[image]Fig. 6 FEEDBACK NATURE OF *** [image]Fig. 7 DEVELOPMENT OF ASSOCIATIVE PATTERNS (MODELS) IN LEARNING A TASK *** [image]Fig. 8 A WORD ASSOCIATION MODEL *** IV. TYPES OF RESEARCH IN ARTIFICIAL INTELLIGENCE There are many subjects that the artificial intelligence researcher has used as a vehicle toward understanding this complex subject. In addition to other areas, language translation and games are two of the broadest. *** Language Translation It was hoped in the early days of research in language translation that programs could be written that would act as automatic translators and interpreters of natural languages. These programs would perform the same type of functions as interpreters at the United Nations. It was also hoped that programs could be written for retrieving natural language text from a computer. It seemed likely that encoding a relatively few basic words in a language translation program could be used as a 'bootstrap' toward the program's associating of new words (Fig. 8). But, after much work using this idea, researchers conclude that this is too difficult for our present stages of knowledge. Machine translation of typed scientific texts, let alone spoken language, is beyond our reach for the present. Some researchers suggest that we do not understand our own language process well enough to replicate it with the computer. *** Games Programming computers to play games has several purposes. First, games resemble many real problems. Second, in games, the problem is well defined, thus easier to work with. The third reason is that doing so may lend solutions for the solving of real problems. And finally, it's fun. Young animals have played games for eons in order to prepare for the real business of living. Game-playing on computers has a similar purpose for the computer scientist. An important part of many game-playing programs is a procedure for searching a 'tree' of logical possibilities. Indeed, this is essential for any problem solving to take place. The problem of trees is that the total number of alternatives available to us are far too many to be exhaustively searched. In order to guarantee a perfect first move in the game of checkers, for example, the program would have to decide its move on the basis of about 10 40 possibilities. In the game of chess, this figure would be about 10 120 possibilities. Therefore, it is easy to see that a method must be used to 'prune' this gametree, and thus cut down on the number of alternatives to be considered. One of the greatest achievements in the field of artificial intelligence has been a computer program to play draughts written by Dr. Arthur Samuel in 1967. This program takes about one minute to make its move and consistently beats Dr. Samuels, who is a good checker player himself. Although the World's Champion Checker Player has beaten the program four out of four games, the program beat the champion of Connecticut once. Even though the game of checkers has yielded somewhat to researchers, the game of chess has not. Computer programs to play chess are still not proficient at the game, despite predictions that a computer would be the world's champion chess player by 1967. *** Other Research John G. Chubbock, A. George Carlton, and others at the Applied Physics Laboratory of Johns Hopkins University have developed a device they call The Beast, The Beast is a battery-operated cylinder on wheels which roams the halls and plugs itself into an electrical outlet whenever its batteries become depleted. It has been known to 'survive' for as long as 40 hours without running down its batteries. Research in this area could be extremely useful in learning how to construct robots to be used in hostile environments, or to automatically chauffeur automobiles. Another use could be in an unmanned space ship going to Mars. Immediate computer response to unexpected problems would be essential since communications between Earth and Mars would take a couple of minutes. Another area of artificial intelligence research has been with computer programs called General Problem Solvers. Variants of this type of program attempt to simulate human behavior. Tests between a General Problem Solver and a college student have shown amazing similarities between thought processes of the student and the General Problem Solver. Although this area has been abandoned as a viable approach to artificial intelligence, it has served to broaden the researcher's outlook as to the problems of mechanizing thought processes. *** 49