Intelligent agents perform multiple concurrent tasks requiring both knowledge-based reasoning and interaction with dynamic entities in the environment, under real-time constraints. Because an agent's opportunities to perceive, reason about, and act upon the environment typically exceed its computational resources, it must determine which operations to perform and when to perform them so as to achieve its most important objectives in a timely manner. Accordingly, we view the problem of real-time performance as a problem in intelligent real-time control. We propose and define several important control requirements and present an agent architecture that is designed to address those requirements. The proposed architecture is a blackboard architecture, whose key features include: distribution of perception, action, and cognition among parallel processes, limited-capacity I/O buffers with best-first retrieval and worst-first overflow, dynamic control planning, dynamic focus of attention, and...

for Correspondence An agent should adopt different control modes in different situations. Depending on the predictability of its environment and the constraint imposed by its goals, the agent should modulate its sensitivity to run-time events and its commitment to specific actions. We propose an opportunistic control model that supports this flexibility. 3 Abstract Given its multiple goals, limited resources, and dynamic environment, an intelligent agent must decide which of many possible actions to execute at each point in time. Planning and reactive models embody two different modes of control. By contrast, we characterize a two-dimensional space of control modes, each of which maximizes the quality of run-time behavior in the corresponding region of a two-dimensional space of control situations. The situation space is defined by dimensions representing the predictability of the agent's task environment and the constraint imposed by its goals. The space of control modes is defined ...

An intelligent agent must interact with dynamic entities in real time. Because it cannot predict all events that will occur, it must notice and respond to important unanticipated events. However, insuring execution of the best possible operation at each point in time conflicts with meeting deadlines, especially as event rate and number of known operations increase. The problem is: How can an agent with limited resources execute high-quality operations in constant time, despite increases in event rate and number of known operations? To support this capability in a blackboard architecture, we replace the conventional reasoning cycle with a satisficing cycle. To bound cycle time, it interrupts triggering to execute the best operation available when either it finds a "good enough" operation or a deadline occurs. To insure the availability of high-quality operations when interrupts occur, it uses dynamic control plans to order its consideration of events and known reasoning operations best-...