Artificial Intelligence

Semester:
5th
Course Type:
Elective Specialization courses (ΠΜ-E)
Track:
CS (Computer Science)
Code:
ΥΣ02
ECTS:
6
TEACHING HOURS per week
Theory:
3
Seminar:
1
Laboratory:
0
Specializations
Foundations of Computer Science (S1):
-
Data and Knowledge Management (S2):
B Βασικό
Software (S3):
B Βασικό
Hardware and Architecture (S4):
-
Communications and Networking (S5):
-
Signal and Information Processing (S6):
-
Related Courses
Prerequisites:
Expected Prior Knowledge:
-
Detailed Description
DIT_PPS_ΥΣ02-ArtificialIntelligence.pdf
Course Content
Artificial Intelligence. Intelligent Agents. Solving problems with search agents. Search strategies: breadth-first search, uniform-cost search, depth-first search, depth-limited search, iterative deepening depth-first search, bi-directional search. Heuristic search strategies: greedy best-first search, A*-search. Local search. Constraint satisfaction problems and algorithms. Knowledge-based Agents. Propositional logic and first-order logic. Using propositional logic and first-order logic to represent knowledge. Knowledge bases and ontologies. Examples from applications. Inference. Modus ponens, unification, forward and backward chairing, resolution. Introduction to logic programming and the language Prolog.
LITERATURE AND STUDY MATERIALS - READING LIST
  • Stuart Russel and Peter Norvig. Artificial Intelligence: A Μodern Approach, Prentice Hall, 2nd edition (2003). http://aima.cs.berkeley.edu/.
  •  I. Vlahavas et. al Artificial Intelligence http://aibook.csd.auth.gr
  •  Slides from lectures based on the Russel and Norvig book.
  •  Relevant material from the Web page of the course
e-Class link
Artificial Intelligence - webpage

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