Number of Unites: 4
Schedule: Three hours of lecture and one hour of
discussion per week.
Prerequisites: Discrete Mathematics, Software
Catalog Description :
artificial intelligence as well as current
trends and characterization
of knowledge-based systems. Search, knowledge
production systems, and expert systems will be
areas include knowledge discovery and neural
- Scope of AI: Games, theorem proving,
natural language processing, vision, expert
systems, AI techniques-search knowledge.
- Problem Solving: State space search;
Production systems, search
space control: depth-first, breadth-first
search, heuristic searches:
Hill climbing, best-first search, branch and
bound, Problem Reduction,
Constraint Satisfaction End, Means-End
- Knowledge Representation: Predicate Logic:
Unification, modus pones, resolution, and
dependency directed backtracking.
- Rule based Systems: Forward reasoning:
Conflict resolution, backward reasoning: use
of no backtrack.
- Expert Systems: Need and justification for
expert systems, knowledge acquisition, Case
- Structured Knowledge Representation:
Semantic Nets, slots, default frames,
conceptual dependency, and scripts.
- Handling uncertainty: Non-Monotonic
Reasoning, Probabilistic reasoning, and use
of certainty factors.
- Learning: Concept of learning, learning
automation, genetic algorithm, learning by
inductions, neural nets.
- Knowledge discovery in database.
Course Objectives & Role in the Program:
of the course is to
present an overview of artificial intelligence
(AI) principles and
approaches. Develop a basic understanding of
the building blocks of AI
as presented in terms of intelligent agents:
representation, inference, logic, and
learning. Students will implement
a small AI system in a team environment. The
knowledge of artificial
intelligence plays a considerable role in some
develop for courses in the program.
Upon successful completion of this course
- be able to design a knowledge based system,
- be familiar with terminology used in this
- have read and analyzed important historical
and current trends addressing artificial
Method of Evaluation
- Project participation and contribution
(will be graded on
individual basis and will include forum
participation, source code,
architecture, documentations contributions
and presentation) - 20%
- Home Assignments – 15%
- Final Exam (3 hours – Open book)– 30%
- Midterm Exam (2 hours – Open book) – 20%
- Class participation (including outside
reading presentations, quizzes and active
learning) – 15%
- Introduction to Artificial
Intelligence, Rajendra Akerkar;
Prentice Hall of India, 2005.
- Required software:
Use the stable versions and the
self-installing executable for Windows
95/98/ME/NT/2000/XP. For this course we
need only the basic components.
- Prolog Tutorials More tutorials: http://www.swi-prolog.org/www.html
- Quick Introduction to
Prolog Tutorial by J.R. Fisher
- Artificial Intelligence: Structures and
Strategies for Complex Problem Solving,
George Luger; Benjamin Cummings, 2004
- Artificial Intelligence: A
Modern Approach (2nd edition), Russell
& Norvig; Prentice Hall. 2003
- Introduction to AI and Expert Systems, D.
W. Patterson; PHI, 1992.
- Other course material will be provided
during the course.
General Online Resources: