Belief-desire-intention agent architectures
Foundations of distributed artificial intelligence
Nonlinear time series analysis
Nonlinear time series analysis
Ant-like missionaries and cannibals: synthetic pheromones for distributed motion control
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Linguistic geometry: from search to construction
Linguistic geometry: from search to construction
Evolving adaptive pheromone path planning mechanisms
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Digital pheromone mechanisms for coordination of unmanned vehicles
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
A preliminary taxonomy of multi-agent interactions
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Artificial War: Multiagent-Based Simulation of Combat
Artificial War: Multiagent-Based Simulation of Combat
Modeling uncertain domains with polyagents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Representing dispositions and emotions in simulated combat
DAMAS'05 Proceedings of the 2005 international conference on Defence Applications of Multi-Agent Systems
Digital pheromones for coordination of unmanned vehicles
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
E Pluribus Unum: Polyagent and Delegate MAS Architectures
Multi-Agent-Based Simulation VIII
Prediction Horizons in Agent Models
Engineering Environment-Mediated Multi-Agent Systems
Agent interaction, multiple perspectives, and swarming simulation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Pheromones, probabilities, and multiple futures
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
Interpreting digital pheromones as probability fields
Winter Simulation Conference
Between agents and mean fields
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
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Reasoning about agents that we observe in the world is challenging. Our available information is often limited to observations of the agent's external behavior in the past and present. To understand these actions, we need to deduce the agent's internal state, which includes not only rational elements (such as intentions and plans), but also emotive ones (such as fear). In addition, we often want to predict the agent's future actions, which are constrained not only by these inward characteristics, but also by the dynamics of the agent's interaction with its environment. BEE (Behavior Evolution and Extrapolation) uses a faster-than-real-time agent-based model of the environment to characterize agents' internal state by evolution against observed behavior, and then predict their future behavior, taking into account the dynamics of their interaction with the environment.