Electric Elves 107intervening, leading to 152 cases of user-prompted rescheduling, indicating thecritical importance of AA in Friday agents.The general effectiveness of E-Elves is shown by several observations. Sincethe E-Elves deployment, the group members have exchanged very few emailmessages to announce meeting delays. Instead, Fridays autonomously informusers of delays, thus reducing the overhead of waiting for delayed members.Second, the overhead of sending emails to recruit and announce a presenter forresearch meetings is now assumed by agent-run auctions. Third, the PeopleLocator is commonly used to avoid the overhead of trying to manually trackusers down. Fourth, mobile devices keep us informed remotely of changes inour schedules, while also enabling us to remotely delay meetings, volunteer forpresentations, order meals, etc. We have begun relying on Friday so heavily toorder lunch that one local Subway restaurant owner even suggested marketingto agents: “More and more computers are getting to order food, so we mighthave to think about marketing to them!!”Most importantly, over the entire span of the E-Elves’ operation, the agentshave never repeated any of the catastrophic mistakes that Section 3 enumeratedin its discussion of our preliminary decision-tree implementation. Forinstance, the agents do not commit error 4 from Section 3 because of the domainknowledge encoded in the bid-for-role MDP that specifies a very high costfor erroneously volunteering the user for a presentation. Likewise, the agentsnever committed errors 1 or 2. The policy described in Section 4 illustrates howthe agents would first ask the user and then try delaying the meeting, beforetaking any final cancellation actions. The MDP’s lookahead capability alsoprevents the agents from committing error 3, since they can see that makingone large delay is preferable, in the long run, to potentially executing severalsmall delays. Although the current agents do occasionally make mistakes, theseerrors are typically on the order of asking the user for input a few minutes earlierthan may be necessary, etc. Thus, the agents’ decisions have been reasonable,though not always optimal. Unfortunately, the inherent subjectivity in userfeedback makes a determination of optimality difficult.6. ConclusionGaining a fundamental understanding of AA is critical if we are to deploymulti-agent systems in support of critical human activities in real-world settings.Indeed, living and working with the E-Elves has convinced us that AAis a critical part of any human collaboration software. Because of the negativeresult from our initial C4.5-based approach, we realized that such real-world,multi-agent environments as E-Elves introduce novel challenges in AA thatprevious work has not addressed. For resolving the AA coordination challenge,our E-Elves agents explicitly reason about the costs of team miscoordination,
108 Socially Intelligent Agentsthey flexibly transfer autonomy rather than rigidly committing to initial decisions,and they may change the coordination rather than taking risky actions inuncertain states. We have implemented our ideas in the E-Elves system usingMDPs, and our AA implementation nows plays a central role in the successful24/7 deployment of E-Elves in our group. Its success in the diverse tasks ofthat domain demonstrates the promise that our framework holds for the widerange of multi-agent domains for which AA is critical.AcknowledgmentsThis research was supported by DARPA award No. F30602-98-2-0108 (Control of Agent-Based Systems) and managed by ARFL/Rome Research Site.References[1] Chalupsky, H., Gil, Y., Knoblock, C. A., Lerman, K., Oh, J., Pynadath, D. V., Russ, T. A.,and Tambe, M. Electric elves: Applying agent technology to support human organizations.In Proc. of the IAAI. Conf., 2001.[2] Collins, J., Bilot, C., Gini, M., and Mobasher, B. Mixed-init. dec.-supp. in agent-basedauto. contracting. In Proc. of the Conf. on Auto. Agents, 2000.[3] Dorais, G. A., Bonasso, R. P., Kortenkamp, D., Pell, B., and Schreckenghost, D. Adjustableautonomy for human-centered autonomous systems on mars. In Proc. of the Intn’l Conf.of the Mars Soc., 1998.[4] Ferguson, G., Allen, J., and Miller, B. TRAINS-95 : Towards a mixed init. plann. asst. InProc. of the Conf. on Art. Intell. Plann. Sys., pp. 70–77.[5] Horvitz, E., Jacobs, A., and Hovel, D. Attention-sensitive alerting. In Proc. of the Conf.on Uncertainty and Art. Intell., pp. 305–313, 1999.[6] Lesser, V., Atighetchi, M., Benyo, B., Horling, B., Raja, A., Vincent, R., Wagner, T., Xuan,P., and Zhang, S. X. A multi-agent system for intelligent environment control. In Proc.of the Conf. on Auto. Agents, 1994.[7] Mitchell, T., Caruana, R., Freitag, D., McDermott, J., and Zabowski, D. Exp. with alearning personal asst. Comm. of the ACM, 37(7):81–91, 1994.[8] Puterman, M. L. Markov Decision Processes. John Wiley & Sons, 1994.[9] Quinlan, J. R. C4.5: Progs. for Mach. Learn. Morgan Kaufmann, 1993.[10] Scerri, P., Pynadath, D. V., and Tambe, M. Adjustable autonomy in real-world multi-agentenvironments. In Proc. of the Conf. on Auto. Agents, 2001.[11] Tambe, M., Pynadath, D. V., Chauvat, N., Das, A., and Kaminka, G. A. Adaptive agentintegration architectures for heterogeneous team members. In Proc. of the Intn’l Conf. onMultiAgent Sys., pp. 301–308, 2000.[12] Tollmar, K., Sandor, O., and Schōmer, A. Supp. soc. awareness: @Work design & experience.In Proc. of the ACM Conf. on CSCW, pp. 298–307, 1996.
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ContentsContributing Authors1Social
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Contents21Experiences with Sparky,
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Contributing AuthorsAude BillardCom
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Contributing AuthorsxiPeyman Farati
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Understanding Social Intelligence 2
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Chapter 3MODELING SOCIAL RELATIONSH
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Modeling Social Relationship 31soci
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Modeling Social Relationship 33Figu
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Chapter 4DEVELOPING AGENTS WHO CANR
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Developing Agents Who Can Relate to
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Developing Agents Who Can Relate to
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Developing Agents Who Can Relate to
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Chapter 5PARTY HOSTS AND TOUR GUIDE
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Party Hosts and Tour Guides 47conve
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Party Hosts and Tour Guides 492.2 E
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Party Hosts and Tour Guides 514. Co
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Chapter 6INCREASING SIA ARCHITECTUR
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Adapting to Affect and Personality
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Chapter 30MULTI-AGENT CONTRACT NEGO
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Enabling Open Agent Institutions 26
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Enabling Open Agent Institutions 26
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Chapter 33EMBODIED CONVERSATIONAL A
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ECA’s In E-Commerce Applications
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ECA’s In E-Commerce Applications
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ECA’s In E-Commerce Applications
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Index"like-me" test 85,89“Giant3
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INDEX 277educational computer games
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INDEX 279mutual selection 243mutual
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INDEX 281theory of dramatic writing