos:

• K-means clustering
• Probabilistic clustering

Project

• description (text)
• skeleton code
• data

Module 4. Intelligent action.

Lectures:

• Nov 16. Utility and uncertain decisions Lecture Notes
• Nov 18. Decisions over time Lecture Notes
• Nov 22. Dynamic programming Lecture Notes
• Nov 30. Game-playing. Guest lecture by Michael Wunder. Lecture Notes
• Dec 2. Reinforcement learning Lecture Notes

Readings:

• Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. Chapter 16: Making Simple Decisions, pages 610-626. Scanned Copy
• Stone. Agents in the Real World. Chapter written for Rutgers Collection, "What is Cognitive Science", 2001. PDF

Demos:

• Blackjack player with reinforcement learning. Applet
• Demo of dynamic programming for uncertain path planning. Applet

Module 5. AI in context.

Lectures:

• Dec 7. AI and cognitive science Lecture Notes
• Dec 9. AI as culture Lecture Notes

Readings:

• Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. Chapter 26: Philosophical Foundations, pages 1020-1040. Scanned Copy

Supplementary material:

• William Gibson, Neuromancer, Ace Books. 1984. Text

Further information:

• Daniela Rus's robot pebbles Project page.
• IBM's Watson Jeopardy computer Project page and New York Times applet
• Stanley robot winner of the DARPA grand challenge. Project page and Good interview with project member
• ICT's virtual humans. Project page.

• Dec 23, 12-3pm. Final exam.