CPSC 420H* - Artificial Intelligence

Spring 2006

* honor's section (min 3.5 GPA requirement)

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

Meeting: MWF, 11:30-12:20, 104 Bright Bldg.

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs420-spr06/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.
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:

Wed, Jan 18: first day of class; go over syllabus
Fri, Jan 20: What is AI? [Ch. 1] (perspectives; core concepts)
Mon, Jan 23:
Wed, Jan 25: Intelligent Agents [Ch. 2] (characteristics, environments, architectures)
Fri, Jan 27: Uninformed Search [Ch. 3] (DFS, BFS, ID)
Mon, Jan 30:

Wed, Feb 1: Informed Search [Ch. 4] (A*)
Fri, Feb 3: heuristics
Mon, Feb 6: local search: hill-climbing; simulated annealing
Wed, Jan 8: Constraint Satisfaction [Ch. 5]
Fri, Jan 10: CSP heuristics, constraint propagation, arc consistency...
Mon, Feb 13: local search for CSPs
Wed, Jan 15: discussion on empirical methods
Fri, Jan 17: Adversarial (Game) Search [Ch. 6] (minimax)
Mon, Feb 20: alpha-beta pruning; evaluation functions for large trees
Wed, Jan 22: (class cancelled)
Fri, Jan 24: (class cancelled)
Mon, Feb 27: Propositional Logic [Ch. 7] (knowledge representation, syntax); Project 1 due

Wed, Mar 1: semantics
Fri, Mar 3: Mid-term exam
Mon, Mar 6: Homework #1 due
Wed, Mar 8: inference
Fri, Mar 10: resolution
Mon, Mar 13: Spring Break
Wed, Mar 15: Spring Break
Fri, Mar 17: Spring Break
Mon, Mar 20: (class cancelled)
Wed, Mar 22: First-order Logic [Ch. 8] (syntax)
Fri, Mar 24: examples, numbers, sets
Mon, Mar 27: Homework #2 due (models, truth conditions)
Wed, Mar 29: inference in FOL (ground inference, instantiation, generalized modus ponens)
Fri, Mar 31: unification, Herbrand's theorem

Mon, Apr 3: resolution refutation proofs, conversion to CNF, Homework #3 due
Wed, Apr 5: theorem-provers (resolution, forward-chaining, Rete, backward-chaing, Prolog)
Fri, Apr 7: ontologies and knowledge engineering (categories, units, parts, space, time)
Mon, Apr 10: default logics, non-mon, higher-order; frames, semantics nets, description logics
Wed, Apr 12: Situation Calculus, Planning (read Ch. 11 up through 11.3, skip 11.4-)
Fri, Apr 14: Reading Day (class cancelled)
Mon, Apr 17: Frame Problem, STRIPS operators
Wed, Apr 19: state progression, goal regression
Fri, Apr 21: partial-order planning
Mon, Apr 24: Machine Learning (read 18.1-18.2, 19.1)
Wed, Apr 26: decision trees (read 18.3), Homework #4 due
Fri, Apr 28: statistical learning (read 20.1-20.4)

Mon, May 1: neural networks (read 20.5)
Tues, May 2: last day of class (redefined day as Fri)
Wed, May 10: final exam, 10:30-12:30

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