Symbiotic adaptive multisimulation: An autonomic simulation framework for real-time decision support under uncertainty

ABSTRACT

Inspired by the compound arthropod eye, Symbiotic Adaptive Multisimulation (SAMS) introduces an autonomic decision support capability for systems in shifting, ill-defined, uncertain environments. Rather than rely on a single authoritative model, SAMS explores an ensemble of plausible models, which are individually flawed but collectively provide more insight than would be possible otherwise. A case study based on a UAV team search and attack model is presented to illustrate the potential of SAMS. Results demonstrate the capability of SAMS to produce a large degree of exploratory behavior, followed by increased exploitative search behavior as the physical system unfolds.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
  I.6 SIMULATION AND MODELING
      I.6.1 Simulation Theory
          Subjects: Types of simulation (continuous and discrete)*

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents
  I.6 SIMULATION AND MODELING
      I.6.5 Model Development
          Subjects: Modeling methodologies

Keywords:
Agent simulation, decision support, robust intelligence, uncertainty