how do you celebrate death with death? and how does an entire nation celebrate osama's death so blatantly as a rejoicing victory when there's got to be a few smart heads out there thinking about consequential retaliation. which had me thinking about how a computer could even weigh out options better than us. call me un-american.
means-ends thinking - is a skill used to reach a stated interpersonal goal (e.g., making friends). With this approach, a person plans step-by-step, sequenced means to reach that goal (e.g., talk to a group leader and show him how to play basketball). The person then identifies potential obstacles that could interfere with reaching that goal (e.g., the leader does not like basketball) and appreciates that problem solving takes time. For example, 3 months later, a few kids see the young boy practicing shooting goals with a hockey stick and ask him to teach them how to shoot goals. The young boy makes several friends.
weighing pros and cons is a skill used to decide whether to carry out an interpersonal act (e.g., going to a party the night before an examination). A person may process the decision by thinking, "If I go to the party I'll see my friends and have fun, but I might fail my exam and get into trouble," or "If I don't go to the party I'll miss meeting the beautiful girl who just moved into the neighborhood, but I'll have more time to study and I'll get a good grade on my test."
alternative solution thinking is the ability to name unconnected, alternative solutions to a stated problem (instead of connecting sequenced plans). For example, to make friends, a child could say, "She could ask him to go to a movie with her," or "She could have a party and invite lots of kids," or "She could get another kid to tell everyone how nice she is." Well-adjusted, more socially competent children were able to think of more alternative solutions than their less well-adjusted classmates.
consequential thinking is the ability to think of different things that might happen in certain situations. For example, David was at Kevin's house, and when Kevin wasn't looking, David put Kevin's new ball in his pocket and later took it home with him. When asked to state what might happen next rather than to list pros and cons, better adjusted youth could offer more different, relevant consequences (e.g., "Kevin will make David pay for a new ball," "He'll make David apologize," or "Call his mother or the police") and more empathic responses, including, "David will feel bad 'cause Kevin thinks he lost his ball" (Shure, 1985).
Type A programs would use a "brute force" approach, examining every possible position for a fixed number of moves using the minimax algorithm. Shannon believed this would be impractical for two reasons.
Type A programs would use a "brute force" approach, examining every possible position for a fixed number of moves using the minimax algorithm. Shannon believed this would be impractical for two reasons.
First, with approximately thirty moves possible in a typical real-life position, he expected that searching the approximately 109 positions involved in looking three moves ahead for both sides (six plies) would take about sixteen minutes, even in the "very optimistic" case that the chess computer evaluated a million positions every second. (It took about forty years to achieve this speed.)
Second, it ignored the problem of quiescence, trying to only evaluate a position that is at the end of an exchange of pieces or other important sequence of moves ('lines'). He expected that adapting type A to cope with this would greatly increase the number of positions needing to be looked at and slow the program down still further.
Instead of wasting processing power examining bad or trivial moves, Shannon suggested that "type B" programs would use two improvements:
- Employ a quiescence search.
- Only look at a few good moves for each position.
This would enable them to look further ahead ('deeper') at the most significant lines in a reasonable time. The test of time has borne out the first approach; all modern programs employ a terminal quiescence search before evaluating positions. The second approach (now called forward pruning) has been dropped in favor of search extensions.
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