Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
AI Game Programming Wisdom
AI Game Programming Wisdom
Personality Parameters and Programs
Creating Personalities for Synthetic Actors, Towards Autonomous Personality Agents
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Adaptive game AI with dynamic scripting
Machine Learning
Human behavior models for agents in simulators and games: part II: gamebot engineering with PMFserv
Presence: Teleoperators and Virtual Environments
Flow in games (and everything else)
Communications of the ACM
DEAL: dialogue management in SCXML for believable game characters
Future Play '07 Proceedings of the 2007 conference on Future Play
2APL: a practical agent programming language
Autonomous Agents and Multi-Agent Systems
OperettA: a prototype tool for the design, analysis and development of multi-agent organizations
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
The DEFACTO system: training tool for incident commanders
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Adaptive Serious Games Using Agent Organizations
Agents for Games and Simulations
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The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals.