Download Poznan University of Technology European Credit Transfer System

Poznan University of Technology
European Credit Transfer System
Faculty of Computing and Information Science
Course Unit Description
Title
Code
101051414101051494
Artificial Intelligence
Field
Year / Semester
Computer Science
2/4
Specialty
Course
-
core
Hours
Number of credits
Lectures:
16
Classes: -
Laboratory:
16
Projects / seminars
:
-
4
Lecturer:
Artur Michalski, Ph.D.
Institute of Computing Science
60-965 Poznań, Piotrowo 2
tel. (48) (61) 665-2925, fax: (48) (61) 877 1525
e-mail: [email protected].
Faculty:
Faculty of Computing and Information Science
ul.Strzelecka 11, 60-965 Poznań,tel.(61) 665 34 20
e-mail: [email protected]
Status of the course in the study program:
This course is obligatory in the field of Computer Science.
Objectives of the course:
Students should be able to apply fundamental methods of artificial intelligence to solve
problems through inference and search.
Course description:
Introduction to Artificial Intelligence: history, basic concepts and definitions of AI. Application
domains of AI. Problem solving in logic: weak methods, modus ponens rule, unification
algorithm, resolution inference procedure, resolution strategies. Problem solving by search:
state space search, forward chaining, backward chaining, iterative deepening, backtracking.
Expert systems. Heuristic search/methods: hill climbing (gradient descent), best first search,
greedy search. Heuristic state estimation: admissibility of heuristic, monotonicity
and
informedness. Algorithm A*. Memory bounded search: IDA* algorithm. Game playing: games as
search problem, min max procedure, game state evaluation function, alpha beta pruning, alpha
beta algorithm improvements. Planning systems. Frame problem. Linear planning in blocks
world : STRIPS system. Sussman anomaly. Tasks decomposition and planning. Regressive
planners. Least commitment strategy in planning. Planning in plan space: nonlinear planners.
Partial order planning.
Initial knowledge:
Graph theory, propositional calculus, predicate calculus, declarative programming, set theory.
Teaching methods:
Lectures and computer laboratory classes with programming exercises.
Assessment methods:
Lecture: final examination; Laboratories: individual projects of expert systems.
Bibliography:
1.
2.
3.
4.
5.
Artificial Intelligence. A modern approach, Russell S. J., Norvig P., Prentice Hall, Inc., 1995
Artificial Intelligence, Second ed., Rich E., Knight K., Mc Graw Hill, 1991
Metody przeszukiwania heurystycznego, Bolc L., J. Cytowski, PWN, t1 1989, t2, 1991
Systemy ekspertowe, Mulawka J., WNT, Warszawa, 1996
Introduction to Artificial Intelligence, Charniak E., Mc Dermot D., Addison Wesley, 1985
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