Kaa: Policy-based explorations of a richer model for adjustable autonomy

Abstract

Though adjustable autonomy is hardly a new topic in agent systems, there has been a general lack of consensus on terminology and basic concepts. In this paper, we describe the multi-dimensional nature of adjustable autonomy and give examples of how various dimensions might be adjusted in order to enhance performance of human-agent teams. We then introduce Kaa (KAoS adjustable autonomy), which extends our previous work on KAoS policy and domain services to provide a policy-based capability for adjustable autonomy based on this richer notion of adjustable autonomy. The current implementation of Kaa uses a combination of ontologies represented in OWL and influence-diagram-based decision-theoretic algorithms to determine what if any changes should be made in agent autonomy in a given context. We have demonstrated Kaa as part of ONR-sponsored research to improve naval de-mining operations through more effective human-robot interaction. A brief comparison among alternate approaches to adjustable autonomy is provided.