Academic journal article ETC.: A Review of General Semantics

Problem Solving with General Semantics

Academic journal article ETC.: A Review of General Semantics

Problem Solving with General Semantics

Article excerpt

Consciously or unconsciously all of us solve problems. We could solve them more effectively if we increase our awareness of what we do. In this article I discuss how you can use general-semantics formulations, along with modern problem solving methods, to improve your problem solving. These problem solving methods and techniques come from the areas of artificial intelligence/computer science, engineering, operations research, and psychology.

I will first provide an overview of problem solving, then relate problem solving methods to general-semantics formulations.

Problem solving can be broken down into six steps, as shown in figure 1.

These steps consist of: 1) Problem finding, where you find the problem before it finds you. Also at this stage, you can find the real cause(s) of the problem, so that you don't just treat the effects, and if you have many possible problems, you can find the appropriate one to solve. Once you have found your problem, or it finds you, then you do step 2) Problem definition. Here you define your problem in the most appropriate way. General-semanticists would apply the formulation "that the 'map' (i.e., the problem definition) should be of similar structure to the territory (i.e., the actual problem)." Asking the right question can be half the battle. That's why during the first two steps you try to make sure you solve the right problem. After defining your problem, you start to solve it, at step 3) Solution creation. Here you use an appropriate algorithm or heuristic method to create some possible solutions. An algorithm is a set of rules that guarantee a correct or optimal solution whereas heuristics only improve your chances of getting a solution, i.e., there is no guarantee. Also heuristics indicate little about the solution being the best. Once you have several possible solutions, you need to evaluate them and decide which you find the best, at step 4) Solution evaluation. In this step you evaluate your possible solutions to see if they are feasible (i.e., they obey all the constraints) and choose the best one. Once we have chosen a solution we move onto step 5) Solution implementation. Here you take action and carry out the solution or do the experiment, etc. Finally in step 6) Outcome evaluation, you evaluate the outcomes resulting from implementing that solution. From this evaluation and your knowledge of the problem and its solution, you learn, in order to do as well or better next time.

You don't always solve the problem in the linear sequence I've just described, but you may jump backwards and forwards through the steps, as shown in the diagram by the arrows. For example, when you evaluate your solutions at step 4, you may find none satisfactory, so you go back to step 3 and create some more solutions.

I will talk mainly about heuristic methods for solution generation and evaluation, as well as a method that uses outcome evaluation and learning.

Solution Generation Methods

By this stage in our problem solving, we have defined our problem. We now try to create some solutions for it. If it is a simple problem like dividing one number by another, you just apply the appropriate algorithm to it, (e.g., long division). But not all problems have algorithms, and even for those that do, as in chess, the algorithm could take too long to solve. In the case of chess, if every atom in the universe was a computer that could evaluate ten billion moves a second, it could take about [10.sup.20] years to find a winning sequence of moves, from the start of the game. Hence we sometimes use heuristics, which, while not guaranteeing a solution, take less effort and improves your chances of formulating one, compared to using trial and error. The main heuristic method that I will relate to general-semantics formulations, I call a "bypass." The bypass, as shown in figure 2, looks at a problem as a barrier between the current state, where you are, and the goal state, where you want to be. …

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