Picture of server farm and IT infrastructure

Where technology & law meet: Open Access research on data security & its regulation ...

Strathprints makes available Open Access scholarly outputs exploring both the technical aspects of computer security, but also the regulation of existing or emerging technologies. A research specialism of the Department of Computer & Information Sciences (CIS) is computer security. Researchers explore issues surrounding web intrusion detection techniques, malware characteristics, textual steganography and trusted systems. Digital forensics and cyber crime are also a focus.

Meanwhile, the School of Law and its Centre for Internet Law & Policy undertake studies on Internet governance. An important component of this work is consideration of privacy and data protection questions and the increasing focus on cybercrime and 'cyberterrorism'.

Explore the Open Access research by CIS on computer security or the School of Law's work on law, technology and regulation. Or explore all of Strathclyde's Open Access research...

An evolutionary approach for interactive computer games

Yannakakis, G. N. and Levine, J. and Hallam, J. (2004) An evolutionary approach for interactive computer games. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC 04). UNSPECIFIED.

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

In this paper we introduce the first stage of experiments on neuro-evolution mechanisms applied to predator/prey multi-character computer games. Our test-bed is a computer game where the prey (i.e. player) has to avoid its predators by escaping through an exit without getting killed. By viewing the game from the predators’ (i.e. opponents’) perspective, we attempt off-line to evolve neural-controlled opponents capable of playing effectively against computer-guided fixed strategy players. Their efficiency is based on cooperation which emerges from an abstract type of partial interaction with their environment. In addition, investigation of behavior generalization demonstrated the crucial contribution of playing strategies in the development of successful predator behaviors. However, emergent well-behaved opponents trained off-line with fixed strategies do not make the game interesting to play. We therefore present an evolutionary mechanism for opponents that keep learning from a player while playing against it (i.e. on-line) and we demonstrate its efficiency and robustness in increasing the predators’ performance while altering their behavior as long as the game is played. Computer game opponents following this on-line learning approach show high adaptability to changing player strategies, which provides evidence for the approach’s effectiveness and interest against human players.