CSCE 625 - Artificial Intelligence

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 845-0161
email: ioerger@cs.tamu.edu
office hours: Tues, 1:00-2:00, or by appointment

TA: Jaewook Yoo
email: jwookyoo@neo.tamu.edu
office hours: Mon, Wed, 12:30-1:30pm, HRBB 339

Meeting: MWF, 10:20 am-11:10, HRBB 113

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs625-fall12/index.html

Course Description (from TAMU course catalog): Basic concepts and methods of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule based deduction systems; knowledge representation; reasoning with uncertainty.

Prerequisites: CSCE 221 (Data Structures and Algorithms)

Textbook

Russell, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. 3rd edition (blue cover). Prentice Hall.

Course Objectives

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 knowledge-based/intelligent decision-making in programs.
3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, machine learning, natural language...).

• Perspective on AI (philosophical, psychological, engineering)
• Intelligent Agents (characteristics, environments, architectures)
• Search Algorithms (depth-first, breadth-first, iterative deepening)
• Heuristic Search (best-first, A*, hill-climbing)
• Constraint Satisfaction Problems
• Adversarial (Game) Search (minimax, alpha-beta pruning)
• Propositional Logic
• First-Order Logic (syntax and semantics)
• Inference Methods (natural deduction, resolution, unification)
• Knowledge Representation, Ontologies
• PROLOG
• Modal logics, description logics, fuzzy logic
• Planning (goal-regression, STRIPS, frame problem)
• Uncertainty and Probability (Bayesian inference, belief nets, Markov decision problems)

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: 50% homework/programming projects, 50% exams. There will most likely be 2 mid-term exams and a final exam. The minimum score for an grade of an A will be 90%, the minimum for a B will be 80%, 70% for C, and so on, though these thresholds may be lowered depending on the performance of the class overall.

The penalty for late assignments is -5% per day (pro-rated over 24 hours).
After 10 days late, the deductions cease; the maximum loss of points is 50%. As long as you turn an assignment in by the end of the semester, it could still be worth as much as half-credit. This is to encourage you to eventually complete the assignment, even if you can't get it in on time initially.

Schedule:

Academic Integrity Statement and Policy

Americans with Disabilities Act (ADA) Policy Statement

Links

• Ch. 1, The Description Logic Handbook
• Ch. 2 from the Description Logic Handbook - Baader and Nutt
• Actions and Events in Interval Temporal Logic (1994). James F. Allen and George Ferguson. Journal of Logic and Computation.
• some background info on the Tile Puzzle - shows that half of randomized configurations are unsolvable
• PRIMES is in P. (Agarwal et al, 2004), perspective by Bornemann
• Satisfiability cliff
• vision as CSP
• Deep Blue: 31-page paper describing implementation details
• TPTP Database - Thousands of Problems for Theorem Provers
• Ch. 7 notes
• Naive Physics Manifesto - Ontology of Liquids Patrick Hayes, 1986
• first-order logic model theory
• Weld (1994) - section 3.2 contains a nice description of goal-regression
• SWI-Prolog manual
• my notes (mini-tutorial) on Prolog
• Charniak, E. (1991). Bayesian Networks without Tears. AI magazine.
• D'Ambrosio, B. (1999). Inference in Bayesian Networks. AI Magazine.
• Ch. 8 from Patt Rec and ML by C. Bishop
• IBM's Jeopardy-playing WATSON, NY Times article