* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Advanced Artificial Intelligence
Survey
Document related concepts
Human-Computer Interaction Institute wikipedia , lookup
Concept learning wikipedia , lookup
Computer vision wikipedia , lookup
Collaborative information seeking wikipedia , lookup
Reinforcement learning wikipedia , lookup
Machine learning wikipedia , lookup
Computer Go wikipedia , lookup
Artificial intelligence in video games wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Intelligence explosion wikipedia , lookup
Philosophy of artificial intelligence wikipedia , lookup
Ethics of artificial intelligence wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Transcript
Yarmouk University Faculty of Information Technology and Computer Sciences Department of Computer Sciences Second Semester 2009/2010 CS670: Advanced Artificial Intelligence Credit Hours: 3 Prerequisite: none Lecturer: Dr. Faisal Alkhateeb Tel: 2464 Office No: khz 204 email: [email protected] Course Description: Deduction, symbolic reasoning, searching, statistical pattern classification, searching cognitive modelling, planning, robotics, vision, machine learning, genetic algorithms, parallel and distributed artificial intelligence. Course Objectives: This course will cover several advanced topics in Artificial Intelligence. By the end of this course, students should possess a firm grounding in the existing techniques and component areas of Artificial Intelligence and be able to apply this knowledge to the development of Intelligent Systems or to the exploration of research problems. Learning Outcomes: Upon completion of this subject students are expected to: Understand the principles of problem solving and be able to apply them successfully. Be familiar with techniques for computer-based representation and manipulation of complex information, knowledge, and uncertainty. Gain awareness of several advanced AI applications and topics such as intelligent agents, planning and scheduling, machine learning, etc. Teaching Methods: The course will be based on the following activities: Lectures Case Studies Lab sessions Review questions 1 Evaluation Plan: Students will be evaluated using the following assessment methods: Mid Exam 30% Project and/or paper presentation 30% Final 40% Teaching Recourses: Main Textbook Artificial Intelligence: A modern approach by Stuart Russell and Peter Norvig. Second edition, Prentice Hall, 2003. Suplementary Textbooks 1. Artificial Intelligence, second Edition, by Elaine Rich, and Kevin Knight, McGrawHill, Inc, 1991. 2. Artificial Intelligence structures and strategies for complex problem Solving. Third Edition. George F. Luger and William A. Stubblefield. Course Plan: Week no. 1. 2,3 4, 5 6 7 Topic Intelligent Agents (ch. 2) Agents and Environments, Good Behavior: The Concept of Rationality, the Nature of Environments, the Structure of Agents. Solving Problems by Searching (ch. 3) Problem-Solving Agents, Example Problems, Searching for Solutions, Uninformed Search Strategies, Searching with Partial Information. Informed Search and Exploration (ch. 4) Informed (Heuristic) Search Strategies, Heuristic Functions, Local Search Algorithms and Optimization Problems, Local Search in Continuous Spaces, Online Search Agents and Unknown Environments. Constraint Satisfaction Problems (ch. 5) Constraint Satisfaction Problems, Backtracking Search for CSPs, Local Search for Constraint Satisfaction Problems. Adversarial Search (ch. 6) Games, Optimal Decisions in Games, Optimal strategies,The minimax algorithm, Optimal decisions in multiplayer games, Alpha2 8, 9 10, 11 12, 13 14 15, 16 Beta Pruning, Cutting off search, State-of-the-Art Game Programs. Planning (ch. 11, 12) The Planning Problem, Planning with State-Space Search, PartialOrder Planning, Planning Graphs, Planning with Propositional Logic, Analysis of Planning Approaches. Time, Schedules, and Resources, Hierarchical Task Network Planning, Planning and Acting in Nondeterministic Domains, Continuous Planning, MultiAgent Planning. Learning from Observations (ch. 18) Forms of Learning, Inductive Learning, Learning Decision Trees, Ensemble Learning. Natural Language Processing (ch. 22) A Formal Grammar for a Fragment of English, Syntactic Analysis (Parsing), Efficient parsing , Augmented Grammars, Semantic Interpretation, Ambiguity and Disambiguation, Discourse Understanding. Computer Vision (ch. 24) Image Formation, Early Image Processing Operations, Extracting Three-Dimensional Information, Object Recognition. Projects and paper presentation 3