Policy-based Agent Directability

Many potential applications for agent technology require humans and agents to work together to achieve complex tasks effectively. In contrast, most of the work in the agents community to date has focused on technologies for fully autonomous agent systems. This paper presents a framework for the directability of agents, in which a human supervisor can define policies to influence agent activities at execution time. The framework focuses on the concepts of adjustable autonomy for agents (i.e., varying the degree to which agents make decisions without human intervention) and strategy preference (i.e., recommending how agents should accomplish assigned tasks). These mechanisms enable a human to customize the operations of agents to suit individual preferences and situation dynamics, leading to improved system reliability and increased user confidence over fully automated agent systems. The directability framework has been implemented within a BDI environment, and applied to a multiagent intelligence-gathering domain.