Decision Methods for Adaptive Task-Sharing in Associate Systems
This work addresses the challenge of flexible human-computer collaboration in time-dependent problem-solving, such as managing robotic rovers on Mars, though it appears incremental in building on existing decision theory principles.
The paper tackles the problem of adaptive task-sharing between human users and knowledge-based systems in complex, uncertain environments, presenting an approach based on decision theory that overcomes limitations of traditional expert-systems, with application to a Mars Rover Manager's Associate scenario.
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.