论文信息 - Scalable Models for Patterns of Life

Scalable Models for Patterns of Life

Patterns of life (POL) are emergent properties of complex social systems. Computational models of POL offer significant potential for practical application and theoretical study, but also important challenges for AI research. Computational POL models must achieve simultaneous scalability along three key dimensions: population size, intelligence, and automatic behavior specification. Three broad research areas that could support important improvements in POL modeling are pattern recognition, representational abstraction, and behavior generation with intelligent agents and the like. This paper describes challenges in POL modeling that AI researchers from many fields can help to meet.

Randolph M. Jones | Jeremiah T. Folsom-Kovarik | Robert E. Wray | Kathleen Bartlett | Sae Lynne Schatz

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