CSCE 420 - Artificial Intelligence

Professor: Dr. Thomas R. Ioerger

email:	ioerger@cs.tamu.edu
Office:	322C Bright Bldg.
office hours:	Wed, 3:00-4:00

TA: Qing Wan

email address:	frankwanbear@gmail.com
office location:	339 HRBB
office hours:	Tues & Thurs, 2:20-3:20

Meeting: TR, 9:35-10:50, HRBB 124

Course Web Page: http://faculty.cs.tamu.edu/ioerger/cs420-fall18/index.html (this page)

Course Description (from TAMU course catalog): Fundamental concepts and techniques of intelligent systems; representation and interpretation of knowledge on a computer; search strategies and control; active research areas and applications such as notational systems, natural language understanding, vision systems, planning algorithms, intelligent agents and expert systems.

Prerequisites: CSCE 221 (Data Structures & Algorithms)

Textbook

Russell, S. and Norvig, P. (2009). 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.
   1. to learn how to formulate computational problems as search
   2. to learn how various search algorithms work
   3. to learn their computational properties (space- and time-complexity)
   4. to learn how heuristics can improve efficiency of search
2. To learn about knowledge representation techniques and methods for knowledge-based/intelligent decision-making in programs.
   1. to learn syntax and semantics of propositional logic and first-order logic
   2. to learn how inference algorithms work
   3. to learn the advantages of alternative knowledge respresentation systems
   4. to learn how to represent and reason about uncertainty using Bayesian probability
3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, machine learning, natural language...).
   1. to learn how symbolic planning algorithms work
   2. to learn different decision-making architectures for intelligent agents
   3. to learn how machine learning can be used to generalize from experience/examples

• 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 written homeworks and programming assignments. Students are expected to be proficient in programming in C++, based on prior course-work in the major.

Exams: There will be one mid-term exam and a non-comprehensive final exam (during finals week).

The overall score for the course will be a weighted combination of these components, which is tentatively set as follows:

• 40% for programming assignments and homeworks
• 30% for mid-term exam
• 30% for final exam

• 90-100% : A
• 80-90% : B
• 70-80% : C
• 60-70% : D
• < 70% : F

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:

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