Communicating sequential processes
Communicating sequential processes
Communicating and mobile systems: the &pgr;-calculus
Communicating and mobile systems: the &pgr;-calculus
Ant Colony Optimization
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Real-time agent characterization and prediction
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Bounded rationality via recursion
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Stigmergic reasoning over hierarchical task networks
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Swarming Polyagents Executing Hierarchical Task Networks
SASO '09 Proceedings of the 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Concurrent modeling of alternative worlds with polyagents
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
Generation and analysis of multiple futures with swarming agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A multi-environment multi-agent simulation framework for self-organizing systems
MABS'09 Proceedings of the 10th international conference on Multi-agent-based simulation
Between agents and mean fields
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
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Agents in a multi-agent system do not act in a vacuum. The outcome of their efforts depends on the environment in which they seek to act, and in particular on the efforts of other agents with whom they share the environment. We review previous efforts to address this problem, including active environments, concurrency modeling, recursive reasoning, and stochastic processes. Then we propose an approach that combines active environments and stochastic processes while addressing their limitations: a swarming agent simulation (which maintains transition probabilities dynamically, avoiding the static assumptions most convenient with traditional Markov models), applied concurrently to multiple perspectives (thus partitioning the active environment and addressing its scalability challenges). We demonstrate this method on a simple example.