Decision Methods for Adaptive Task-Sharing in Associate Systems
  This paper describes some results of research on associate systems:
knowledge-based systems that flexibly and adaptively support their human users
in carrying out complex, time-dependent problem-solving tasks under
uncertainty. Based on principles derived from decision theory and decision
analysis, a problem-solving approach is presented which can overcome many of
the limitations of traditional expert-systems. This approach implements an
explicit model of the human user's problem-solving capabilities as an integral
element in the overall problem solving architecture. This integrated model,
represented as an influence diagram, is the basis for achieving adaptive task
sharing behavior between the associate system and the human user. This
associate system model has been applied toward ongoing research on a Mars Rover
Manager's Associate (MRMA). MRMA's role would be to manage a small fleet of
robotic rovers on the Martian surface. The paper describes results for a
specific scenario where MRMA examines the benefits and costs of consulting
human experts on Earth to assist a Mars rover with a complex resource
management decision.
