Learning the Behavior of a Dynamical System Via a “20 Questions” Approach

Authors

  • Abhijin Adiga Virginia Tech
  • Chris Kuhlman Virginia Tech
  • Madhav Marathe Virginia Tech
  • Ravi S. S. Virginia Tech
  • Daniel Rosenkrantz University at Albany – SUNY
  • Richard Stearns University at Albany – SUNY

Keywords:

Graphical dynamical systems, threshold and symmetric functions, inference, batch and adaptive query modes

Abstract

Using a graphical discrete dynamical system to model a networked social system, the problem of inferring the behavior of the system can be formulated as the problem of learning the local functions of the dynamical system. We investigate the problem assuming an active form of interaction with the system through queries. We consider two classes of local functions (namely, symmetric and threshold functions) and two interaction modes, namely batch mode (where all the queries must be submitted together) and adaptive mode (where the set of queries submitted at a stage may rely on the answers received to previous queries). We develop complexity results and efficient heuristics that produce query sets under both query modes. We demonstrate the performance of our heuristics through experiments on over 20 well-known networks.

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Published

2018-04-26

How to Cite

Adiga, A., Kuhlman, C., Marathe, M., S. S., R., Rosenkrantz, D., & Stearns, R. (2018). Learning the Behavior of a Dynamical System Via a “20 Questions” Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11588

Issue

Section

AAAI Technical Track: Multiagent Systems