Abstract-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 player. This requires a way to create accurate models of players' driving styles, as well as a tentative definition of when a racing track is fun, both of which are provided. We believe this approach opens up interesting new research questions and is potentially applicable to commercial racing games.Keywords: Car racing, player modelling, entertainment metrics, content creation, evolution. I. THREE APPROACHES TO COMPUTATIONAL INTELLIGENCE IN GAMESMuch of the research done under the heading "computational intelligence and games" aims to optimise game playing strategies or game agent controllers. While these endeavours are certainly worthwhile, there are several other quite different approaches that could be at least as interesting, from both an academic and a commercial point of view.In this paper we discuss three approaches to computational intelligence in games: optimisation, imitation and innovation. We describe these approaches as they apply to games in general and exemplify them as they apply to racing games in particular. We then describe an experiment where these approaches are used in a racing game to augment player satisfaction. The taxonomy given below is of course neither final nor exhaustive, but it is a start. A. The optimisation approachMost research into computational intelligence and games takes the optimisation approach, which means that an optimisation algorithm is used to tune values of some aspect of the game. Examples abound of using evolutionary computation to develop good game-playing strategies, in all sorts of games from chess to poker to warcraftSeveral groups of researchers have taken this approach towards racing games. Tanev [3] developed an anticipatory control algorithm for an R/C racing simulator, and used evolutionary computation to tune the parameters of this algorithm for optimal lap time. Chaperot and Fyfe [4] evolved neural network controllers for minimal lap time in a 3D motocross game, and we previously ourselves investigated which controller architectures are best suited for such optimisation in a simple racing game [5]. Sometimes optimisation is multiobjective, as in our previous work on optimising controllers for performance on particular racing tracks versus robustness in driving on new tracks [6]. And there are other things than controllers that can be optimised in car racing, as is demonstrated by the work of Wloch and Bentley, who optimised the parameters for simulated Formula 1 cars in a physically sophisticated racing game [7].While games can be excellent test-beds for evolutionary and other optimisation algorithms, it can be argued that improving game-playing agents is in itself of little practical value...

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 tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the on-board communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in Least Square (LS) algorithms which are widely used in RSS based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter for distance estimation. Furthermore, the constantly changing path loss factor during UAV flight can also affect the accuracy of estimation. In order to overcome this challenge, we present an adaptive algorithm to estimate the path loss factor. Given the position and velocity information, if a collision is detected we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

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 helicopters is described. INTRODUCTIONAt one time or another we have all been impressed by the sheer agility of a flock of starlings flying in a city square at dusk -wheeling and manoeuvring so swiftly and precisely as to create the illusion of a single and very superior controlling intelligence. Artificial flock and swarm systems exploiting these abilities, which thanks to the seminal work of Craig Reynolds [1] are now well understood, have been the focus of active research for almost twenty years. But there is another way of looking at a flock of starlings: a typical flock will contain upwards of a thousand birds, and each bird will contain a gram or so of brain tissue, so in the aggregate the flock will contain about the same amount as a single human brain. If there were some way in which the starlings' brains could be linked together to form one human-sized nervous system, could the flock collectively achieve something approaching a human level of intelligence?Of course, the knowledge that no such linkage is possible instantly takes the steam out of such a speculation (although some biologists have proposed analogies between ants and neurons, suggesting for example that the chemically-mediated interactions between individual ants in a colony and the chemically mediated interactions between individual neurons in a brain may support intelligent behavior in ways that are somehow similar). However, the constraints on the effective linkage of computational components clearly do not apply to artificial systems: the recent convergence between computation and communication means that distributed processing, in the form of cluster computing, is becoming the norm for highperformance computing. (In the latest top 500 supercomputer rankings, 58% are cluster machines.) In these clusters, large numbers of relatively low-powered computers are linked into a single architecture using highbandwidth (1 or 2 Gigabits/sec) wired network connections. Might it be possible to construct a flock of individually simple artificial agents that flew like a flock of starlings, but was also able to process information like a cluster-based supercomputer by using high-bandwidth wireless links between the agents' computational elements?We have named the general concept of combining swarm intelligence and wireless cluster computing the UltraSwarm. Although the genesis of the idea occurred in the context of flocking systems, the basic philosophy could also apply to swarm intelligence systems based on social insect behaviour. In both flock-based and social-insectbased UltraSwarms, the attraction lies in the potential for combining the two technologies of swarm intelligence and conventional c...

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 network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so.

Abstract. 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 styles, evolution of racing tracks, comparisons of evolution with other forms of reinforcement learning, and modelling and controlling physical cars. It is suggested that computational intelligence can be used in different but complementary ways in racing games, and that there is unrealised potential for cross-fertilisation between research in evolutionary robotics and CI for games.