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AAMAS 2016

Simulation Summarization (Extended Abstract)

Conference Paper Extended Abstracts Autonomous Agents and Multiagent Systems

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

Keywords

  • simulation summarization
  • causal states

Context

Venue
International Conference on Autonomous Agents and Multiagent Systems
Archive span
2002-2025
Indexed papers
7403
Paper id
382013568047465113