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HU Credits:
5
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Computer Sciences
Semester:
2nd Semester
Teaching Languages:
English
Campus:
E. Safra
Course/Module Coordinator:
Prof Jeff Rosenschein
Coordinator Office Hours:
Tuesdays, 10:30am-11:00am
Teaching Staff:
Prof Jeff Rosenschein
Mr. Yoni Sher
Mr. Reshef Mintz
Course/Module description:
The course serves as an introduction to the solution techniques and application areas in the field of artificial intelligence.
List of Topics:
1. Introduction to artificial intelligence
2. Search: uninformed, informed, constraint satisfaction problems, adversarial search
3. Knowledge representation: propositional and first-order logic, inference, unification, resolution
4. Planning: partial order planning, planning graphs, hierarchical task network planning
5. Basic probability: axioms of probability, independence, Bayes’ Rule
6. Learning: learning from observations, learning decision trees, MDPs, reinforcement learning
7. Game theory: non-zero sum games, auctions, negotiation, voting, manipulation, power indexes
Course/Module aims:
To introduce students to the research field of Artificial Intelligence, with a particular emphasis on five basic areas within the field, namely: Search; Knowledge Representation; Planning; Learning; and Game Theory applied in multiagent systems.
Learning outcomes - On successful completion of this module, students should be able to:
See course aims
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Frontal lecture, plus exercise groups; students are given three small exams during the semester (on the five topic areas: search, knowledge representation, planning, learning, and game theory). Students also hand in four Python programming assignments, five regular assignments, and carry out a large-scaled project at the end of the semester.
Course/Module Content:
1. Introduction to artificial intelligence
2. Search: uninformed, informed, constraint satisfaction problems, adversarial search
3. Knowledge representation: propositional and first-order logic, inference, unification, resolution
4. Planning: STRIPS, SAS, PDDL, planning as SAT, planning as Search, relaxations, abstractions
5. Basic probability: axioms of probability, independence, Bayes’ Rule
6. Learning: MDPs, reinforcement learning, learning from observations, learning decision trees
7. Game theory: non-zero sum games, auctions, negotiation, voting, manipulation, power indexes
Required Reading:
The primary textbook for the course is "Artificial Intelligence: A Modern Approach", by Stuart Russell and Peter Norvig, Third Edition, 2010.
Additional Reading Material:
Additional optional reading material is provided for each topic.
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 40 %
Assignments 28 %
Reports 0 %
Research project 0 %
Quizzes 32 %
Other 0 %
Additional information:
The assignments, all together worth 28% of the final grade, are split into 4 Python programming assignments (worth 16% of the final grade) and five written assignments (worth 12% of the final grade).
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