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Multi-agent systems (mostly observations on the Electric Elves)

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1 Multi-agent systems (mostly observations on the Electric Elves)

2 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,…

3 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

4 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 ( – Order our meals

5 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

6 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

7 Overall E-Elves Architecture, Showing Friday Agents Interacting with Users

8 Elves in Use: Reschedule Meetings Personalize

9 Friday Ordering Dinner “ More & More computers are ordering food,… we need to think about marketing” Subway owner

10 Elves in Use: Wireless Devices PALM VII + GPS WAP Phone

11 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

12 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)

13 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)

14 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

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