CS 4320 / CS 5314 - Artificial Intelligence
Annoucements:

  • You can stop be Martine Ceberio's office on Monday May, 2nd from 3 to 5pm for any question you want to ask before the final on Tuesday.
  • Final on Tuesday May, 3rd: details here
  • report of your AI project, due by April, 29th (midnight): guidelines available here, all late submissions will be penalized by 15 points per day of lateness.

    Objectives: syllabus available here

    The objective of this course is to provide the students with a general understanding of artificial intelligence:
  • what is artificial intelligence? this is now a familiar term, but what is this about after all?
  • what are the main research areas in artificial intelligence? what people, working in making computers "intelligent", are interested in?
          * make a robot able to find its path to a goal?
          * enable a computer to deduce information from knowledge? even when knowledge is
          partial, and / or uncertain
          * design machines able to learn?
          * enable robot to "see"? "hear"? "speak" properly?
          * how robots / agents interact together?
    In particular, in class, we will review the above-mentioned topics, describe the corresponding techniques, learn how to recognize what kind of problem we tackle, etc.
    As many practical problems (i.e., from real-life) as possible will be presented in class.

    Students will also have to work on a project. Teams will be defined, and each team will have to pick up a subject for their project among a list of AI-related topics. They will team-work on this project all semester long, and will have to submit a report (+ program code) of their work at the end of the semester.
    More details about the class: a tentative schedule

     This class will meet every Tuesdays and Thursdays, from noon to 1:20pm. The textbook for this class is: "Artificial Intelligence, a Modern Approach" (second edition), by Stuart Russel and Peter Norvig, Prentice Hall Series in Artificial Intelligence.

    The content of classes is (tentatively) expected to be as follows:

  • week #1: Introduction to Artificial Intelligence
  • week #2: What are agents?
  • week #3: Knowledge and reasoning: semantic networks and logic
  • week #4: Knowledge and Reasoning: propositional logic + 1st mid-term
  • week #5: Knowledge and Reasoning: predicate logic, rule-based systems and applications
  • week #6: Expert systems + Uncertain Knowledge and Reasoning
  • week #7: Uncertain Knowledge and Reasoning
  • week #8: Problem-solving in AI
  • week #9: Problem-solving
  • week #10: Presentation of UTEP's career services + Presentation (by TA) of Joe Peirluissi's work
  • week #11: Spring break
  • week #12: End of problem-solving + Planning and constraint solving (no class on 03/31)
  • week #13: Review exercises (by TA) + 2nd midterm
  • week #14: Game playing
  • week #15: Game playing + Current research in constraints (distributed, speculative, flexible, etc.)
  • week #16: General Review

    As far as assignments and exams , there will be:
  • reading assignments, and homework assignments (randomly checked);
  • (announced and un-announced) quizzes throughout the semester;
  • 2 mid-terms (cf. syllabus for schedule);
  • 1 final exam.

    Topics covered in class so far:
  • Introduction to AI: class notes (pdf), slides (pdf)
  • Agents: class notes (pdf)
  • Knowledge representation: properties of KR systems, semantic networks, logic (propositional and predicate), rule-based systems, and applications (part of the class notes are here)
  • Expert systems and reasoning under uncertainty: part 1 (here), part 2 (here)

    Exercises given in class:
  • Intro to AI (pdf)
  • Logic: propositional logic (pdf), predicate logic (pdf)
  • Problem-solving: uninformed and informed search methods (pdf)
  • Questions to prepare the project (pdf)

    Quizzes and Exams:
  • Quiz #1 (pdf), #2 (pdf + solution), #3 (pdf), #4 (pdf), #5 (pdf) , #6 (pdf), #7 (pdf)
  • Midterm #1 (pdf), Midterm #2 (pdf)

    References, and other material:
  • Guidelines for midterm #2: here
  • Expert systems and probabilities: 1, 2, 3, 4
  • Presentation of Joe Pierluissi's work on Thursday March, 10th (his slides are available here)
    Project teams:
  • Team #1: Antonio Vasquez, Sean Dexter, Elmeisha Bellamy, Kumar Soujanya Mamidipally
    -- working on: a tutor system
    -- weekly meetings on: Tuesdays at 10am

  • Team #2: Marquez Gabriel, Victor Ponce, Tesleem Akinsipe, Steven Ruiz, Montalvo Luis
    -- working on: an expert system
    -- weekly meetings on: Tuesdays at 9:30am

  • Team #3: Yuhua Liu, Leobardo Landeros, Rodrigo Vega, Antonio Bologna
    -- working on: human-computer dialog
    -- weekly meetings on: Wednesdays at 3pm

  • Team #4: Aaron Skinner
    -- working on: game-development
    -- weekly meetings on: Thursdays at 10am

  • Team #4bis: Marina Rodriguez-Moya, Francisco Pajaro, Felipe Velez
    -- working on: ??
    -- weekly meetings on: ??

  • Team #5: Elizabeth Lujan, Joel Barba, Jaime Mendez, David Sterling
    -- working on: path finding
    -- weekly meetings on: Fridays at 11am

  • Team #6: Annette Arrigucci, Ivan Carrazco, Hector Quintana, Saul Acosta
    -- working on: game development (race cars)
    -- weekly meetings on: Tuesdays at 1:30pm

    Classes of Fall 2004: click here
    Classes of Spring 2004: click here
    Classes of Fall 2003: click here

    Martine Ceberio
    Last modified: Fri Jun 17 00:42:51 MDT 2005