Agent-based technologies can be applied to many aspects of supply chain management. The need for responsive, flexible agents is pervasive in this environment due to its complex, dynamic nature. Two critical aspects of agent capabilities are the ability to (1) classify agent behaviors according to autonomy level and (2) adapt problem-solving roles to various problem-solving situations during system operation. Sensible Agents, capable of Dynamic Adaptive Autonomy, have been developed to address these issues. A Sensible Agent’s “autonomy level” constitutes a description of the agent’s problem-solving role with respect to a particular goal. Problem-solving roles are defined along a spectrum of autonomy ranging from command-driven, to consensus, to locally autonomous/master. Dynamic Adaptive Autonomy is a capability that allows Sensible Agents to change autonomy levels during system operation to meet the needs of a particular problem-solving situation. This paper provides an overview of the Sensible Agent Testbed and introduces an example supply chain management domain with a scenario showing how this testbed could be used to simulate agent-based problem solving.

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