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The Art of Computer Game Design
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The obvious alternative to the use of sheer numbers is to provide the computer player with intelligence adequate to meet the human on equal terms. Unfortunately, artificial intelligence techniques are not well enough developed to be useful here. Tree-searching techniques have been developed far enough to allow us to produce passable chess, checkers, and Othello players. Any other game that can be expressed in direct tree-searching terms can be handled with these techniques. Unfortunately, very few games are appropriate for this treatment. An alternative is to develop ad-hoc artificial intelligence routines for each game. Since such routines are too primitive to be referred to as "artificial intelligence", I instead use the less grandiose term "artificial smarts". This is the method I have used in TANKTICS, EASTERN FRONT 1941, and LEGIONNAIRE, with varying degrees of success. This strategy demands great effort from the game designer, for such ad-hoc routines must be reasonable yet unpredictable. Our first requirement of any artificial smarts system is that it produce reasonable behavior. The computer should not drive its tanks over cliffs, crash spaceships into each other, or pause to rest directly in front of the human s guns. In other words, obviously stupid moves must not be allowed by any artificial smarts system. This requirement tempts us to list all possible stupid moves and write code that tests for each such stupid move and precludes it. This is the wrong way to handle the problem, for the computer can demonstrate unanticipated creativity in the stupidity of its mistakes. A better (but more difficult) method is to create a more general algorithm that obviates most absurd moves. A second requirement of an artificial smarts routine is unpredictability. The human should never be able to second-guess the behavior of the computer, for this would shatter the illusion of intelligence and make victory much easier. This is may seem to contradict the first requirement of reasonable behavior, for reasonable behavior follows patterns that should be predictable. The apparent contradiction can be resolved through a deeper understanding of the nature of interaction in a game. Three realizations must be combined to arrive at this deeper understanding. First, reaction to an opponent is in some ways a reflection of that opponent. A reasonable player tries to anticipate his opponent s moves by assessing his opponent s personality. Second, interactiveness is a mutual reaction---both players attempt to anticipate each other s moves. Third, this interactiveness is itself a measure of "gaminess". We can combine these three realizations in an analogy. A game becomes analogous to two mirrors aligned towards each other, with each player looking out from one mirror. A puzzle is analogous to the two mirrors being unreflective; the player sees a static, unresponsive image. A weakly interactive game is analogous to the two mirrors being weakly reflective; each player can see and interact at one or two levels of reflection. A perfectly interactive game (the "gamiest game") is analogous to the two mirrors being perfectly reflective; each of the two players recursively exchanges places in an endless tunnel of reflected anticipation s. No matter how reasonable the behavior, the infinitely complex pattern of anticipation and counter-anticipation defies prediction. It is reasonable yet unpredictable.
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The Art of Computer Game Design
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Unfortunately, a perfectly interactive game is beyond the reach of microcomputers, for if the computer is to anticipate human moves interactively, it must be able to assess the personality of its opponents---a hopeless task as yet. For the moment, we must rely on more primitive guidelines. For example, my experience has been that algorithms are most predictable when they are "particular". By "particular" I mean that they place an emphasis on single elements of the overall game pattern. For example, in wargames, algorithms along the lines of "determine the closest enemy unit and fire at it" are particular and yield predictable behavior. I have found that the best algorithms consider the greatest amount of information in the broadest context. That is, they will factor into their decision-making the largest number of considerations rather than focus on a small number of particular elements. To continue with the example above, a better algorithm might be "determine the enemy unit posing the greatest combination of threat and vulnerability (based on range, activity, facing, range to other friendly units, cover, and sighting); fire on unit if probability of kill exceeds probability of being killed". How does one implement such principles into specific algorithms I doubt that any all purpose system. can ever be found. The best general solution I have found so far for this problem utilizes point systems, field analysis, and changes in the game structure. First, I establish a point system for quantifying the merit of each possible move. This is a timehonored technique for many artificial intelligence systems. A great deal of thought must go into the point system. The first problem with it is one of dynamic range: the designer must insure that the probability of two accessible moves each accumulating a point value equal to the maximum value allowed by the word size (eight bits) approaches zero. In other words, we can t have two moves each getting a score of 255 or we have no way of knowing which is truly the better move. This problem will diminish as 16-bit systems become more common. A second problem with the point system is the balancing of factors against each other. In our hypothetical tank game used above, we agree that climbing on top of a hill is good, but we also agree that moving onto a road is good. Which is better If a hilltop position is worth 15 points, what is a road position worth These questions are very difficult to answer. They require a deep familiarity with the play of the game. Unfortunately, such familiarity is impossible to attain with a game that has yet to be completed. The only alternative is broad experience, intimate knowledge of the situation being represented, painstaking analysis, and lots of experimenting. A second element of my general approach to artificial smarts is the use of field analysis. This is only applicable to games involving spatial relationships. In such games the human relies on pattern recognition to analyze positions and plan moves. True pattern recognition on the level of human effort is beyond the abilities of a microcomputer. However, something approaching pattern recognition can be attained through the use of field analysis. The key effort here is the creation of a calculable field quantity that correctly expresses the critical information needed by the computer to make a reasonable move. For example, in several of my wargames I have made use of safety and danger fields that tell a unit how much safety or danger it faces. Danger is calculated
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