Dynamic adaptive autonomy in agent-based systems

Agent-based systems require flexibility to perform effectively in complex and dynamic environments. Previous research has identified numerous motivations for adaptability in agent-based systems: however, the extent of this adaptability can be expanded. This paper asserts that agents should be able to benefit from controlling the problem-solving frameworks (also called planning-interaction frameworks) under which they plan for their goals. Dynamic Adaptive Autonomy (DAA) allows agents to control their planning-interaction styles, called autonomy levels, along a defined spectrum (from command-driven to consensus to locally autonomous/master). This allows agents to dynamically form, modify, or dissolve goal-oriented problem-solving groups. This paper presents motivation for DAA arguing that the best type of problem-solving framework for a group of agents depends not only on the problem domain and the pre-defined characteristics of the system, but also on run-time factors that can change during system operation. Thus, it is possible for agents to benefit from the capability to dynamically adapt their problem solving framework to their situation