Adapting In-Game Agent Behavior by Observation of Players Using Learning Behavior Trees

In this paper we describe Learning Behavior Trees, an extension of the popular game AI scripting technique. Behavior Trees provide an effective way for expert designers to describe complex, in-game agent behaviors. Scripted AI captures human intuition about the structure of behavioral decisions, but suffers from brittleness and lack of the natural variation seen in human players. Learning Behavior Trees are designed by a human designer, but then are trained by observation of players performing the same role, to introduce human-like variation to the decision structure. We show that, using this model, a single hand-designed Behavior Tree can cover a wide variety of player behavior variations in a simplified Massively Multiplayer Online Role-Playing Game

Game engines selection framework for high-fidelity serious applications

Serious games represent the state-of-the-art in the convergence of electronic gaming technologies with instructional design principles and pedagogies. Despite the value of high-fidelity content in engaging learners and providing realistic training environments, building games which deliver high levels of visual and functional realism is a complex, time consuming and expensive process. Therefore, commercial game engines, which provide a development environment and resources to more rapidly create high-fidelity virtual worlds, are increasingly used for serious as well as for entertainment applications. Towards this intention, the authors propose a new framework for the selection of game engines for serious applications and sets out five elements for analysis of engines in order to create a benchmarking approach to the validation of game engine selection. Selection criteria for game engines and the choice of platform for Serious Games are substantially different from entertainment games, as Serious Games have very different objectives, emphases and technical requirements. In particular, the convergence of training simulators with serious games, made possible by increasing hardware rendering capacity is enabling the creation of high-fidelity serious games, which challenge existing instructional approaches. This paper overviews several game engines that are suitable for high-fidelity serious games, using the proposed framework

A Crisis in Physics Education: Games to the Rescue!

An education in Physics develops both strong cognitive and practical skills. These are well-matched to the needs of employers, from engineering to banking. Physics provides the foundation for all engineering and scientific disciplines including computing technologies, aerospace, communication, and also biosciences and medicine. In academe, Physics addresses fundamental questions about the universe, the nature of reality, and of the complex socio-economic systems comprising our daily lives. Yet today, there are emerging concerns about Physics education: Secondary school interest in Physics is falling, as is the number of Physics school teachers. There is clearly a crisis in physics education; recent research has identified principal factors. Starting from a review of these factors, and from recommendations of professional bodies, this paper proposes a novel solution – the use of Computer Games to teach physics to school children, to university undergraduates and to teacher-trainees

LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC.

This Lab-Directed Research and Development (LDRD) sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed path-planning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables 'one-click' layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based force-on-force (FOF) simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication

Integrating social power into the decision-making of cognitive agents

AbstractSocial power is a pervasive feature with acknowledged impact in a multitude of social processes. However, despite its importance, common approaches to social power interactions in multi-agent systems are rather simplistic and lack a full comprehensive view of the processes involved. In this work, we integrated a comprehensive model of social power dynamics into a cognitive agent architecture based on an operationalization of different bases of social power inspired by theoretical background research in social psychology. The model was implemented in an agent framework that was subsequently used to generate the behavior of virtual characters in an interactive virtual environment. We performed a user study to assess users' perceptions of the agents and found evidence supporting both the social power capabilities provided by the model and their value for the creation of believable and interesting scenarios. We expect that these advances and the collected evidence can be used to support the development of agent systems with an enriched capacity for social agent simulation

The Lived Experience of Virtual Environments: A Phenomenological Study

This is study of the experience of virtual environments (VE) in the context of safety training. Research involves participants from two companies who use VEs for safety training in hazardous work environments. The research approach is phenomenology. The key findings of the study show how the users actively form the VE experience. These insights will be useful for VE research and development

Methods for engineering symbolic human behaviour models for activity recognition

This work investigates the ability of symbolic models to encode context information that is later used for generating probabilistic models for activity recognition. The contributions of the work are as follows: it shows that it is possible to successfully use symbolic models for activity recognition; it provides a modelling toolkit that contains patterns for reducing the model complexity; it proposes a structured development process for building and evaluating computational causal behaviour models

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Empirical Game-Theoretic Methods for Strategy Design and Analysis in Complex Games.

Complex multi-agent systems often are not amenable to standard game-theoretic analysis. I study methods for strategic reasoning that scale to more complex interactions, drawing on computational and empirical techniques. Several recent studies have applied simulation to estimate game models, using a methodology known as empirical game-theoretic analysis. I report a successful application of this methodology to the Trading Agent Competition Supply Chain Management game. Game theory has previously played little—if any—role in analyzing this scenario, or others like it. In the rest of the thesis, I perform broader evaluations of empirical game analysis methods using a novel experimental framework. I introduce meta-games to model situations where players make strategy choices based on estimated game models. Each player chooses a meta-strategy, which is a general method for strategy selection that can be applied to a class of games. These meta-strategies can be used to select strategies based on empirical models, such as an estimated payoff matrix. I investigate candidate meta-strategies experimentally, testing them across different classes of games and observation models to identify general performance patterns. For example, I show that the strategy choices made using a naive equilibrium model quickly degrade in quality as observation noise is introduced. I analyze three families of meta-strategies that predict distributions of play, each interpolating between uninformed and naive equilibrium predictions using a single parameter. These strategy spaces improve on the naive method, capturing (to some degree) the effects of observation uncertainty. Of these candidates, I identify logit equilibrium as the champion, supported by considerable evidence that its predictions generalize across many contexts. I also evaluate exploration policies for directing game simulations on two tasks: equilibrium confirmation and strategy selection. Policies based on computing best responses are able to exploit a variety of structural properties to confirm equilibria with limited payoff evidence. A novel policy I propose—subgame best-response dynamics—improves previous methods for this task by confirming mixed equilibria in addition to pure equilibria. I apply meta-strategy analysis to show that these exploration policies can improve the strategy selections of logit equilibrium.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61590/1/ckiekint_1.pd