Spring 2014 // Offered every two years
ECE 466 -- Knowledge-Systems Engineering
Description: 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.
Grading: Regular grades are awarded for this course: A B C D E
May be convened with ECE 566
A special fee may apply for web-delivered sections.
Russell, Stuart and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd Ed. Pearson. 2009.
Course Learning Outcomes:
By the end of this course, the student will be able to:
- 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, 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 of 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, utility theory, utility functions, decision networks, decision-theoretic knowledge systems, sequential decision problems, value iteration (3 lectures)
Two, 75-minute lectures per week
Relationship to Student Outcomes:
ECE 466 contributes directly to the following specific Electrical and Computer Engineering Student Outcomes of the ECE department:
- an ability to apply knowledge of mathematics, science and engineering (High)
- an ability to identify, formulate, and solve engineering problems (Medium)
- an ability to communicate effectively (Low)
- a recognition of the need for, and an ability to engage in life-long learning (Low)