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Petra University (Private Accredited University) ( ) آ ات اب Faculty of Information Technology Department of Computer Science Course Title Course No. Credit Hrs Artificial Intelligence 601452 3 Instructor Name Office e-mail/Web Site Prerequisite 601351 – Algorithms Design & Analysis Year (semester) Lec./Lab. Credit 2008-2009(3) Lecture: 3 Lab : 0 Office Hours Coordinator Dr. Ghassan Issa Text Book Artificial Intelligence Structures and Strategies for Complex Problem Solving George F Luger & W. A. Stubblefield, 5th ed., Addison-Wesley, 2005 Course Description This course presents an introduction to the essential concepts and techniques of AI and its applications’ areas. The course focuses on the major sub-disciplines of AI such as: problem spaces, search strategies, knowledge representation, logic and inference mechanisms, automated reasoning and problem solving techniques. Other topics are introduced include Expert Systems, Intelligent Agents, Machine Learning, and Natural Language processing. Aims The main goal of this course is to give the students a practical and a theoretical overview of the AI technology and its fields through acquiring Conceptual Knowledge about Complex Problem Solving techniques and programming in logic. For achieving this goal there are intermediate objectives, which have to be reached during the different phases of the course. These sub objectives can be summarized as follows: Objectives – AI Fundamental issues & Applications: The difference between AI Approaches and Traditional Approaches, Features of AI, Turing Test, principles of Artificial Intelligence, Heuristics, Introduction to Knowledge Representation and problem solving, introduction to AI Application areas. Search and Constraint Satisfaction: State Space representation and its components, Uninformed search, Combinatorial explosion, Heuristic search, Characteristics of heuristic function, finding the optimal solution, Game Search, utilizing backtracking for Solving a Constraint satisfaction Problem Knowledge Representation and reasoning: represent knowledge using logical Formulas and prepositional logic, how to utilize resolution and unification as theorem proving method, shortcoming of two valued logic, basic principles of model theory, Search, Backtracking in PROLOG and Backward Chaining and Theorem Proving in PROLOG Advanced knowledge and Representation Reasoning under Uncertainty: how express uncertain knowledge using probability theory, Bayes Theorem, shortcoming of probability theory based knowledge representation and reasoning, Fuzzy Logic for representation of Imprecise and/or Uncertain knowledge. AI Planning Systems: the basic concepts of a planning system, the distinction between problem solving as a search problem and planning system, planning in robotics. 1 Natural Language Processing: role of knowledge Representation in a Natural Language Understanding Systems, syntax checkers based on different type of grammars, use PROLOG as a tool for programming a NLP system based on examples for Arabic. Intelligent Agents: architecture of an agent, agents that are capable of keeping track with their environments, the applications of the agent approach. Non-Symbolic AI and Emergent Fields: Neural Nets, Genetic Algorithms, Artificial Life Machine Learning: SYMBOL-BASED Vs. CONNECTIONIST, Version Space, The ID3 Decision Tree Induction Algorithm, Inductive Bias and Learnability Intended Learning Outcomes : Successful completion of this course should lead to the following learning outcomes : A- Knowledge and Understanding (students should): A1. Identify the different Application areas of AI A2. Understand the concept of problem solving as search, and learn how to use the various and Heuristic Search Techniques. A3. Understand the concepts and methodologies of Knowledge Representation. A4. Understand the basics of AI areas including Expert Systems, Case-Based and Model-Based Reasoning, Natural Language Processing, Neural Networks, Intelligent Agents, and Machine Learning. B- Intellectual Skills (Student should be able to): B1. Analyze, compare and criticize the different search techniques B2. Synthesize modified search algorithms from existing ones. B3. Contrast the main approaches to AI: symbolic((logic, semantic nets, rules) vs. emergent (connectionist, genetic) B4. Analyze and compare inference mechanisms. C) Subject Specific Skills (Student should be able to): C1. Solve a problem requiring a suitable knowledge representation and a search method C2. Learn the essentials of the Prolog Programming Language and experiment with an expert system shell C3. Write a report on a selected AI area D) Transferable Skills (Student should be able to): D1. work in a group in order to design and implement solutions of several AI problems. D2. Conduct research and Present results D3. Deploy communication skills D4. Deploy proper report writing skills Teaching and Learning Methods: Interactive lectures (IlOs: A1, A2, A3, A4) Lecture on major concepts and issues: Interactive lectures with videos and PowerPoint slides are conducted with lecturer explaining and illustrating the concepts. Students will be invited to share their view and experience in applying the concepts. Group Projects and Presentations (ILOs: B1, B2, B3, C1, C2, C3, D1, D2, D3, D4) Students will work on a course projects (2 to 3 students in a group). Each group will submit a short proposal of their project, including the names of team members starting from the second week of classes. Once the project is approved by the instructor, the group submits a more extended proposal which includes the role of each team member, Time-Plan, and the tools and applications that will be employed in the project. Each group will submit their project with a presentation at the end of the semester. 2 Online search / research and short presentations (ILOs: C2 , C3, D1, D3) Each student will be required to search the net for a new topic that relates to this course. A one page summary of this topic is to be submitted a long with a 10 minute presentation. Textbook Problems (ILOs: A1, A2, A3) Problems have been selected for in-class illustration of certain concepts and applications. Additional textbook problems have been assigned for students to practice and gain better understanding of the concepts discussed. Homework assignments will be collected for grading. Outside-classroom activities (ILOs: B3, C1 ,D2, D4) Students are required to schedule meetings with their groups, and to document the results of such meetings. AI Lab (ILOs: C1, C2, C3) Students are required to visit the AI lab and to experiment with Prolog and the expert System Shells available. Course Contents : Week Topics 1 Introduction 2 AI Principles 3 Strategies for State Space Search 4 Topic Details What is AI? The foundations of AI; The history of AI; Reference (chapter) Chp.1 Application Areas in AI Features of AI work, Knowledge Representation, Search and Problem Solving, Backward/Forward Search Problem Space, Uniformed Search (Graph-Search, BruteForce Search, Depth-First, Breadth-First, Depth-first with iterative deepening) Chp.1, Ch3, Notes Chp.3, Chp.4 Heuristic Search Simple-Hill Climbing, Steepest-Ascent Hill Climbing Heuristic Evaluation Function Characteristics of Heuristic function Chp.4 5 Heuristic Search Chp.4 6 Heuristics in Games Best-First Search Admissibility, Monotonicity, Informdness A* Algorithm MinMax (Full-ply and two-Ply) Alpha-Beta Procedure 7 Knowledge Representation Chp.4 First Exam Logic as Basic for Knowledge Representation and reasoning 8 Logic and PROLOG 9 Introduction to Agents Types of knowledge. Methods of Knowledge representations: Logic Representation Frames. Object-Oriented Representation, Semantic Networks Logic and Programming in Logic Prepositional logic Predicate Logic first Order Syntax: Terms, Formulae, Literals, Quantifiers, Clause Form, Horn Clauses Semantics Interpretation Logical Consequence Resolution Principle Resolution Refutation Principle Substitution Unification Resolution and Theorem Proving, Introduction to PROLOG Designing Intelligent Agent 3 Chp. 2, 7 Chp.7, 13 Chp. 7 Assessment Agent Human Agent Software Agent Robot Agent Simple-Reflex-Agent Intelligent Agent Simple Planning agent, Planning as Search Problem Representation of Planning problems, Block World Robot Planner Probabilistic Conditional Probabilities Bayes Theorem Certainty Factors Believe Functions Fuzzy Logic: Compositional Rule of Inference 10 Basic concepts of Planning 11 Reasoning under Uncertainty 12 Rule-Based Expert Systems 13 14 Non-Symbolic AI 15 Natural Language Understanding Notes Chp. 8 Chp.5, 9 Second Exam Overview of Expert System Technology; Rule-Based Expert Systems Model-Based, Case Based, and Hybrid Systems Forward and Backward Chaining Implementation issues Neural Nets, Genetic Algorithms, Artificial Life Chp. 8 Chp.11, 12 SYMBOL-BASED Vs. CONNECTIONIST, Version Space, The ID3 Decision Tree Induction Algorithm, Inductive Bias and Learnability Role of Knowledge in Language Understanding, Deconstructing Language: A Symbolic Analysis, Syntax and Knowledge with ATN Parsers, Natural Language Applications (Arabic NLP) Machine Learning 16 Chp.10, 11 Chp.14 FINAL EXAM Final Exam CONTINUAL COURSE Quality IMPROVEMENT The following Measures are taken seriously to continuously improve the quality of the course: Student Feed back: Using the University Student Evaluation, and the IT faculty Special Evaluation Form to provide instructor and department with feedback. Peer Visitation: Feedback from faculty members with similar specialization Course Coordinator: Participates in course updates, and monitors course progress Internal Examiner: Feedback pertaining to course outline, exams and projects, Course objectives and ILOs External Examiner: Feedback pertaining to course outline, exams and projects, Course objectives and ILOs ACM, AIS, and AITP Curriculum Guidelines MOH Guidelines for Standard Efficiency Exams Assessment and Grade Distribution Assessment I. Group Work Project Presentation II. Individual Work Attendance, Participation, and Home works Quizzes First Exam ILOs Requirement for Grading / Due Date B1, B2, B3, C1, C2, C3, D1, D2, D3, D4 Proposal + written Report 85% Discussions, Unannounced Short quizzes Covers Chapters 1, 3, 4 4 Total 15% 10% 5% Power Point Slides A1, A2, A3, B1, B2, B3, C1, C2, C3 Chapter Homework, Presentations Points Short 5% 15% Second Exam A Comprehensive Final examination Fifteen Multiple Choice Questions worth 25% of exam Grade. Four to Five Essay Questions worth 75% of exam grade. Covers Chapters 2, ,5, 7, 8, 9, 13 Fifteen Multiple Choice Questions worth 25% of exam Grade. Four to Five Essay Questions worth 75% of exam grade. Covers Chapters 1–5, 7-14 25 Multiple Choice Questions worth 25% of exam Grade. Five to six Essay Questions worth 75% of exam grade. TOTAL 15% 50% 100% * Make-up exams will be offered for valid reasons. It may be different from regular exams in content and format. References: 1. Artificial Intelligence A Modern Approach Stuart Russel and Peter Norvig, Prentice Hall, 2nd ed. 2003 2. Artificial Intelligence A Guide to Intelligent Systems M. Negnevitsky, Addison Wesley; 2 edition 2004 3. Artificial Intelligence: Theory and Practice Thomas Dean, Addison Wesley; 1st edition (May 10, 2002) 4. Artificial Intelligence, 3th Edition) Patrick Henry Winston, Addison Wesley; 3 edition (January 15, 1992) 5. PROLOG Programming Language For Artificial Intelligence Ivan Bartko, Addison Wesley; 4 edition (13 Nov 2008) 6. Artificial Intelligence, Nils J. Nilsson Morgan Kaufmann Publishers (1998) 7. Lecturer’s Notes COURSE POLICIES The University Regulations on academic dishonesty will be strictly enforced! Please check the University Statement on plagiarism. Make-up Exams: Only students with valid excuses are allwed to have make up exams. All excuses must be signed by the Faculty Dean. Student has the responsibility to arrange with his/her instructor for an exam date before the occurrence of the next regular exam. All assignment and class work must be submitted at the specified due date. No late work will be accepted. Attendance policy will be strictly enforced (refer to student's Handbook). No make up for quizzes under any circumstance. Last updated by Dr. Bassam Haddad 18. 02, 2008 Approved by: Course Coordinator Curriculum Committee Quality Assurance Committee Faculty Dean Name Date 5 Signature