Goal-Driven Autonomy for Cognitive Systems

Matthew PaisnerUniversity of Maryland
Michael CoxUniversity of Maryland Institute of Advanced Computer Studies
Michael MaynordUniversity of Maryland
Don PerlisUniversity of Maryland

Abstract

Complex, dynamic environments present special challenges to autonomous agents. Specifically, agents have difficulty when the world does not cooperate with design assumptions. We present an approach to autonomy that seeks to maximize robustness rather than optimality on a specific task. Goal-driven autonomy involves recognizing possibly new problems, explaining what causes the problems and generating goals to solve the problems. We present such a model within the MIDCA cognitive architecture and show that under certain conditions this model outperforms a less flexible approach to handling unexpected events.

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