To be Offered Spring 2014
Offered every two years. Design and implementation of knowledge-based software systems, machine intelligence, expert system design, reasoning under uncertainty, advanced automated problem solving methods, case-based reasoning, machine learning, genetic algorithms, distributed intelligent systems, logical foundations of knowledge systems. Applications to robotics, manufacturing and CAD.
May be convened with ECE 566
Grading: Regular grades are awarded for this course: A B C D E.
Usually offered: Spring
ECE 373, or ability to program in Java or C++; knowledge of discrete mathematics
"Artificial Intelligence: A Modern Approach", Stuart Russell, Peter Norvig, latest edition, Prentice Hall.
Course Learning Outcomes:
- Understand propositional and first order logic
- Represent information in first order logical formulas, and perform formula unification/matching.
- Understand forward and backward automated inference
- Formulate problems as state space search.
- Develop programs for breadth-first, depth-first, heuristic, and hill climbing searches.
- Represent information in semantic networks
- Understand constraint networks, constraint satisfaction, and develop programs for constraint satisfaction
- Understand genetic operators, genetic optimization, and genetic learning and write programs for this purpose.
- Understand Bayes rules, Bayesian belief networks, and evidence accumulation
- Knowledge Representation in first order logic, Matching and Unification of first order logic formulae, [2 lectures]
- Rule-based Expert Systems, Rule Firing, forward and backward chaining, [2 lectures]
- Automated Planning and Problem Solving, Total Order problem solvers, Least Commitment Planning, Hierarchical Problem solving , [4 lectures]
- Search Methods, Depth-first Search, Breadth-first Search, Heuristic Search, Greedy Search, A* algorithms, Hill Climbing, [2 lectures]
- Structured Knowledge Representation, Representing Knowledge using Frames, Objects, and Semantic Networks, First Order Logic Correspondence, Matching, Inheritance, Defaults, and Automated Inference , [2 lectures]
- Constraints, Constraint Networks, Constraint Satisfaction, Node and arc Consistency, Compound Labeling, Constraint Satisfaction algorithms, Problem Reduction, Look ahead, Back Jumping, Interval Constraints, Interval calculus, Algorithms for Interval Constraint Satisfaction , [4 lectures]
- Genetic Algorithms, Genetic representation of knowledge, Fitness functions, Genetic operators, Genetic search and optimization, Genetic learning, [2 lectures]
- Bayesian Probabilistic Networks, Fundamentals from Probability Theory, Likelihood Vectors, and Conditional Probability Matrices, Hierarchical Propagation of Evidence, Computational Algorithms for General Networks , [5 lectures]
- Dempster-Shafer Theory of Evidence, Belief Interval Representations for uncertainty, Evidence Accumulation and Propagation , [2 lectures]
- Knowledge-based Decision systems, Intro to Utility Theory, Utility functions, Decision networks, Decision-theoretic knowledge systems, Sequential decision problems, value iteration. , [3 lectures]
Two 75-minute lectures per week.
4 major assignments during semester plus a few small assignments
One semester project
Midterm examination plus a final examination.
Computer Usage: Major assignments include programming in a high level language such as Java.
Relationship to Student Outcomes:
(a) an ability to apply knowledge of mathematics, science, and engineering (HIGH),
(e) an ability to identify, formulate, and solve engineering problems (MEDIUM),
(g) an ability to communicate effectively (LOW)
(i) a recognition of the need for, and an ability to engage in life-long learning (LOW),