Decision Methods for Adaptive Task-Sharing in Associate Systems

TL;DR

This paper presents a decision-theoretic approach for adaptive task sharing in associate systems, demonstrated through a Mars Rover Manager's Associate that optimally balances autonomous actions and human consultation.

Contribution

It introduces an influence diagram-based model for adaptive task sharing, integrating human problem-solving capabilities into the system architecture.

Findings

MRMA effectively balances autonomous decision-making and human consultation.

The influence diagram approach improves flexibility in uncertain, complex tasks.

Results show potential for enhanced decision support in space exploration.

Abstract

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.