2-IKV-103 Fundamentals of Artificial Intelligence for Cognitive Science

Evaluation during the course: project evaluation

Concluding evaluation: exam + final projct evaluation

Subject aim: To give the students a basic insight into AI in connection with CS anb basic methods such as searching, solving constriant satisfaction problems, logical agents, planning , bayesian artificial intelligence and time series.

Brief curriculum of the subject:

Subject of AI:

- agent, methods, AI and Cognitive science, history of AI

Solving the problems by searching

- agent, state space, uninformed searching

- heuristic function, informed searching

- minimax, alfabeta prunning.

CSP problem

- backtracking, heuristisc

- forward checking, edge consistency, local search

Logics: logical agents, unification, rezolvence, forward and backward chaining

Planning: strips, pop, graphs

Bayesian AI

- Bayes rule, bayesian nets, inference in  bayesian nets (exact: direct sampling, probabilistic: rejection sampling,likelihood weighting, algoritmus mcmc)

Time series: dynamical system, time series, finding trend and period in time series

Applications to cognitive science

- emergent events

Literature:

S. Russel, P. Norvig: Artificial Intelligence. A Modern Approach, 2nd ed., Prentice Hall, 2003

Language in which the subject is taught: Slovak / English

Date of the last sheet revision: 6.6.2007