Human behavior models for agents in simulators and games: part I: enabling science with PMFserv
Presence: Teleoperators and Virtual Environments
Modeling factions for "effects based operations": part I--leaders and followers
Computational & Mathematical Organization Theory
The Behavior Markup Language: Recent Developments and Challenges
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Requirements analysis of presence: Insights from a RPG game
Computers in Entertainment (CIE) - SPECIAL ISSUE: Media Arts and Games
A platform-independent metamodel for multiagent systems
Autonomous Agents and Multi-Agent Systems
From KISS to KIDS: an 'anti-simplistic' modelling approach
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Enabling generative, emergent artificial culture
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Demand is on the rise for scientifically based human-behavior models that can be quickly customized and inserted into immersive training environments to recreate a given society or culture. At the same time, there are no readily available science model-driven environments for this purpose (see survey in Sect. 2). In researching how to overcome this obstacle, we have created rich (complex) socio-cognitive agents that include a large number of social science models (cognitive, sociologic, economic, political, etc) needed to enhance the realism of immersive, artificial agent societies. We describe current efforts to apply model-driven development concepts and how to permit other models to be plugged in should a developer prefer them instead. The current, default library of behavioral models is a metamodel, or authoring language, capable of generating immersive social worlds. Section 3 explores the specific metamodels currently in this library (cognitive, socio-political, economic, conversational, etc.) and Sect. 4 illustrates them with an implementation that results in a virtual Afghan village as a platform-independent model. This is instantiated into a server that then works across a bridge to control the agents in an immersive, platform-specific 3D gameworld (client). Section 4 also provides examples of interacting in the resulting gameworld and some of the training a player receives. We end with lessons learned and next steps for improving both the process and the gameworld. The seeming paradox of this research is that as agent complexity increases, the easier it becomes for the agents to explain their world, their dilemmas, and their social networks to a player or trainee.