Multi-agent systems

(mostly observations on theElectric Elves)

Electric ElvesAgents 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/ISIDemonstration 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 DevicesPALM VII+ GPS

WAP Phone

Question: presentation

• The whole approach to anthropomorphising the assistant process has to be done with care– Probably elves are

less loaded thanFridays

– Still all sort ofroom formisinterpretationand setting antisocialnorms

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