AAMAS 2016
Simulation Summarization (Extended Abstract)
Abstract
As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary for concisely summarizing the results of a simulation run. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant states from the trajectories of the agents. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which have significant impact on the final outcome of interest. We apply it to a complex simulation of a major disaster in an urban area and present results.
Authors
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Context
- Venue
- International Conference on Autonomous Agents and Multiagent Systems
- Archive span
- 2002-2025
- Indexed papers
- 7403
- Paper id
- 382013568047465113