# ECE 466

## Knowledge-System Engineering

Spring

Required Course:
No

### Course Level

Undergraduate

### Units

3

### Course Texts

Russell, Stuart, and Peter Norvig. *Artificial Intelligence: A Modern Approach*. 3rd ed. Pearson, 2009.

### Schedule

150 minutes lecture per week

### Course 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.

May be convened with ECE 566.

### 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

### Course Topics

- 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)

### Relationship to Student Outcomes

ECE 466 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

- Ability to apply knowledge of mathematics, science and engineering (high)
- Ability to identify, formulate and solve engineering problems (medium)
- Ability to communicate effectively (low)
- Recognition of the need for, and an ability to engage in, life-long learning (low)

### Syllabus Prepared By

Michael Marefat, 01/01/2013