Adjustable Autonomy for the Real World

Adjustable autonomy refers to agents’ dynamically varying their own autonomy, transferring decision making control to other entities (typically human users) in key situations. Determining whether and when such transfers of control must occur is arguably the fundamental research question in adjustable autonomy. Previous work, often focused on individual agent-human interactions, has provided several different techniques to address this question. Unfortunately, domains requiring collaboration between teams of agents and humans reveals two key shortcomings of these previous techniques. First, these techniques use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects of actions) to an agent’ team due to such transfers of control.

[1]  Jean Oh,et al.  Electric Elves: Applying Agent Technology to Support Human Organizations , 2001, IAAI.

[2]  Stuart J. Russell,et al.  Principles of Metareasoning , 1989, Artif. Intell..

[3]  Robert P. Goldman,et al.  Integrated Task Representation for Indirect Interaction Position Paper , 1997 .

[4]  David Kortenkamp,et al.  Adjustable Autonomy for Human-Centered Autonomous Systems on Mars , 1998 .

[5]  K. Suzanne Barber,et al.  Dynamic adaptive autonomy in multi-agent systems , 2000, J. Exp. Theor. Artif. Intell..

[6]  Keith W. Miller,et al.  How good is good enough?: an ethical analysis of software construction and use , 1994, CACM.

[7]  Worthy N. Martin,et al.  Effects of Uncertainty on Variable Autonomy in Maintenance Robots , 1999 .

[8]  Tom M. Mitchell,et al.  Experience with a learning personal assistant , 1994, CACM.

[9]  Victor R. Lesser,et al.  The UMASS intelligent home project , 1999, AGENTS '99.

[10]  K. Suzanne Barber,et al.  A Communication Protocol Supporting Dynamic Autonomy Agreements in Multi-agent Systems , 2000, PRICAI Workshops.

[11]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[12]  Eric Horvitz,et al.  Principles of mixed-initiative user interfaces , 1999, CHI '99.

[13]  S. Guerlain,et al.  Integrated Task Representation for Indirect InteractionPosition , 1997 .

[14]  Abhimanyu Das,et al.  Adaptive Agent Integration Architectures for Heterogeneous Team Members , 2000, ICMAS.

[15]  Jean Oh,et al.  Electric Elves: Immersing an Agent Organization in a Human Organization , 2000 .

[16]  Debra Schreckenghost Human Interaction with Control Software Supporting Adjustable Autonomy , 1999 .

[17]  Henry Hexmoor,et al.  Adjusting Autonomy by Introspection , 1999 .

[18]  Eric Horvitz,et al.  Attention-Sensitive Alerting , 1999, UAI.

[19]  David P. Miller,et al.  Experiences with an architecture for intelligent, reactive agents , 1995, J. Exp. Theor. Artif. Intell..

[20]  Milind Tambe,et al.  Towards Flexible Teamwork , 1997, J. Artif. Intell. Res..

[21]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[22]  James F. Allen,et al.  TRAINS-95: Towards a Mixed-Initiative Planning Assistant , 1996, AIPS.

[23]  Shlomo Zilberstein,et al.  Monitoring anytime algorithms , 1996, SGAR.

[24]  Maria L. Gini,et al.  Mixed-initiative decision support in agent-based automated contracting , 2000, AGENTS '00.

[25]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[26]  David J. Musliner,et al.  Adjustable Autonomy in Procedural Control for Refineries , 1999 .

[27]  Christine M. Mitchell,et al.  Human interaction with lights-out automation: a field study , 1996, Proceedings Third Annual Symposium on Human Interaction with Complex Systems. HICS'96.

[28]  Henry Hexmoor,et al.  A Cognitive Model of Situated Autonomy , 2000, PRICAI Workshops.