Problem Solving. Views of Problem solving. Well-defined problems Much studied in AI Requires search Domain general heuristics for solving problems What about ill-defined problems? No real mechanisms for dealing with these
Play the game: http://www.mazeworks.com/hanoi/
333 = 27
434 = 81
535 = 243
636 = 729
Edward L. Thorndike (1874-1949) found that many animals search by trial and error
(aka random search)
Found that cats in a “puzzle box” (see left) initially behaved impulsively and apparently random.
After many trials in puzzle box, solution time decreases.
In order to escape the animal has to perform three different actions: press a pedal, pull on a string, and push a bar up or down
According to the hill-climbing strategy, at each step, you choose a path that moves you closer to the goal. This strategy can fail when steps are required that initially will move away from the goal
With these six sticks:
Make four of these:
Kohler observed that chimpanzees appeared to have an insight into the problem before solving it
Maier’s (1931) two-string problem
Only 39% of subjects were able to see solution within 10 minutes
Less experience might help...
(collapse of the upper right lobe, upper left in picture)
(Chase & Simon, 1973)
(Chi, Glaser, and Farr, 1988)
Novices group these
Experts group these
Chi, Feltovich, and Glaser, 1981)
(Voss et al., 1983)
Speed of reading
Do not predict
superior performance in a particular domain
Experts are not better problem solvers in general. Expertise is domain specific
* IQ tests might predict some success in high-complexity jobs (e.g. scientist)
disagree that concept of talent is useful or explains anything (genius is 90% perspiration and 10% inspiration)
this is controversial!
Graph from Ericsson et al. (1996) showing the cumulative amount of practice by two groups of aspiring musical performers (experts and good violinists) and those who planned to teach music