CIS 587 Artificial Intelligence: Syllabus
- Instructor: Dr. Giorgio P. Ingargiola
- Office: Computer Activity Building, Room 1038
- Phone: (215)204-6825
- e-mail: email@example.com
CIS 203 or permission of Instructor. Some knowledge of first-order logic
and of Lisp or of Prolog is desirable.
- Artificial Intelligence: A Modern Approach
- Prentice-Hall, 1995(Recommended)
- Programs: 40%
- Final: 40%
- Midterm: 20%
You may choose, after agreement with instructor, to do a project with a
report and presentation
in place of the midterm and of selected homeworks.
The purpose of this course is to give students an understanding
of Artificial Intelligence methodologies, techniques, tools and
results. Students will use at least one AI-language [Lisp, Prolog].
Students will learn the theoretical and conceptual components of this
discipline and firm up their understanding by using AI and Expert
System tools in home assignments. Interactions between
Artificial Intelligence and other disciplines will be explored.
You will be assigned a number of homeworks. Most homeworks will
involve programming in Lisp.
Unless otherwise specified, you are expected to work on your own.
The midterm is on the third tuesday of March.
The final exam is on the last day of classes. The exams are
written, closed book, with small
but numerous questions on specific topics and techniques covered in the course.
Overview of history and goals of AI: 1 week
Tentative definitions. Turing's test.
Knowledge vs. Symbolic Level. Relations with other disciplines, from
Philosophy, to Linguistics, to Engineering. Review of AI successes and
State Spaces, Production Systems, and Search: 2 weeks
State Space representation of problems. Problem solving as search.
Definition and examples of Production Systems. Heuristic search techniques.
Two person games.
Knowledge Representation Issues: 1 week
Procedural Knowledge Representation vs. Declarative Knowledge + Reasoning.
Facts, General Assertions, Metaknowledge. The Frame Problem.
Using First-Order Logic for Knowledge Representation: 2 weeks
Propositional Logic: Semantics and Deduction. First Order Logic: Semantics and
Deduction. Unification. Resolution-based theorem proving. Using
to answer questions about the truth of sentences or to identify individuals
that satisfy complex constraints. Logic Programming.
Common Sense Reasoning : 0.5 week
Nonmonotonic reasoning and modal logics for nonmonotonic reasoning.
How to deal with Agents and their Beliefs.
Weak Slot-and-Filler Structures: 0.5 week
Semantic Nets and Frames. Scripts for representing prototypical combinations
of events and actions.
Rule-Based Systems: 1 week
Pattern-matching algorithms. The problem of Control in
Rule Based Systems. The Rete Algorithm.
Planning: 1 week
Representing plans. Partial order planning. Planning applications.
Statistical Reasoning: 2 weeks
Use of Certainty Factors in Rule-Based Systems. Associating probabilities
to assertions in first-order logic. Bayesian Networks. Fuzzy Logic.
Learning: 3 weeks
Learning to classify concepts using features of their instances.
Learning a concept [Induction] from examples. Explanation-Based Learning.
Version Spaces. Neural Nets with back propagation.