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COURSE DESCRIPTION Department and Course Number Course Title CIS 479 Artificial Intelligence Course Coordinator Total Credits Bruce R. Maxim 3 Current Catalog Description This course introduces students to basic concepts and methods of artificial intelligence from a computer science perspective. Emphasis of the course will be on the selection of data representations and algorithms useful in the design and implementation of intelligent systems. The course will contain an overview of one AI language and some discussion of important applications of artificial intelligence methodology. Textbook Luger, G. Artificial Intelligence (5th Edition), 2005. References Chopin, B. Artificial Intelligence Illuminated, 2004. Winston, P. H. and Horn, B. K. P. Lisp (3rd Edition), 1989. Course Goals This course is intended to provide an overview of the problems and methods studied in the field of artificial intelligence. The focus of the course will be on the study of methods of knowledge representation, data structures, and algorithms useful to the development of intelligent programs. The course will also include discussion of important applications of AI methodology. Prerequisites by Topic 1. Discrete mathematics is assumed to have been taken before this class. 2. Knowledge of at least one modem programming language. 3. Ability to write medium sized programs. 4. Knowledge of data structures (linked-lists, trees, graphs). 5. Knowledge of recursive algorithms. 6. Knowledge of searching and sorting algorithms. 7. Ability to perform algorithm analysis. 8. Knowledge of truth functional logic. 9. Some knowledge of elementary probability theory. 10. Some understanding of grammars. Major Topics Covered in the Course 1. LISP programming language (9 hours) 2. Heuristic Search Algorithms (3 hours) 3. Game Playing (3 hours) 4. Automated Problem Solving (3 hours) 5. Knowledge Representation: Semantic Nets, Frames, Scripts (4 hours) 6. Predicate Logic (2 hours) 7. Production Systems and Knowledge Engineering Tools (6 hours) 8. Uncertainty and Statistical Reasoning (3 hours) 9. Planning Systems (2 hours) 10. Machine Learning (3 hours) 11. Neural Networks (3 hours) 12. Constraint Satisfaction (3 hours) 13. Parallel and Distributed AI (3 hours) 14. Exams (5 hours) Laboratory projects (specify number of weeks on each) 1. Write a series of Lisp functions from a simple application area. (2 weeks) 2. Write game playing program in which the computer opponent uses mini-max with alpha-beta pruning. (5 weeks) 3. Perform the knowledge engineering for a classification or diagnostic task and used an expert system shell to implement the system (4 weeks) 4. Conduct experiments using an autonomous agent toolkit. (2 weeks) Estimate CSAB Category Content CORE ADVANCED CORE 1.0 ADVANCED 0.5 Computer Organization and Architecture Data Structures 1.0 Algorithms Software Design 0.5 Concepts of Programming Languages Oral and Written Communications Every student is required to submit at least 4 written reports (not including exams, tests, quizzes, or commented programs) of typically 10 pages and to make 1 oral presentations of typically 10 minutes duration. Social and Ethical Issues 1. Human risks and the development of knowledge-based software - exam questions Theoretical Content 1. Heuristics and Algorithms (6 hours) 2. Programming Language Paradigms (4 hours) 3. Algorithm Complexity (2 hours) 4. Knowledge Representation (4 hours) 5. Machine Learning Models (2 hours) 6. Automated Problem Solving Models (2 hours) 7. Non-Deterministic Reasoning (4 hours) Problem Analysis 1. Identify a domain expert for some classification or diagnostic task and construct a rule-based representation of this knowledge. (2 weeks) 2. Identify a source of training facts for some classification, diagnostic, or predictive task. (2 weeks) Solution Design 1. Design a game playing program which uses the mini-max algorithm with alpha-beta pruning as the basis of generating the computer's next move. (5 weeks) 2. Identify a domain expert for some classification or diagnostic task and construct a rule-based system that can solve representative problems in this domain. (4 weeks)