Multi-agent systems
(mostly observations on the
Electric Elves)
Electric Elves
Agents revolution: agents have proliferated in human organizations
• Personal assistants: Gather information, manage email,
shopping…
• Control resources: Building temp, software tools, …
Next step: Dynamic agent teams facilitate entire organizations
• Teams function 24/7
• Agent proxies for humans, helping:
– Routine coordination in organizations
– Coherent/robust actions to attain organizational goals
– Swift reaction to crises
• E.g., Coordinate move of personnel, equipment to crisis site
• Results applicable to many organizations: military, business,…
Illustrative Tasks from USC/ISI
Demonstration in Washington, DC:
• Rapid team formation: People flying out, support at ISI
• Team planning: Travel arrangements, shipping equipment
• Team plan monitor/repair: Team member becomes ill,
flights delayed, equipment breakdown
Hosting visitors at ISI
• Team plans/repair: Schedule visit; monitor/reschedule
Help at conferences/technical meetings
• Team formation/monitor: Arrange meeting with other
researchers
Facilitate routine organizational activities
Current Focus:
Elves in One Research Group
Mixed 15 agent team:
• Agent proxies for 9 researchers (called “Friday”)
– Interfaces: PDA/GPS, WAP phones, workstation, fax,
speech
• Agent proxy for a project assistant
• Information agents, schedulers, matchers…
Agent proxies run 24/7
• First deployment in a real organization
• Help us with real tasks
–
–
–
–
Coordinate meetings (reschedule if delays, cancel)
Decide presenters at research meetings (via auctions)
Track people (www.isi.edu/teamcore/info.html)
Order our meals
Research Challenges
•
•
•
•
Teamwork and adjustable autonomy in teams
Data source verification and reinduction
Hybrid logic and topic-based matching
Matchmaking for complex agents
•
•
•
•
•
•
Dynamic team formation (e.g., via auctions)
Human organization norms: authorities, permissions etc.
Scale up complexity, number, and heterogeneity
Rapid incorporation of new agents
Robustness and adaptability of agents
Widespread substitutability of agents
Focusing on One Research Topic:
Adjustable Autonomy in Teams
Proxies for users: Teamwork with others, while serving human users
Adjustable autonomy: “Dynamically adjust agent’s autonomy”
• Autonomous action on behalf of humans reduces burden, but…
– Proxies face significant uncertainty, e.g., how hungry?
– Errors in autonomous actions may be costly
• Reduce autonomy, transfer control to humans in critical situations
Teams raise novel challenges for adjust autonomy!
• Previous work: Individual agent/user interactions
• With teams, an agent must serve the user AND the team
E.g., Cannot wait for user input: causes team miscoordination
• Pursuing an approach based on C4.5 then Markov Decision
Processes
Overall E-Elves Architecture, Showing
Friday Agents Interacting with Users
Elves in Use: Reschedule
Meetings
Personalize
Friday Ordering Dinner
“ More & More computers are ordering food,…
we need to think about marketing” Subway owner
Elves in Use: Wireless Devices
WAP Phone
PALM VII
+ GPS
Question: presentation
• The whole approach to
anthropomorphising the
assistant process has to be done
with care
– Probably elves are
less loaded than
Fridays
– Still all sort of
room for
misinterpretation
and setting antisocial
norms
To act automatically or with request
guidance?
• Agent (group) task: get all meeting attendees to arrive at
same time
– But what if one attendee is perceived by his agent as apt to be
delayed
– User is often better able to determine if the meeting needs to
be delayed for him
– But potential for mis-coordination while awaiting user response
if agent hands the decision over
• Agent can
– Make an autonomous decision
– Transfer control (ask user, and wait)
– Change coordination constraints (e.g. delay the meeting a little)
Sometimes it goes wrong
• Learning defaults by C4.5 (patched with some heuristics)
– This won’t always model everything a human would want taken
into consideration
• Error observed with the elves
– Autonomously cancelling a meeting that was desired (e.g. with
big boss) (either initially, or after too long of delay from user)
– Accepting an invite (to give a presentation) that the user didn’t
want
– Repeatedly delaying a meeting in small increments (almost 50
times at 5 minutes per)
• They’re trying a more sophisticated model
– Partially observable Markov decision processes
– But the trade-off of autonomy and error in inherent (we’ll come
back to this)
Privacy and manipulation
• The agents contradict ‘little white lies’
– “I was stuck in traffic”
“No, you were at the café”
– [locked office, lights out]
(email): Your agent says you’re in there
• Hurt feelings by making importance levels clear
– Why are we (e.g. PhD students) given lower priority?!
• Allow statistical summaries that embarrass
– You’re always 5 minutes late to PhD meetings but on time with
staff colleagues!
• Manipulation
– Stack calendar with dummy meetings, or meetings labelled
‘basketball’ that agent doesn’t know are lower priority, to avoid
being selected to give a presentation