Predictive Regret-Matching for Cooperating Interceptors to Defeat an Advanced Threat

Arvind Rajagopalan, Duong Duc Nguyen, Jijoong Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Threats combining kinematic superiority, high-g maneuvering and evasive capabilities are in development. These advanced threats can reduce the survivability of high-value assets (HVAs). Here, we demonstrates that it is possible to defeat such an advanced threat with cheaper lower-performance interceptors using an alternative approach to traditional optimal control. These interceptors harness the knowledge of the forecasted regions that the threat can access, referred to as threat reachability. Applying reachability, the interceptors can be organized to block the passage of the threat to the HVAs as well as to defeat it. Here, we have developed a reachability calculator that is scalable to accommodate multiple interceptors and combined it with an on-line regret-matching learner derived from game theory to produce the self-organization and guidance for the interceptors. Numerical simulations are provided to demonstrate the validity of the resulting solution. Furthermore, some comparison is provided to benchmark our approach against a recently published differential game solution on the same scenarios. The comparison shows that our algorithm outperforms the optimal control solution.

Original languageEnglish
Title of host publicationAI 2019
Subtitle of host publicationAdvances in Artificial Intelligence - 32nd Australasian Joint Conference, 2019, Proceedings
EditorsJixue Liu, James Bailey
Place of PublicationCham, Switzerland
PublisherSpringer
Pages28-40
Number of pages13
ISBN (Electronic)978-3-030-35288-2
ISBN (Print)9783030352875
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event32nd Australasian Joint Conference on Artificial Intelligence, AI 2019 - Adelaide, Australia
Duration: 2 Dec 20195 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11919 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd Australasian Joint Conference on Artificial Intelligence, AI 2019
CountryAustralia
CityAdelaide
Period2/12/195/12/19

Keywords

  • Game theory
  • Reachability
  • Regret-matching
  • Team interception

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  • Cite this

    Rajagopalan, A., Nguyen, D. D., & Kim, J. (2019). Predictive Regret-Matching for Cooperating Interceptors to Defeat an Advanced Threat. In J. Liu, & J. Bailey (Eds.), AI 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, 2019, Proceedings (pp. 28-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11919 LNAI). Springer. https://doi.org/10.1007/978-3-030-35288-2_3