Why the elf acted autonomously: towards a theory of adjustable autonomy

ABSTRACT

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 transfer of control must occur is arguably the fundamental research question in adjustable autonomy. Practical systems have made significant in roads in answering this question and in providing high-level guidelines for transfer of control decisions. For instance, [11] report that Markov decision processes were successfully used in transfer of control decisions in a real world multiagent system, but that use of C4.5 led to failures. Yet, an underlying theory of transfer of control, that would explain such successes or failures is missing. To take a step in building this theory, we introduce the notion of a transfer-of-control strategy, which potentially involves several transfer of control actions. A mathematical model based on this notion allows both analysis of previously reported implementations and guidance for the design of new implementations. The practical benefits of this model are illustrated in a dramatic simplification of an existing adjustable autonomy system.

REFERENCES

• 1: Hans Chalupsky , Yolanda Gil , Craig A. Knoblock , Kristina Lerman , Jean Oh , David V. Pynadath , Thomas A. Russ , Milind Tambe, Electric Elves: Applying Agent Technology to Support Human Organizations, Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference, p.51-58, August 07-09, 2001
• 2: M. Fleming and R. Cohen. Towards a methodology for designing and evaluating mixed-initiative AI systems. In Proceedings of AAAI Workshop on mixed initiative intelligence, 1999
• 3: M. Fleming and R. Cohen. A utility-based theory of initiative in mixed initiative systems. In The IJCAI-01 Workshop on Autonomy, Delegation, and Control: Interacting with Autonomous Agents, 2001
• 4: Call for Papers. AAAI spring symposium on adjustable autonomy. www.aaai.org, 1999
• 5: M. Goodrich, D. Olsen, J. Crandall, and T. Palmer. Experiments in adjustable autonomy. In Proceedings of IJCAI Workshop on Autonomy, Delegation and Control: Interacting with Intelligent Agents, 2001
• 6: Henry Hexmoor, A Cognitive Model of Situated Autonomy, Revised Papers from the PRICAI 2000 Workshop Reader, Four Workshops held at PRICAI 2000 on Advances in Artificial Intelligence, p.325-334, August 28-September 01, 2000
• 7: Eric Horvitz, Principles of mixed-initiative user interfaces, Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit, p.159-166, May 15-20, 1999, Pittsburgh, Pennsylvania, United States [doi>10.1145/302979.303030]
• 8: L. Pryor and G. Collins. Planning for contingency: a decision based approach. J. of Artificial Intelligence Research, 4:81--120, 1996
• 9: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 10: Stuart Russell , Eric Wefald, Principles of metareasoning, Proceedings of the first international conference on Principles of knowledge representation and reasoning, p.400-411, December 1989
• 11: Paul Scerri , David Pynadath , Milind Tambe, Adjustable autonomy in real-world multi-agent environments, Proceedings of the fifth international conference on Autonomous agents, p.300-307, May 2001, Montreal, Quebec, Canada [doi>10.1145/375735.376314]
• 12: S. Zilberstein. Using anytime algorithms in intelligent systems. AI Magazine, 17(3):73--83, 1996

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents

General Terms:
Theory