CS343: Artificial Intelligence</a> -- Spring 2012: Resources Page

Resources for Artificial Intelligence (cs343)

Week 1: Introduction

  • The slides presented in class: Tuesday;
    Thursday; more from Thursday (modified from Dan Klein's at UC Berkeley)
  • Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents ( Alternative link).
    Stan Franklin and Art Graesser
    •

    Week 2: Search:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • Sven Koenig's site on LPA* and D* lite
  • Andrew Ng's A* search notes
  • A student found this A* tutorial useful.

    • Week 3: Beyond Classical Search:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's); still more from Tuesday (on GAs, from Tom Mitchell);
    Thursday; more from Thursday (modified from Dan Klein's)
  • Continuous state space learning on Aibos: walking; ball control
  • GA applications
  • Video games: the NERO project
  • From a former student: Genetic Mona Lisa (shown in practice)
  • From a former student: robot evolution
  • An article on Removing the Genetics from GAs (Baluja and Caruana, 1995).
  • Path search in continuous environments using RRT's.

    • Week 4: Adversarial Search

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • Some adversarial reasoning in the 2010 Super Bowl (a pdf version).
  • And some more from the 2012 Super Bowl (a pdf version).
  • The University of Alberta GAMES group.
  • The Berkeley GamesCrafters group.
  • The Stanford General Game Playing group.
  • An AI Connect 4 player
  • A paper showing PacMan is NP-hard. A slashdot discussion on it.

    • Week 5: Probability

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday (modified from Dan Klein's)
  • Information on pair programming; A video

    • Week 6: Utilities and MDPs (2/16,18)

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • Chapter's 3 and 4 of Sutton and Barto give a good introduction to MDPs.

    • Week 7: Reinforcement Learning

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • Sections 6.1, 6.2, 6.5 of Sutton and Barto give a good introduction to Q-learning

    • Week 8: Midterm

  • The slides presented in class: Thursday; more from Thursday (on RoboCup soccer keepaway); more from Thursday (modified from Dan Klein's)
  • The RoboCup soccer keepaway page with links to the videos shown in class and a paper with full details.

    • Week 9: Bayesian Networks:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)

    • Week 10: Probabilistic Reasoning over Time:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)

    • Week 11: Particle Filters:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • The readings on particle filters from my Fall 2011 graduate class on autonomous robots. Some are from the book "Probabilistic Robotics" by Thrun, Burgard, and Fox.
  • The full list of resources from that class, including the following:
  • Slides from the book: (ppt).
  • Slides about the legged localization paper: (pdf).
  • Some videos from the textbook authors.
  • A page with some good videos and diagrams on particle filters
  • The Hungarian algorithm for efficient matching.
  • Some videos from GA Tech on multi-object tracking

    • Week 12: Learning Probabilistic Models:

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • An on-line clustering demo.

    • Week 13: Supervised Learning:

  • The slides presented in class: Tuesday (modified from Dan Klein's);
    Thursday; more from Thursday (modified from Dan Klein's)
  • The UC Irvine Machine Learning Repository
  • Open source classification/regression software: WEKA
  • A new effort for comparing ML algorithms: ML comp
  • A decision tree applet from University of Alberta.

    • Week 14: Classical Planning

  • The slides presented in class: Tuesday; more from Tuesday (modified from Dan Klein's); The ones on Prodigy;
    Thursday; more from Thursday (modified from Dan Klein's); The ones on partial order planning
  • A USC planning class, including some graphplan slides
  • Planning today: 20th International Conference on Automated Planning and Scheduling.
  • Planning competitions
  • Two papers I published on domain-independent planning heuristics during my first year of grad school. A shorter conference paper and a longer journal article.
  • The RoboCup US Open

    • Week 15: Philosophical Foundations

  • The slides presented in class: Tuesday; Thursday
  • Why the Future Doesn't Need Us by Bill Joy - Wired, 2000. (pdf version)
  • The Essence of Soccer: Can Robots Play Too?
    Peter Stone, Michael Quinlan, and Todd Hester.
    Appear in a book on philosophy and soccer.
  • Some writings on the singularity: a resesarch institute, and Ray Kurzweil's page.
  • Jordan Pollack's GOLEM project: evolving physical robots.
  • Daniel Wilson's Robot Uprising book.
  • The Lifeboat Foundation is dedicated to assessing and protecting against threats to humanity.

    • Final Exam

  • As a practice, refer to Dan Klein's Berkeley Spring 2009 final exam and solutions. Note that they covered some different material than us. e.g. I wouldn't ask questions on CSPs (1c, 1f, 4c, 4f-h)
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