Psych 240-Lecture 19

Front 1

Problem

Back 1

consists of some initial state in which a person begins and a goal state that is to be attained, plus a non-obvious way of getting from the first to the second.

Front 2

Well-defined problem

Back 2

Completely specified starting conditions, goal state, and methods for achieving the goal.
Geometry proofs

Front 3

Ill-defined problem

Back 3

Some aspects are not completely specified.
Finding the perfect mate.
Choosing a career.
Writing the best novel.
Peace in the Middle East.

Front 4

5 stages of problem solving

Back 4

Form a representation
Construct a plan
Execute plan
Checking/Evaluation Reformulate

Front 5

Problem space

Back 5

whole range of possible states and operators,
only some of which will lead to the goal state

Front 6

Operators in math problem

Back 6

Substitute for n

Front 7

Analogies

Back 7

Retrieve a representation of a problem from memory that is similar to the problem you currently face.

Solve the new problem using the solution to the old problem.
People tend to miss deep similarities between problems, because they tend to focus on surface similarities.

Front 8

Gick and Holyoak hypothesis

Back 8

Maybe if subjects know of an analogy, they will be better able to come to the correct solution to Duncker’s Ray Problem

Front 9

Gick and Holyoak Analogical Prob solving

Back 9

Group 1: Unrelated story-Ray prob: 10%

Group 2: Military story-Ray problem: 30%

Group 3: Military story-(hint)-Ray problem: 80%

Front 10

Hindrance in problem solving

Back 10

Top-down preconceptions

Constrain problem space unnecessarily
Nine-dot problem

Front 11

Functional Fixedness

Back 11

see an object as having only a fixed, familiar function.
Candle problem
Two-ropes problem

Front 12

Examples of Functional Fixedness

Back 12

Candle Problem
2 ropes problem

Front 13

examples of Constraining problem space unnecessarily

Back 13

9 dot problem
Cheap necklace problem

Front 14

Luchin's water jar problem proves what

Back 14

Stuck in set

Front 15

Algorithm

Back 15

A method that will lead to a guaranteed solution (if one exists)
Consider all possible moves within problem space.

Front 16

Heuristic

Back 16

Short cut / “Rule of thumb”
Won’t always work
Reduces the number of moves to be considered

Front 17

Difference reduction

Back 17

At any point, select the operator that moves you closer to the goal state: is new state more similar to goal?
(never choose an operator that moves you away)


Should not do this though..example is orc problem

Front 18

Means-end analysis

Back 18

Identify the largest difference between current state and goal state
Set as a subgoal reducing that difference.
Find & apply an operator to reduce the difference
(If operator can’t be applied, new subgoal = remove obstacle that prevents applying the operator)

Front 19

Means-end analysis with air travel

Back 19

Goal: Get to California
Operator: Fly in a plane
Subgoal: Get to airport
Operator: Driving car
Subgoal: Get to car
Operator: walking
Apply walking operator to achieve subgoal
Apply Driving car operator to achieve sub-goal
Apply Flying in plane operator to achieve goal.

Front 20

Working backward

Back 20

Transform goal state so it is more similar to the initial state.
Start with solution and see what you can derive from that.
Apply an operator to the goal state.

Useful if too many paths leading from initial state.