- author = "Julian Togelius",
- title = "Optimization, Imitation and Innovation: Computational Intelligence and Games",
- school = "Department of Computer Science, University of Essex",
- year = "2007",
- address = "UK",
- month = sep,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://julian.togelius.com/thesis.pdf",
- size = "202 pages",
-
abstract = "This thesis concerns the application of computational
intelligence techniques, mainly neural networks and
evolutionary computation, to computer games. This
research has three parallel and non-exclusive goals: to
develop ways of testing machine learning algorithms, to
augment the entertainment value of computer games, and
to study the conditions under which complex general
intelligence can evolve. Each of these goals is
discussed at some length, and the research described is
also discussed in the light of current open questions
in computational intelligence in general and
evolutionary robotics in particular.
A number of experiments are presented, divided into three chapters: optimisation, imitation and innovation. The experiments in the optimization chapter deals with optimising certain aspects of computer games using unambiguous fitness measures and evolutionary algorithms or other reinforcement learning algorithms. In the imitation chapter, supervised learning techniques are used to imitate aspects of behaviour or dynamics. Finally, the innovation chapter provides examples of using evolutionary algorithms not as pure optimisers, but rather as innovating new behaviour or structures using complex, nontrivial fitness measures.
Most of the experiments in this thesis are performed in one of two games based on a simple car racing simulator, and one of the experiments extends this simulator to the control of a real world radio-controlled model car. The other games that are used as experimental environments are a helicopter simulation game and the multi-agent foraging game Cellz.
Among the main achievements of the thesis are a method for personalised content creation based on modelling player behaviour and evolving new game content (such as racing tracks), a method for evolving control for non-recoverable robots (such as racing cars) using multiple models, and a method for multi-population competitive co-evolution.",
