SOAR: an architecture for general intelligence
Artificial Intelligence
The use of hierarchies for action selection
Adaptive Behavior
The role of emotion in believable agents
Communications of the ACM
An environment for real-time urban simulation
I3D '95 Proceedings of the 1995 symposium on Interactive 3D graphics
The Belief-Desire-Intention Model of Agency
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
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ACM SIGGRAPH 2006 Papers
Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior
Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior
Cognitive Architectures: Where do we go from here?
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Extending the Soar Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
A unified cognitive architecture for physical agents
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Designing comprehensible agents
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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Robotics and Autonomous Systems
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WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Dynamic level of detail for large scale agent-based urban simulations
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
The animat contribution to cognitive systems research
Cognitive Systems Research
Cognitive architectures: Research issues and challenges
Cognitive Systems Research
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This paper addresses the issue of hybridization between reactive and cognitive approaches within a single decision-making architecture for virtual agent in an urban simulation. We use a reactive module in order to manage reactive behaviors and agent autonomy, and a cognitive module for anticipation, learning and complex behaviors management. The purpose of the cognitive module is to increase the agent's behavior credibility. The agent's reactive and proactive behaviors are sent to a decision module which is able to integrate, decompose, combine and select an action.