Artificial Intelligence (AI) def: - AI is the branch of science that tries to automate the intelligent behavior of the human to allow computers to perceive, reason, and decide.
Course Description:
Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the computer equipped with AI technology can be even more intelligent than us. The Deep Blue system which defeated the world chess champion is a well-know example.
The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem solving and for reasoning. In fact, the skill for problem solving itself should be acquired through learning. Also, methods for problem solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition.
In this course, we will study the most fundamental knowledge for understanding AI. We will introduce some basic search algorithms for problem solving; knowledge representation and reasoning; pattern recognition; fuzzy logic; and neural networks.
Course Objectives:
This course introduces students to basic concepts and methods of artificial intelligence from a computer science perspective. Emphasis of the course will be on the selection of data representation and algorithms useful in the design and implementation of intelligent systems. The course will contain an overview of one Al language and some discussion of important applications of artificial intelligence methodology.
· To have an appreciation for and understanding of both the achievements of AI and the theory underlying those achievements.
· To have an appreciation for the engineering issues underlying the design of AI systems.
· To have a basic proficiency in a traditional AI language including an ability to write simple to intermediate programs and an ability to understand code written in that language.
· To have an understanding of the basic issues of knowledge representation and blind and heuristic search, as well as an understanding of other topics such as minimax, resolution, etc. that play an important role in AI programs.
Course Aim:
The general course goal is to give basic skills and understanding of AI.
Ø Knowledge and understanding
· Demonstrate basic knowledge and understanding of AI both individually and in a group.
· Demonstrate basic knowledge of proven AI methods and theories.
· Demonstrate insight in how AI is engineered in industry.
Course Components:
· Introduction
AI definition AI roots and history, the Turing Test, characteristics of intelligent systems, types of tasks, faces of AI, Agents and example
· Mathematical Structures for Computer Science
· Knowledge representation and reasoning
First-order logic and inference with first –order logic.
· Resolution algorithm
Resolution principle
Producing the Clause Form
Resolution Proof Procedure
Unification algorithm
Most general unifier
· Search strategies
State space search
Problem reduction
· Problem solving and search strategies
Blind search,
Heuristic search strategies
Best-First Search
Depth first search
Best first search
A* algorithm
Text book:
· Russel, S.J. and Norvig, P. (2003). Artificial intelligence a modern approach. New-Jersey, Prentice-Hall.
· Luger, G.F.. (2005). Artificial intelligence structure and strategies for complex problem solving. Addison- Wesley.
· Padhy, N. P. (2005). Artificial intelligence and intelligent systems. New Delhi: Oxford University Press.
· Turban, E., Aronson, J.E, Liang, T. & Sharda, R. (2007). Decision support systems and business intelligent systems. New Jersey, Prentice-Hall.
In addition to the above, the students will be provided with handouts by the lecturer.
Learning Outcomes:
· Knowledge and understanding
· Cognitive skills (thinking and analysis).
· Communication skills (personal and academic).
· Practical and subject specific skills (Transferable Skills).
Assessment Instruments
Makeup Policy
No missed tests without prior excuse. Each case will be handled separately based on its own merits. Makeup tests will be much more difficult than regularly scheduled tests. Each student is responsible for what is covered and assigned in any classes which they miss. Abuse of this policy will result in a loss of leniency.
Note that:
· You should know something about Lisp and Prolog.
· This syllabus may change as the course progresses