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Subject: The Art of Computer Game Design- Chapter 6
Date: Sat, 15 Jan 2005 13:07:09 +0800
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G>
<UL>
<DIV align=3Dright>
<P><A name=3Dtop></A><FONT size=3D+3>Design Techniques and=20
Ideals<BR></FONT><B>Chapter Six</B></P></DIV></UL>
<P>
<HR width=3D"100%">
<P></P>
<CENTER>
<TABLE cellPadding=3D10>
<TBODY>
<TR vAlign=3Dtop align=3Dleft>
<TD width=3D"50%"><B><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
BALANCING SOLITAIRE">BALANCING=20
SOLITAIRE GAMES<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Vast">Vast=20
Resources<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Artificial">Artificial=20
Smarts<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Conclusions on Artificial">Conclusions=20
on Artificial Smarts<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Limited">Limited=20
Information<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Summary">Summary</A></B>=20
<P><B><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
RELATIONSHIPS BETWEEN">RELATIONSHIPS=20
BETWEEN OPPONENTS</A></B></P></TD>
<TD width=3D"50%"><B><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Symmetric">Symmetric=20
Relationships<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Asymmetric">Asymmetric=20
Games<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Triangularity">Triangularity<BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
Actors and Indirect">Actors=20
and Indirect Relationships<BR><BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
SMOOTH LEARNING">SMOOTH=20
LEARNING CURVES<BR><BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
THE ILLUSION OF">THE=20
ILLUSION OF WINNABILITY<BR><BR></A><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
SUMMARY">SUMMARY</A></B></TD></TR></TBODY></TABLE></CENTER>
<UL>
<P><B><FONT size=3D+2>E</FONT></B>very artist develops her own special =
techniques and ideals for the execution of her art. The painter =
worries about=20
brush strokes, mixing of paint, and texture; the musical composer =
learns=20
techniques of orchestration, timing, and counterpoint. The game =
designer also=20
acquires a variety of specialized skills, techniques, and ideals for =
the=20
execution of her craft. In this chapter I will describe some of the =
techniques=20
that I use. <FONT size=3D-1><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
top">Top</A>=20
</FONT></P>
<P><A name=3D"BALANCING SOLITAIRE"></A><B>BALANCING SOLITAIRE =
GAMES</B></P>
<P>A solitaire game pits the human player against the computer. The =
computer=20
and the human are very different creatures; where human thought =
processes are=20
diffuse, associative, and integrated, the machine=92s thought =
processes are=20
direct, linear, and arithmetic. This creates a problem. A computer =
game is=20
created for the benefit of the human, and therefore is cast in the=20
intellectual territory of the human, not that of the computer. This =
puts the=20
computer at a natural disadvantage. Although the computer could easily =
whip=20
the human in games involving computation, sorting, or similar =
functions, such=20
games would be of little interest to the human player. The computer =
must play=20
on the human=92s home turf, something it does with great difficulty. =
How do we=20
design the game to challenge the human? Four techniques are available: =
vast=20
resources, artificial smarts, limited information, and pace. <FONT =
size=3D-1><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
top">Top</A>=20
</FONT></P>
<P><A name=3DVast></A><B>Vast Resources</B></P>
<P>This is by far the most heavily used technique for balancing a =
game. The=20
computer is provided with immense resources that it uses stupidly. =
These=20
resources may consist of large numbers of opponents that operate with =
a=20
rudimentary intelligence. Many games use this ploy: SPACE INVADERS, =
MISSILE=20
COMMAND, ASTEROIDS, CENTIPEDE, and TEMPEST are some of the more =
popular games=20
to use this technique. It is also possible to equip the computer with =
a small=20
number of opponents that are themselves more powerful than the human =
player=92s=20
units, such as the supertanks in BATTLEZONE. The effect in both cases =
is the=20
same: the human player=92s advantage in intelligence is offset by the =
computer=92s=20
material advantages.</P>
<P>This approach has two benefits. First, it gives the conflict =
between the=20
human and the computer a David versus Goliath air. Most people would =
rather=20
win as apparent underdog than as equal. Second, this approach is the =
easiest=20
to implement. Providing artificial intelligence for the computer=92s =
players can=20
be difficult, but repeating a process for many computer players takes =
little=20
more than a simple loop. Of course, the ease of implementing this =
solution=20
carries a disadvantage: everybody else does it. We are knee-deep in =
such=20
games! Laziness and lack of determination have far more to do with the =
prevalence of this technique than game design considerations. <FONT =
size=3D-1><A=20
=
href=3D"http://www.vancouver.wsu.edu/fac/peabody/game-book/Chapter6.html#=
top">Top</A>=20
</FONT></P>
<P><A name=3DArtificial></A><B>Artificial Smarts</B></P>
<P>The obvious alternative to the use of sheer numbers is to provide =
the=20
computer player with intelligence adequate to meet the human on equal =
terms.=20
Unfortunately, artificial intelligence techniques are not well enough=20
developed to be useful here. Tree-searching techniques have been =
developed far=20
enough to allow us to produce passable chess, checkers, and Othello =
players.=20
Any other game that can be expressed in direct tree-searching terms =
can be=20
handled with these techniques. Unfortunately, very few games are =
appropriate=20
for this treatment.</P>
<P>An alternative is to develop ad-hoc artificial intelligence =
routines for=20
each game. Since such routines are too primitive to be referred to as=20
"artificial intelligence", I instead use the less grandiose term =
"artificial=20
smarts". This is the method I have used in TANKTICS, EASTERN FRONT =
1941, and=20
LEGIONNAIRE, with varying degrees of success. This strategy demands =
great=20
effort from the game designer, for such ad-hoc routines must be =
reasonable yet=20
unpredictable.</P>
<P>Our first requirement of any artificial smarts system is that it =
produce=20
reasonable behavior. The computer should not drive its tanks over =
cliffs,=20
crash spaceships into each other, or pause to rest directly in front =
of the=20
human=92s guns. In other words, obviously stupid moves must not be =
allowed by any=20
artificial smarts system. This requirement tempts us to list all =
possible=20
stupid moves and write code that tests for each such stupid move and =
precludes=20
it. This is the wrong way to handle the problem, for the computer can=20
demonstrate unanticipated creativity in the stupidity of its mistakes. =
A=20
better (but more difficult) method is to create a more general =
algorithm that=20
obviates most absurd moves.</P>
<P>A second requirement of an artificial smarts routine is =
unpredictability.=20
The human should never be able to second-guess the behavior of the =
computer,=20
for this would shatter the illusion of intelligence and make victory =
much=20
easier. This is may seem to contradict the first requirement of =
reasonable=20
behavior, for reasonable behavior follows patterns that should be =
predictable.=20
The apparent contradiction can be resolved through a deeper =
understanding of=20
the nature of interaction in a game. Three realizations must be =
combined to=20
arrive at this deeper understanding. First, reaction to an opponent is =
in some=20
ways a reflection of that opponent. A reasonable player tries to =
anticipate=20
his opponent=92s moves by assessing his opponent=92s personality. =
Second,=20
interactiveness is a mutual reaction---both players attempt to =
anticipate each=20
other=92s moves. Third, this interactiveness is itself a measure of =
"gaminess".=20
We can combine these three realizations in an analogy. A game becomes=20
analogous to two mirrors aligned towards each other, with each player =
looking=20
out from one mirror. A puzzle is analogous to the two mirrors being=20
unreflective; the player sees a static, unresponsive image. A weakly=20
interactive game is analogous to the two mirrors being weakly =
reflective; each=20
player can see and interact at one or two levels of reflection. A =
perfectly=20
interactive game (the "gamiest game") is analogous to the two mirrors =
being=20
perfectly reflective; each of the two players recursively exchanges =
places in=20
an endless tunnel of reflected anticipation=92s. No matter how =
reasonable the=20
behavior, the infinitely complex pattern of anticipation and=20
counter-anticipation defies prediction. It is reasonable yet=20
unpredictable.</P>
<P>Unfortunately, a perfectly interactive game is beyond the reach of=20
microcomputers, for if the computer is to anticipate human moves=20
interactively, it must be able to assess the personality of its =
opponents---a=20
hopeless task as yet. For the moment, we must rely on more primitive=20
guidelines. For example, my experience has been that algorithms are =
most=20
predictable when they are "particular". By "particular" I mean that =
they place=20
an emphasis on single elements of the overall game pattern. For =
example, in=20
wargames, algorithms along the lines of "determine the closest enemy =
unit and=20
fire at it" are particular and yield predictable behavior.</P>
<P>I have found that the best algorithms consider the greatest amount =
of=20
information in the broadest context. That is, they will factor into =
their=20
decision-making the largest number of considerations rather than focus =
on a=20
small number of particular elements. To continue with the example =
above, a=20
better algorithm might be "determine the enemy unit posing the =
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