Reflective negotiating agents for real-time multisensor target tracking

Leen Kiat Soh, Costas Tsatsoulis

Research output: Contribution to journalConference articlepeer-review

53 Scopus citations


In this paper we describe a multiagent system in which agents negotiate to allocate resources and satisfy constraints in a real-time environment of multisensor target tracking. The agents attempt to optimize the use of their own consumable resources while adhering to the global goal, i.e., accurate and effective multisensor target tracking. Agents negotiate based on different strategies which are selected and instantiated using case-based reasoning (CBR). Agents are also fully reflective in that they are aware of all their resources including system-level ones such as CPU allocation, and this allows them to achieve real-time behavior. We focus our discussion on multisensor target racking, case-based negotiation, and real-time behavior, and present experimental results comparing our methodology to ones using either no negotiation or using a static negotiation protocol.

Original languageEnglish (US)
Pages (from-to)1121-1127
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2001
Externally publishedYes
Event17th International Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA, United States
Duration: Aug 4 2001Aug 10 2001

ASJC Scopus subject areas

  • Artificial Intelligence


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