As discussed in the April 1998 Phi Delta Kappa article titled “What is problem solving”, general heuristics are “cognitive rules of thumb that are useful in solving a great variety of problems”. Specific heuristics are used in specialized areas, often-specific subject domains or professions. There are three common methods in heuristic problem solving. First, the most powerful general heuristic is to form a sub-goal to reduce the discrepancy between your present state and your ultimate goal state. Do something to get a little closer to the end goal. Problems defy one-shot solutions; they must be broken down into smaller parts. A second heuristic method seeks to solve prob

The second heuristic method used by Deep Blue is called “the killer” heuristic. The major advantage of this heuristic method is that it allows Deep Blue to keep track of recent killers by evaluating them first. The “killer” heuristic is useful in improving move ordering. The Killers are moves that `kill' the opponent. A killer in chess terms are captures, especially of `big' pieces by `little' ones, checks, promotions, but most games have moves that are more likely than others to be good. The `killer heuristic', is used keep track of recent killers, and try them again if they find them in the move list. These work on the grounds that a move that has worked once against random play by the opponent is quite likely to work again. The idea is that a good move in one branch of the tree is also good at another branch at the same depth. For this purpose at each play we maintain one or two killer moves that are searched before other moves are searched. A successful cutoff by a non-!

Another example of heuristic methods being used to find effective solutions involves the IBM computer “Deep Blue”. One of the reasons for Deep Blue’s success is that it utilizes heuristic techniques. Deep Blue is not only the finest chess-playing computer in the world, but it is also the fastest. Using heuristic techniques Deep Blue can conduct the most extensive searches analyzing more possible positions. The ability to do more searches gives the computer a wider array of moves to choose from and therefore a greater chance of choosing the optimum move.

One use of heuristics has been in the area of artificial intelligence. Artificial intelligence is increasing in many areas especially the science professions. With artificial intelligence you try to program a computer to make decisions in ways that are similar to a human would. To do this the computer will need to find solutions that are good, but may not be the best. Which means that the computer may estimate which solutions are “closest” to the goal. As you add in more variables, the solutions will change depending on the variable. The computer then users what it learns as it adds new variables to come up with new possible resolutions to the problems

The first method we will examine is called the “null move”. The major advantage of this heuristic method is that it allows Deep Blue to skip large searches in parts of the decision tree where the current position is currently optimized. The “null move” is used to select a piece and then value its current position versus a change to that position. If a change in position yields a lower value, than the current position yields, Deep Blue selectively then eliminates the remaining decision tree searches in the process. If the change in position yields a higher value then the previous position, then Deep Blue would selectively evaluate the remaining decision tree searches. The “null move” heuristic has big dangers because it can fail to detect deep combinations. The majority of all chess programs use null moves, but switch them off when the number of piec

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