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Electric Elves 107

intervening, leading to 152 cases of user-prompted rescheduling, indicating the
critical importance of AA in Friday agents.
The general effectiveness of E-Elves is shown by several observations. Since
the E-Elves deployment, the group members have exchanged very few email
messages to announce meeting delays. Instead, Fridays autonomously inform
users of delays, thus reducing the overhead of waiting for delayed members.
Second, the overhead of sending emails to recruit and announce a presenter for
research meetings is now assumed by agent-run auctions. Third, the People
Locator is commonly used to avoid the overhead of trying to manually track
users down. Fourth, mobile devices keep us informed remotely of changes in
our schedules, while also enabling us to remotely delay meetings, volunteer for
presentations, order meals, etc. We have begun relying on Friday so heavily to
order lunch that one local Subway restaurant owner even suggested marketing
to agents: “More and more computers are getting to order food, so we might
have to think about marketing to them!!”
Most importantly, over the entire span of the E-Elves’ operation, the agents
have never repeated any of the catastrophic mistakes that Section 3 enumer-
ated in its discussion of our preliminary decision-tree implementation. For
instance, the agents do not commit error 4 from Section 3 because of the do-
main knowledge encoded in the bid-for-role MDP that specifies a very high cost
for erroneously volunteering the user for a presentation. Likewise, the agents
never committed errors 1 or 2. The policy described in Section 4 illustrates how
the agents would first ask the user and then try delaying the meeting, before
taking any final cancellation actions. The MDP’s lookahead capability also
prevents the agents from committing error 3, since they can see that making
one large delay is preferable, in the long run, to potentially executing several
small delays. Although the current agents do occasionally make mistakes, these
errors are typically on the order of asking the user for input a few minutes earlier
than may be necessary, etc. Thus, the agents’ decisions have been reasonable,
though not always optimal. Unfortunately, the inherent subjectivity in user
feedback makes a determination of optimality difficult.

6. Conclusion
Gaining a fundamental understanding of AA is critical if we are to deploy
multi-agent systems in support of critical human activities in real-world set-
tings. Indeed, living and working with the E-Elves has convinced us that AA
is a critical part of any human collaboration software. Because of the negative
result from our initial C4.5-based approach, we realized that such real-world,
multi-agent environments as E-Elves introduce novel challenges in AA that
previous work has not addressed. For resolving the AA coordination challenge,
our E-Elves agents explicitly reason about the costs of team miscoordination,