ACT-R

Question 1

ACT-R

Answer 1

cognitive architecture : a theory of simulating and understanding human cognition. Researchers working on ACT-R strive to understand how people organize knowledge and produce intelligent behavior.

Question 2

declarative memory

Answer 2

memory of facts and events, represented in chunks, created by encoding of objects in the environment

Question 3

chunks

Answer 3

ACT models store declarative memories in ‘‘chunks’’ which are collections of elements

Question 4

procedural memory

Answer 4

refers to memory of skills, created by encoding of chunks / transformations of environment, represented in production rules

Question 5

production rules

Answer 5

set of rules in aproduction system about behavior

ACT models store procedural memory in form of production rules that have a IF condition and a THEN condition
-> if a productions precondition matches the current state of the world , then the production is said to be triggered

Question 6

production compilation

Answer 6

the process by which knowledge from declarative memory is tranferred to procedural memory (new rules are learned with this process)

Question 7

cognitive modelling

Answer 7

area in cog science that deals with explaining cog processes in the brain, described in mathematical/computer language

> such a model can be used to simulate or predict human behavior or performance on tasks similiar to the ones modeled and improve human-coputer interaction

Question 8

court of last resort

Answer 8

if it is not known how to obtain x , create a space known to include X and search for it

Question 9

knowledge search

Answer 9

the agent searches in memory for knowledge to select operators for problem search

Question 10

problem search

Answer 10

the agent searches within a problem space for knowledge that helps with the problem

Question 11

prerparation vs deliberation trade off

Answer 11

intelligent agents do knowledge and problem search, must do trade off between deliberation (analysing situation) and preparation (use stored preparation)

Question 12

sub symbolic learning

Answer 12

human based info formats arent always the best fit for AI and encourages feeding raw info into the AI that it can then analyze and construct its own knowledge spread over units and connections between units, learning as change of connections between chunks and production ruless -> hebb, pattern associator, selective retention

Question 13

symbolic learning

Answer 13

the best way to teach AI is to feed it human-readable info related to what you think it needs to know

Question 14

list memory

Answer 14

experimental paradigm used in cognitive psychology to investigate how people store and recall items from short term memory