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EVENT TRACKING IN A DYNAMIC MULTIAGENT ENVIRONMENT
Author(s) -
Tambe Milind,
Rosenbloom Paul S.
Publication year - 1996
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1996.tb00273.x
Subject(s) - computer science , event (particle physics) , context (archaeology) , key (lock) , tracking (education) , multi agent system , intelligent agent , autonomous agent , representation (politics) , artificial intelligence , human–computer interaction , real time computing , distributed computing , computer security , psychology , pedagogy , physics , quantum mechanics , paleontology , politics , political science , law , biology
In a dynamic, multiagent environment, an automated intelligent agent is often faced with the possibility that other agents may instigate events that hinder or help the achievement of its own goals. To act intelligently in such an environment, an automated agent needs an event tracking capability to continually monitor the occurrence of such events and the temporal relationships among them. This capability enables an agent to infer the occurrence of important unobserved events as well as to obtain a better understanding of the interaction among events. This article focuses on event tracking in one complex and dynamic multiagent environment: the air‐combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals three new issues that have not been addressed in previous work in this area: (i) tracking events generated by agents’ flexible and reactive behaviors, (ii) tracking events in the context of continuous agent interactions, and (iii) tracking events in real time. This article proposes one solution to address these issues. One key idea in this solution is that the (architectural) mechanisms that an agent employs in generating its own flexible and reactive behaviors can be used to track other agents’ flexible and reactive behaviors in real time. A second key idea is the use of a world‐centered representation for modeling agent interactions. The solution is demonstrated using an implementation of an automated pilot agent.