1. problem-solving when you are trying to a achieve a goal, but the solution is not immediately obvious e.g., 2 X 5 = ? –not a problem, solution is immediately obvious e.g., 23 X 57 = ? (1,311) –problem, because answer not immediately obvious

2. Algorithms vs. heuristics some problems have guaranteed solutions (i.e., algorithms) –typically, math problems, physics problems, chemistry problems without algorithms, use heuristics (rules of thumb) –how to spend your money wisely; the most efficient driving route; money to raise a happy, health baby; finding a mate

3. parts of a problem clearly specified goal (goal-state) (aka, end-state) starting point (our current situation) (start- state) (aka, initial state) solution path = the way to get from the start-state to the goal-state actions that we can take = “operators”

4. start-state goal-state operators intermediate state Be happy Be married Financially stable In love unhappy unmarried solution path PROBLEM SPACE

5. Problem space problem space = all of the possible states, operators, and solutions for a problem (aka, search space) heuristics help us find a solution path; tell us which actions to try; but, not guaranteed to work

6. systematic random search trial and error = try one solution –if it works, you’re done! –if not, then you try another solution keep going until you find the solution –not efficient – could spend a lot of time trying to solve your problem random search = try operators randomly (could possibly use the same attempt twice) systematic = keep track of solutions that don’t work

7. backward search problem-solving heuristic “start” (imagining) at the goal-state seek a path from the goal-state to the start-state imagine what (intermediate) state you were in just prior to (immediately before) you got to the goal-state

8. Other issues rules = determine what are acceptable actions (operators) –e.g., student code; legal system must be extremely specific with your goal

9. Means-end analysis You are trying to change your current state to look as much as possible like the goal state 1. make a list of possible operators that could change our current state to look like goal state –Evaluate each operator; how far will it go towards goal?

10. More on m-e analysis 2. apply (use) operator that takes you the farthest toward your goal 3. go back to step 1 and do it over, starting at your new state 4. keep going until you have solved your goal