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Artificial Intelligence. Module Title AI 401 Module Code Subject Status Credit Rating Credit Level Pre-Requisite Subjects Co-requisite Subjects Mandatory 10 4 None None Lecture 2 Hours per Week Tutorial Computer Lab Other 1 Hour per Week 0 Total Contact Hours Subject Aims This subject aims to make students aware of the many areas of artificial intelligence and the tools available for AI type solutions. Identify suitable problems for AI solutions. Examine in detail and implement structures for representing knowledge. The manipulation of knowledge, especially rule based systems. Examine the various types of expert system paradigms with examples and examine the concept of expert system shells. Implementing some of the AI techniques that have been introduced with the AI Programming Language Prolog. Natural Language Parsing. Learning Outcomes: The student will be knowledgeable in the foundation and general principles of Artificial Intelligence The student will be aware of the types of AI solutions that can be formulated. The student will be able to formulate, prove and program logical representations. The student will be able to recognise structure and program various knowledge representations. The student will be able to represent and code search spaces and solutions. The student will be able to encapsulate knowledge in rule based format. The student will be knowledgeable in the representation and manipulation of natural language. The student will be able to program in a range of AI languages. Syllabus Content Knowledge Representation 20% Represent and manipulate and program knowledge using Logic - Predicate calculus and lists, trees Rule based systems Semantic Networks Frames and scripts Neural Networks Game trees Manipulation of Knowledge 20% Represent problem and search spaces. Represent and program search methods as a means of manipulating search spaces. Differentiate between various reasoning methods Natural Language Parsing 15% Interpret models for representing language content, canonical models etc. Interpret Construct and program parse trees and parsing rules Information retrieval and natural language parsing Identify the limitations of natural language understanding by computers Expert Systems 15% Explain the design and structure of expert systems Describe the features of expert system shells and tools E.G. CLIPS, COOL Critique Knowledge base design issues and development. Importance of explanation facilities. Examination of classical and commercial examples Hybrid Expert systems. AI Languages 30% Write Programs in Prolog to cover the following features Declaring and querying facts, rules, constants, variables, structures, lists Modifying the Database Formatting queries Evaluating goals with facts and rules Tracing evaluations Implement AI problems using Prolog and CLIPS Represent real world problems and relationships as facts and rules in the program database Evaluate theorem proving using Backward chaining, binding and instantiation Implement knowledge representations using Prolog and CLIPS. Achieving and Assessing the Attainment of Learning Outcomes Learning Outcome Teaching Methods Assessment Method 1) The student will be Lecture Examination Lecture Examination, programming assessment Lecture Examination Lecture, Lab Work Examination, programming assessment Lecture, Lab Work Examination, programming assessment Lecture, Examination, programming assessment Lecture, Lab Work Examination, programming assessment Lecture, Lab work Examination, programming assessment 2) 3) 4) 5) 6) 7) 8) knowledgeable in the foundation and general principles of Artificial Intelligence The student will be aware of the types of AI solutions that can be formulated. The student will be able to formulate, prove and program logical representations. The student will be able to recognise structure and program various knowledge representations. The student will be able to represent and code search spaces and solutions. The student will be able to encapsulate knowledge in rule based format. The student will be knowledgeable in the representation and manipulation of natural language. The student will be able to program in a range of AI languages. Method of Assessment: Continuous Assessment 40% Written examination Various small Programming tasks over the year Expert system Shell assignment incorporating NLP Knowledge representation and manipulation Assignment Final Examination 10% 10% 10% 10% 60% Examples of assessments Sample Simple Prolog Tasks 1) Write a predicate called extract_list which extracts lists from a list of elements containing lists and other types. 2) Write a predicate called display_list which use the member predicate to list all the elements of a list each on a separate line. 3) Write a predicate delete_lists that deletes (use the delete built in predicate in swi-prolog) all elements from a list that are lists. Expert system assessment Using a diagnostic area that you are familiar with, design an expert system using the rule format to represent rules in the problem domain you have chosen. Implement a meta-interpreter that will show how a conclusion was reached, ask questions of the user and explain why that question is being asked. Write a natural language interpreter using DCG rules that will handle simple expressions of fact and query in the context of the expert system. Knowledge representation and manipulation Assignment Planning and search in Prolog Build an intelligent Robot (Program) that can navigate the college (ground floor only). Recommended Reading Core Text Title Authors Publisher Year Artificial Intelligence a modern approach Computational Intelligence Russell and Norvig 2001 Programming in Prolog The Essence of Artificial Intelligence Expert systems and applied Artificial Intelligence Bradko Alison Cawley Prentice Hall ISBN 0-13-103805-2 Oxford ISBN 0-19-510270-3 Addison-Wesley Prentice Hall Turban MacMillan 1992 Authors Publisher Year John Kelly Covington, Nute & Vellino Davalo, Eric and Naim, Patrick Waterman, D. A. Callear, David Prentice Hall Prentice Hall MacMillan Education 1997 1997 1991 Addison Wesley DP Publications 1986 1994 Supplementary Reading Title The Essence of Logic Prolog Programming in depth Neural Networks A Guide to Expert Systems Prologue Programming for Students Poole, Mackworth, Goebel 1998 1991 1998 Teaching Methodology Lectures will be given on the theoretical content of the course. Lab work will be completed on the application of the theory presented. Assessment will be based on application of the theory to formulation of programmed content.