Although evolution has proven to be
a powerful search method for discovering effective behavior
for sequential decision-making problems,
it seems unlikely that evolving for raw performance could
result in behavior that is distinctly humanlike.
This chapter demonstrates how humanlike behavior can be evolved
by restricting a bot's actions in a way consistent with human
limitations and predilections. This approach evolves good
behavior, but assures that it is consistent with how humans
behave. The approach is demonstrated in the UT^2
bot for the commercial first-person shooter videogame
Unreal Tournament 2004. UT^2's humanlike qualities
allowed it to take 2nd place in
BotPrize 2010, a competition to develop humanlike bots
for Unreal Tournament 2004.
This chapter analyzes UT^2, explains how it
achieved its current level of humanness, and discusses
insights gained from the competition results that
should lead to improved humanlike bot performance in
future competitions and in videogames in general.
[Though this chapter was published later, an earlier paper describes how this bot was improved for later competitions: paper