Topics in the course "ARTIFICIAL INTELLIGENCE" we start on Monday! 1. Review of basic LISP constructs: macros, mapping primitives, LAMBDA definitions. Advanced LISP programming: structures, lexical and special variables, generators and encapsulation, procedures returning multiple values. 2. Review of classical problem solving: search and forward and backward chaining. 3. Pattern-directed inference: representation of data and rules. Natural deduction as a problem solving paradigm. Implementation examples of pattern-directed inference systems. 4. Introduction to Truth Maintenance Systems (TMSs): TMSs as a general tool for building intelligent problem solvers, representational issues and classification of TMSs. 5. Justification-based TMSs: knowledge representation, inference and explanation mechanism. JTMS as a framework for implementing default reasoning. 6. Logic-based TMSs: representing negation and Boolean constraint propagation. 7. Assumption-based TMSs: knowledge representation and inference procedures. 8. Contradiction-tolerant TMS: representation of uncertain knowledge and contradiction maintenance. 9. Model-based diagnosis, and the ATMS as a tool for implementing model-based reasoning systems. 10. Introduction to quality assurance of knowledge-based systems. The CTMS-based framework for verification, validation and refinement of knowledge bases. 11. Antecedent Constraint Languages. 12. Symbolic relaxation systems and constraint satisfaction: constraint networks and finite CSPs.
Posted on: Sun, 06 Oct 2013 18:58:53 +0000
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