Introduction to Artificial Intelligence
Semester A 2010-2011 (Fall 2010)
* Under Construction *
BGU Computer Science Department
Description of the course
Artificial intelligence (AI) has recently regained the limelight, as the human
world chess champion was beaten by Deep Blue, a program written by a team
of researchers and programmers from IBM. Even more recently, a "re-match"
against a distributed machine in Jerusalem also favored a computer program.
In the more difficult field of partial information and chance games,
such as Poker, AI programs now hold their own against human
champions, as exhibited in a competition held during AAAI-2008.
True AI applications are also
on the rise, from expert systems for diagnosis and advice, through increasingly
intelligent robots, to intelligent and autonomous www agents.
This course deals with the issues of defining intelligence and rationality
in an agent, various methods of formalizing them, and
models for representing and using knowledge. In specific topics, mainly
search, logical reasoning, and probabilistic reasoning,
the course will focus all the way down to the
algorithm level, in order to provide some hands-on experience with programming
artificially intelligent agents.
Since this is now a graduate-level course, weight assigned to examining some open reasearch
problems in some sub-fields of AI will be increased.
Course data and information pointers
- Course Reference: Artificial Intelligence
(202-2-5661) - Semester A 2010-2011 (Fall 2010)
- Credits: 4
- Instructor: Prof. Solomon Eyal Shimony
- Grader: Iddo Aviram, see grader's
- Course scheduled at:
- Monday 9-11, Building 34, Room 107.
- Wednesday 9-11, Building 34, Room 107.
- Syllabus and requirements
- Midterm exams, (use of books and notes allowed):
- 1st midterm exam: Friday, December 3 at 9:00, Building 97 rooms 201, 202.
- 2nd midterm exam: Thursday, January 6 at 17:00, Buliding 34, room 202.
definitions and algorithms for Bayes networks.
Environments and search (simplified, deterministic).
Assignment 2: Adversarial games.
Assignment 3: theoretical assignment on search and reasoning,
Solo submission. Solutions.
- Assignment 4: Partial observability - reasoning.
- Assignment 5: Partial observability - decision-making.
- Assignment 6: Exercise on reasoning and decision making under uncertainty, and learning. Solo submission. Solutions.
- Assignment 7: Partial observability - decision-making, bonus assignment.
- Example quiz and
- Lecture topics and notes.
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