ECE 466
Knowledge-System Engineering
Course Level
Units
Instructor(s)
Prerequisite(s)
Schedule
Course Description
Specific Course Information:
2021-2022 Catalog Data: Offered every two years. Knowledge systems are intelligent systems that totally or partially involve computational representation and processing of knowledge. This class introduces the principles and techniques for engineering and development of knowledge systems. Alternative computational structures for knowledge representation, procedures and algorithms for computational processing, automated reasoning and inference from knowledge, learning new knowledge, handling uncertainty in information, knowledge-based decision networks, distributed knowledge systems, alternative system architectures and engines.
Learning Outcomes
Specific Goals for the Course:
Outcomes of Instructions: 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
Brief list of topics to be covered:
- 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:
1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors