14,164 research outputs found
Optimising Humanness: Designing the best human-like Bot for Unreal Tournament 2004
This paper presents multiple hybridizations of the two best
bots on the BotPrize 2014 competition, which sought for the best humanlike
bot playing the First Person Shooter game Unreal Tournament 2004.
To this aim the participants were evaluated using a Turing test in the
game. The work considers MirrorBot (the winner) and NizorBot (the
second) codes and combines them in two different approaches, aiming to
obtain a bot able to show the best behaviour overall. There is also an
evolutionary version on MirrorBot, which has been optimized by means
of a Genetic Algorithm. The new and the original bots have been tested
in a new, open, and public Turing test whose results show that the evolutionary
version of MirrorBot apparently improves the original bot, and
also that one of the novel approaches gets a good humanness level.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
Playing Smart - Artificial Intelligence in Computer Games
Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games
An evolutionary model with Turing machines
The development of a large non-coding fraction in eukaryotic DNA and the
phenomenon of the code-bloat in the field of evolutionary computations show a
striking similarity. This seems to suggest that (in the presence of mechanisms
of code growth) the evolution of a complex code can't be attained without
maintaining a large inactive fraction. To test this hypothesis we performed
computer simulations of an evolutionary toy model for Turing machines, studying
the relations among fitness and coding/non-coding ratio while varying mutation
and code growth rates. The results suggest that, in our model, having a large
reservoir of non-coding states constitutes a great (long term) evolutionary
advantage.Comment: 16 pages, 7 figure
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