TrauMAP - Integrating Anatomical and Physiological Simulation (Dissertation Proposal)

In trauma, many injuries impact anatomical structures, which may in turn affect physiological processes - not only those processes within the structure, but also ones occurring in physical proximity to them. Our goal with this research is to model mechanical interactions of different body systems and their impingement on underlying physiological processes. We are particularly concerned with pathological situations in which body system functions that normally do not interact become dependent as a result of mechanical behavior. Towards that end, the proposed TRAUMAP system (Trauma Modeling of Anatomy and Physiology) consists of three modules: (1) a hypothesis generator for suggesting possible structural changes that result from the direct injuries sustained; (2) an information source for responding to operator querying about anatomical structures, physiological processes, and pathophysiological processes; and (3) a continuous system simulator for simulating and illustrating anatomical and physiological changes in three dimensions. Models that can capture such changes may serve as an infrastructure for more detailed modeling and benefit surgical planning, surgical training, and general medical education, enabling students to visualize better, in an interactive environment, certain basic anatomical and physiological dependencies

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

A RULE-BASED APPROACH TO ANIMATING MULTI-AGENT ENVIRONMENTS

This dissertation describes ESCAPE (Expert Systems in Computer Animation Production Environments), a multi-agent animation system for building domain-oriented, rule-based visual programming environments. Much recent work in computer graphics has been concerned with producing behavioural animations of artificial life-forms mainly based on algorithmic approaches. This research indicates how, by adding an inference engine and rules that describe such behaviour, traditional computer animation environments can be enhanced. The comparison between using algorithmic approaches and using a rule-based approach for representing multi-agent worlds is not based upon their respective claims to completeness, but rather on the ease with which end users may express their knowledge and control their animations with a minimum of technical knowledge. An environment for the design of computer animations incorporating an expert system approach is described. In addition to direct manipulation of objects on the screen, the environment allows users to describe behavioural rules based upon both the physical and non-physical attributes of objects. These rules can be interpreted to suggest the transition from stage to stage or to automatically produce a longer animation. The output from the system can be integrated into a commercially available 3D modelling and rendering package. Experience indicates that a hybrid environment, mixing algorithmic and rule-based approaches, would be very promising and offer benefits in application areas such as creating realistic background scenes and modelling human beings or animals either singly or in groups. A prototype evaluation system and three different domains are described and illustrated with preliminary animated images

Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar