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Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human(More)
This paper addresses the problem of automatically constructing tracks tailor-made to maximize the enjoyment of individual players in a simple car racing game. To this end, some approaches to player mod-eling are investigated, and a method of using evolutionary algorithms to construct racing tracks is presented. A simple player-dependent metric of(More)
As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible(More)
This chapter surveys the research of us and others into applying evolutionary algorithms and other forms of computational intelligence to various aspects of racing games. We first discuss the various roles of computational intelligence in games, and then go on to describe the evolution of different types of car controllers, modelling of players' driving(More)
This paper explores the idea that it may be possible to combine two ideas - UAV flocking, and wireless cluster computing - in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric(More)
— The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several(More)
—Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper(More)
The SUAAVE project is funded by EPSRC under the WINES wireless networking initiative to consider issues of multiple aerial vehicles communicating and collaborating in performing tasks, and involves teams from University College London, University of Ulster, and the University of Oxford. The focus of SUAAVE lies in the creation and control of swarms of UAVs(More)
Geometric particle swarm optimization (GPSO) is a recently introduced formal generalization of traditional particle swarm optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In this paper we apply GPSO to the space of genetic programs represented as expression trees, uniting the paradigms of genetic programming and(More)
We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of(More)