| Chapter title |
Summarizing Simulation Results Using Causally-Relevant States
|
|---|---|
| Chapter number | 6 |
| Book title |
Autonomous Agents and Multiagent Systems
|
| Published in |
Lecture notes in computer science, September 2016
|
| DOI | 10.1007/978-3-319-46840-2_6 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-946839-6, 978-3-31-946840-2
|
| Authors |
Nidhi Parikh, Madhav Marathe, Samarth Swarup |
| Editors |
Nardine Osman, Carles Sierra |
| Abstract |
As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor > Associate Professor | 1 | 50% |
| Student > Bachelor | 1 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 1 | 50% |
| Social Sciences | 1 | 50% |