Electric Elves - Observations on Multi-Agent Systems
Discover the revolutionary concept of Electric Elves and the impact of multi-agent systems in human organizations. Explore their diverse functions, from managing email to facilitating organizational goals, with a focus on teamwork and adjustable autonomy in teams.
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Presentation Transcript
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 caf [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
