Download Advanced Artificial Intelligence

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:
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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:
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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