What is a Good Pattern of Life Model? Guidance for Simulations

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each

Common Crowd Dynamics: Shaping Behavioral Intention Models

As the human population grows, so too does the need to understand human behavior. One particularly important aspect of human behavior is how it changes within conglomerations of people, i.e. crowds. In this thesis, a method for modeling crowd behavior is proposed. This method draws inspiration from the concept of behavioral intention and the related forces of attitudes, influences, and social norms. These topics are first defined and detailed, followed by a survey of related research. Next, the model is presented and adapted to three common crowd dynamics, each stressing a different component of behavioral intention. Observations are made about these models, and extensions to the models and directions for future research are considered

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