Human Terrain Data – What Should We Do With It?

What are we in the Modeling & Simulation (M&S) community to do with the volumes of \u27human terrain\u27 data now being published by the military and others in databases of the demographics and needs/values/norms of populations of interest? This paper suggests that the M&S community would be remiss if it did not rise to this challenge and suggest next steps for the use of this Human Terrain (HT) data resource. These datasets are a key asset for those interested in synthesis of two major agent-based modeling paradigms – the cognitive and the social – as this paper argues. We pursue this argument with a case study integrating a cognitive agent environment (PMFserv) and a social agent environment (FactionSim) and applying them to various regions of interest (Iraq, SE Asia, Crusades) to assess their validity and realism

Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv

This article focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of personality/cultural values and affect as well as biology/stress upon individual coping and group decision-making. The first section offers an assessment of the state of the practice and of the need to integrate valid human performance moderator functions (PMFs) from traditionally separated sub-fields of the behavioral literature. The second section pursues this goal by postulating a unifying architecture and principles for integrating existing PMF theories and models. It also illustrates a PMF testbed called PMFserv created for implementating and studying how PMFs may contribute to such an architecture. To date it interconnects versions of PMFs on physiology and stress (Janis-Mann, Gillis-Hursh, others); personality, cultural and emotive processes (Damasio, Cognitive Appraisal-OCC, value systems); perception (Gibsonian affordance); social processes (relations, identity, trust, nested intentionality); and cognition (affect- and stress-augmented decision theory, bounded rationality). The third section summarizes several usage case studies (asymmetric warfare, civil unrest, and political leaders) and concludes with lessons learned. Implementing and inter-operating this broad collection of PMFs helps to open the agenda for research on syntheses that can help the field reach a greater level of maturity. Part II presents a case study in using PMFserv for rapid scenario composability and realistic agent behavior

Challenges of Country Modeling with Databases, Newsfeeds, and Expert Surveys

According to expert practitioners and researchers in the field of human behavior modeling ([Silverman et al., 2002; Pew and Mavor, 1998; Ritter et al., 2003]), a common central challenge now confronting designers of HBM (human-behavior-modeling) applications is to increase the realism of the synthetic agents\u27 behavior and coping abilities. It is well accepted in the HBM (human-behavior-modeling) community that cognitively detailed, thick models are required to provide realism. These models require that synthetic agents be endowed with cognition and personality, physiology, and emotive components. (We will hereafter refer to these rich models as cognitively detailed models or thick agents. ) To make these models work, one must find ways to integrate scientific know-how from many disciplines, and to integrate concepts and insights from hitherto fragmented and partial models from the social sciences, particularly from psychology, cultural studies, and political science. One consequence of this kind of integration of multiple and heterogeneous concepts and models is that we frequently end up with a large feature space of parameters that then need to be filled in with data

Agar--an animal construction kit

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1989.Includes bibliographical references.by Michael D. TraversM.S

Computational mechanisms for action selection

Imagine a zebra in the African savannah. At each moment in time this zebra has to weigh up alternative courses of action before deciding which will be most beneficial to it. For instance, it may want to graze because it is short of food, or it may want to head towards a water hole because it is short of water, or it may want to remain motionless in order to avoid detection by the predator it can see lurking nearby. This is an example of the problem of action selection: how to choose, at each moment in time, the most appropriate out of a repertoire of possible actions. This thesis investigates action selection in a novel way and makes three main contribu¬ tions. Firstly, a description is given of a simulated environment which is an extensive and detailed simulation of the problem of action selection for animals. Secondly, this simulated environment is used to investigate the adequacy of several theories of ac¬ tion selection such as the drive model, Lorenz's hydraulic model and Maes' spreading activation network. Thirdly, a new approach to action selection is developed which determines the most appropriate action in a principled way, and which does not suffer from the inherent shortcomings found in other methods

Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

Perception in real and artificial insects: a robotic investigation of cricket phonotaxis

The aim of this thesis is to investigate a methodology for studying percep¬ tual systems by building artificial ones. It is proposed that useful results can be obtained from detailed robotic modelling of specific sensorimotor mechanisms in lower animals. By looking at the sensory control of behaviour in simple biological organisms, and in working robots, it is argued that proper appreciation of the physical interaction of the system with the environment and the task is essential for discovering how perceptual mechanisms function. Although links to biology, and concern with perceptual competence, are fields of growing interest in Artificial Intelligence, much of the current research fails to adequately address these issues, as the model systems being built do not represent real sensorimotor problems.By analyzing what is required for a model of a system to contribute to ex¬ plaining that system, a particular approach to modeling perceptual systems is suggested. This involves choosing an appropriate target system to model, building a system that validly represents the target with respect to a particular hypothesis, and properly evaluating the behaviour of the model system to draw conclusions about the target. The viability and potential contribution of this approach is demonstrated in the design, implementation and evaluation of a mobile robot model of a hypothesised mechanism for phonotaxis in the cricket.The result is a robot that successfully locates a specific sound source under a variety of conditions, with a range of behaviour that resembles the cricket in many ways. This provides some support for the hypothesis that the neural mechanism for phonotaxis in crickets does not involve separate processing for recognition and location of the signal, as is generally supposed. It also shows the importance of un¬ derstanding the physical interaction of the system's structure with its environment in devising and implementing perceptual systems. Both these results vindicate the proposed methodology