Introduction to Artificial Intelligence
Semester B 2004-2005 (Spring 2005)
(* 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.
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 and logical 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.
- Course Reference: Artificial Intelligence
(202-1-5151) - Spring 2005
- Credits: 4
- Instructor: Prof. Solomon Eyal Shimony
- Grader:
Itzhak Beckman
- Course scheduled at:
- Sunday 14-16, building 90, room 240.
- Wednesday 13-15, building 28, room 205.
- Syllabus and requirements
- Midterm exams, (use of books and notes allowed). Dates:
- Sunday, May 1, 14-16, Building 90, rooms 240, 236.
Solutions.
- Wednesday, June 15, 13-15, Building 28, room 205.
Solutions.
- Assignments: will be base on a simplified version
of the game Unreal Tournament. Assignment and final
grades (.xls), updated July 18, 2005.
-
Assignment 1:
environments and search (simplified, deterministic).
NEW optional
graphical
interface you can use for output.
-
Assignment 2: game tree search - Hunt with an adversary.
-
Assignment 3: written exercise on agents, search, and games.
Solutions.
-
Assignment 4: probabilistic reasoning.
- Assignment 5: (BONUS ASSIGNMENT)
learning (heuristic evaluation functions for
game of simplified Unreal Tournament).
- Assignment 6:
Exercises in probabilistic inference and learning. Solutions (partial):
- Example quiz and
Answers
- Lecture topics and notes.
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