CPSC 625 - Artificial Intelligence

Fall 2006

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 845-0161
email: ioerger@cs.tamu.edu
office hours: (to be determined)

Meeting: MWF, 1:50-2:40, 113 Bright Bldg.

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-fall06/index.html

Prerequisites: CPSC 311 (Analysis of Algorithms)

Textbook

Russell, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. 2nd edition (green cover). Prentice Hall.

Goals of this Course

  1. To learn about intelligent search methods and their role in building complex problem-solving programs.
  2. To learn about knowledge representation techniques and methods for exploiting knowledge in programs.
  3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, learning...).
Topics Assignments, Projects, Exams, and Grading

The work for this course will consist of a mix of homework assignments, programming projects, and exams. The final grade for the course will be a weighted-combination of these three components, which is tentatively set as follows (though subject to change): 30% homework, 30% projects, 40% exams. There will most likely be a mid-term exam and a final exam. The minimum score for an grade of an A will be 90%, the minimum for a B will be 80%, and so on, though these thresholds may be lowered depending on the performance of the group overall.

Schedule:

Mon, Aug 28: first day of class; go over syllabus
Wed, Aug 30: What is AI? [Ch. 1] (perspectives; core concepts)

Fri, Sep 1: Intelligent Agents [Ch. 2] (characteristics, environments, architectures)
Mon, Sep 4:
Wed, Sep 6: Uninformed Search [Ch. 3] (BFS, DFS)
Fri, Sep 8: (UC,ID)
Mon, Sep 11: Informed Search [Ch.4] (Greedy/best-first, A*)
Wed, Sep 13: heuristics
Fri, Sep 15: local search (hill-climbing, simulated annealing); Homework #1 due
Mon, Sep 18: (class cancelled)
Wed, Sep 20: Constraint Satisfaction [Ch. 5]; Homework #2 due
Fri, Sep 22: CSP heuristics; constraint propagation techniques
Mon, Sep 25: arc-consistency
Wed, Sep 27: edge-labeling (vision), stochastic CSP algorithms
Fri, Sep 29: Game Search (Ch. 6); Homework #3 due

Mon, Oct 2: Propositional Logic (Ch. 7) (syntax)
Wed, Oct 4: (semantics)
Fri, Oct 6: (rules of inference)
Mon, Oct 9: Mid-term exam
Wed, Oct 11: (forward/backward-chaining); Project #1 due
Fri, Oct 13: (resolution; davis-putnam)
Mon, Oct 16: First-Order Logic (Ch. 8) (syntax, model theory); Homework #4 due
Wed, Oct 18: (preventing looping in backchaining)
Fri, Oct 20: (knowledge-based decision-making in the wumpus world)
Mon, Oct 23: Inference in FOL (Ch. 9) (unification; first-order rules of inference)
Wed, Oct 25: inference rules in FOL; Project #2 due
Fri, Oct 27: (resolution in FOL, Prolog)
Mon, Oct 30: (RETE/CLIPS/JESS) Homework #5 due

Wed, Nov 1: Ontologies (Ch. 10), (axioms for sets, numbers, measures, fluids)
Fri, Nov 3: (axioms for spatial relations and time, including event calculus/interval logic)
Mon, Nov 6: KR alternatives to FOL (semantic nets, frames, description logics...); HW #6 due
Wed, Nov 8: Uncertainty and Default Reasoning (non-monotonic/default logics)
Fri, Nov 10: Mid-term Exam #2
Mon, Nov 13: Bayesian Probability; Fuzzy Logic (probabilistic methods for uncertainty reasoning) (Ch. 13, 14.1)
Wed, Nov 15: (class cancelled)
Fri, Nov 17: Planning (Ch. 11) - Situation Calculus
Mon, Nov 20: STRIPS, state-space planning, and goal regression
Wed, Nov 22: (class cancelled)
Fri, Nov 24: Thanksgiving (class cancelled)
Mon, Nov 27: non-linear/partial-order planning (POP)
Wed, Nov 29: other types of planners (HTN's, etc.)

Fri, Dec 1: summary discussion about AI (read Ch. 29?)
Mon, Dec 4: last day of class

Final Project: Agent for Wumpus World in Prolog

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