Characters in games and virtual worlds continue to gain improvements in both their visual appearance and more human-like behaviours with each successive generation of hardware. One area that seemingly would need to be addressed if this evolution in human-like characters is to continue is in the area of characters with emotions. To begin addressing this, the thesis focuses on answering the question “Can an emotional architecture be developed for characters in games and virtual worlds, that is built upon a foundation of formal psychology? Therefore a primary goal of the research was to both review and consolidate a range of background material based on the psychology of emotions to provide a cohesive foundation on which to base any subsequent work. Once this review was completed, a range of supplemental material was investigated including computational models of emotions, current implementations of emotions in games and virtual worlds, machine learning techniques suitable for implementing aspects of emotions in characters in virtual world, believability and the role of emotions, and finally a discussion of interactive characters in the form of chat bots and non-player characters. With these reviews completed, a synthesis of the research resulted in the defining of an emotion architecture for use with pre-existing agent behaviour systems, and a range of evaluation techniques applicable to agents with emotions. To support validation of the proposed architecture three case studies were conducted that involved applying the architecture to three very different software platforms featuring agents. The first was applying the architecture to combat bots in Quake 3, the second to a chat bot in the virtual world Second Life, and the third was to a web chat bot used for e-commerce, specifically dealing with question and answers about the companies services. The three case studies were supported with several small pilot evaluations that were intended to look at different aspects of the implemented architecture including; (1) Whether or not users noticed the emotional enhancements. Which in the two small pilot studies conducted, highlighted that the addition of emotions to characters seemed to affect the user experience when the encounter was more interactive such as in the Second Life implementation. Where the interaction occurred in a combat situation with enemies with short life spans, the user experience seemed to be greatly reduced. (2) An evaluation was conducted on how the combat effectiveness of combat bots was affected by the addition of emotions, and in this pilot study it was found that the combat effectiveness was not quite statistically reduced, even when the bots were running away when afraid, or attacking when angry even if close to death. In summary, an architecture grounded in formal psychology is presented that is suitable for interactive characters in games and virtual worlds, but not perhaps ideal for applications where user interaction is brief such as in fast paced combat situations. This architecture has been partially validated through three case studies and includes suggestions for further work especially in the mapping of secondary emotions, the emotional significance of conversations, and the need to conduct further evaluations based on the pilot studies.

In this thesis an architecture, similar to subsumption architectures, is presented which uses low level behaviour modules, based on combinations of machine learning techniques, to create teams of autonomous agents cooperating via shared plans for interaction. The purpose of this is to perform effective single plan execution within multiple scenarios, using a modern team based first person shooter video game as the domain and visualiser. The main focus is showing that through basic machine learning mechanisms, applied in a multi-agent setting on sparse data, plans can be executed on game levels of varying size and shape without sacrificing team goals. It is also shown how different team members can perform locally sub-optimal operations which contribute to a globally better strategy by adding exploration data to the machine learning mechanisms. This contributes to the reinforcement learning problem of exploration versus exploitation, from a multi-agent perspective.

This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions played were recorded in order for other humans to replay. Both players and spectators of the game sessions left feedbacks and reports that could later be analyzed. The data collected from these experiments was then analyzed, and reflections were made on the entire project. Tips and ideas are proposed to developers of shooter games who are interested in making human-like artificial intelligence. Conclusions are made and extra information is provided in order to further iterate on this research. / Denna rapport undersöker möjligheterna att förfalska mänskligt beteende genom artificiell intelligens i datorspel, med hjälp av effektiva metoder som sparar värdefull utvecklingstid och som även skapar en rikare upplevelse för spelare. Den specifika implementationen av artificiell intelligens som utvecklas och diskuteras är ett neuralt nätverk som kontrollerar en finite-state machine. Målet var att efterlikna mänskligt beteende snarare än att simulera verklig intelligens. Ett 2D shooter-spel utvecklas och används för utförda experiment med mänskliga och artificiell intelligens-kontrollerade spelare. De sessioner som spelades under experimenten spelades in, för att sedan låta andra människor titta på inspelningarna. Både spelare och åskådare av spelsessionerna lämnade återkoppling och rapporter för senare analysering. Datan som samlats in från experimenten analyserades, och reflektioner utfördes på hela projektet. Tips och idéer presenteras till utvecklare av shooter-spel som är intresserade av en mer människolik artificiell intelligens. Slutsatser läggs fram och extra information presenteras för att kunna fortsätta iterera vidare på denna undersökning.