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Thesis Abstract: Masters Thesis : Jenn Sandercock

Welcome to the abstract of my Masters thesis...

Using Adaptation and Goal Context to Automatically Generate Individual Personalities for Virtual Characters

Personality is a key component of characters that inhabit immersive virtual environments, such as games and virtual agent applications. In order to be distinguishable from other characters in the environment, each character should have its own personality in the form of different observable behaviour, not solely in its physical appearance or animation. Previous work in this field has mostly relied on time-consuming, handcrafted characters and static, trait-based approaches to personality. Our goal is a method to develop complex, individual personalities without handcrafting every behaviour. Unlike most implemented versions of personality theories, cognitive-social theories of personality address how personality is developed and adapts throughout childhood and over our lifetimes. Cognitive-social theories also emphasise the importance of situations in determining how we behave. From this basis, we believe that personality should be individual, adaptive, and based on context. Characters in current state-of-the-art games and virtual environments do not demonstrate all of these features without extensive handcrafting.

We propose a model where personality influences both decision-making and evaluation of reward. Characters use their past experiences in the form of simple somatic markers, or gut-instinct, to make decisions; and determine rewards based on their own personal goals, rather than via external feedback. We evaluated the model by implementation of a simple game and tested it using quantitative criteria, including a purpose-designed individuality measure. Results indicate that, although characters are given the same initial personality template, it is possible to develop different personalities (in the form of behaviour) based on their unique experiences in the environment and relationships with other characters. This work shows a way forward for more automated development of personalities that are individual, context-aware and adapt to users and the environment.

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