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Working together - cont Lecture outline

Multi-Agent Systems Lecture 7 University “Politehnica” of Bucarest 2005 - 2006 Adina Magda Florea adina@cs.pub.ro http://turing.cs.pub.ro/blia_06. Working together - cont Lecture outline. 1 Modeling coordination by shared mental states 2 Joint action and conventions.

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Working together - cont Lecture outline

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  1. Multi-Agent SystemsLecture 7University “Politehnica” of Bucarest2005 - 2006Adina Magda Floreaadina@cs.pub.rohttp://turing.cs.pub.ro/blia_06

  2. Working together - contLecture outline 1 Modeling coordination by shared mental states 2 Joint action and conventions

  3. 1 Modeling coordination by shared mental states Collective mental states (a) Common knowledge • Every member in group G knows p EGp aiGKaip - shared knowledge • Every member in G knows EGp E2Gp  EG(EGp) • Every member knows that every member knows that every … Ek+1Gp  EG(EKGp) k1 • Common knowledge CGp  p  EGp  E2Gp  …  EkGp  ... 3

  4. (b) Mutual belief • EGp aiGaiBelp - Every one in group G believes p - shared belief • E2Gp  EG(EGp) • Ek+1Gp  EG(EKGp) k1 • MGp  EGp  E2Gp  …  EkGp  … - Mutual belief 4

  5. (c) Joint intentions(Levesque & Cohen, 1990) C1) each agent in the group has a goal p aiG aiIntp Modal operators of temporal logic p U q - p is true until q becomes true - until Xp - p is true in the next moment - next Pp - p was true in a past moment - past Fp - p will eventually be true in the future - eventually Gp - p will always be true in the future – always Ap - at a particular time moment, p is true in all paths emanating from that point - inevitable p Ep - at a particular time moment, p is true in some path emanating from that point - optional p 5

  6. s is true in each time point (situation) and on all path • r is true in each time point on a single path • p will eventually be true on a single path • q will eventually be true on all path s p s q F - eventually G - always A - inevitable E - optional AGs EGr AFq EFp r s r s r s q s q s r - Alice is in Italy p -Alice visits Paris s – Paris is the capital of France q - it is spring time 6

  7. (c) Joint intentions- cont C1) each agent in the group has a goal p aiG aiIntp C2) each agent will persist with this goal until it is mutually believed that p has been achieved or that p cannot be achieved aiG aiInt (A Fp)  A ( aiInt(A Fp)  (MG(Achieve p)  MG(Achieve p))) C3) conditions (C1) and (C2) are mutually believed MG(C1)  MG(C2) F - eventually G - always A - inevitable E - optional 7

  8. (d) Joint commitments Agents in the group: • have a joint goal • agree they wish to cooperate • the group becomes jointly committed to achieve the goal (joint goal) Joint intentions can be seen as a joint commitment to a joint action while in a certain shared mental state 8

  9. 3 Joint action and conventions 3.1 Conventions An agent should honor its commitments provided the circumstances do not change. Conventions = describe circumstances under which an agent should reconsider its commitments An agent may have several conventions but each commitment is tracked using one convention 9

  10. Commitments provide a degree of predictability so that the agents can take future activity of other agents in consideration when dealing with inter-agent dependencies the necessary structure for predictable interactions • Conventions constrain the conditions under which commitments should be reassesed and specify the associated actions that should be undertaken: retain, rectify or abandon the commitment the necessary degree of mutual support 10

  11. 3.2 Specifying conventions Reasons for re-assessing the commitment • commitment satisfied • commitment unattainable • motivation for commitment no longer present Actions R1: ifcommitment satisfiedor commitment unattainableor motivation for commitment no longer present thendrop commitment • But such conventions are asocial constructs; they do not specify how the agent should behave towards the other agents if: • it has a goal that is inter-dependent • it has a joint commitment to a joint goal 11

  12. Social Conventions Invoke when: Inter-dependent goals • local commitment dropped • local commitment satisfied • motivation for local commitment no longer present R1: iflocal commitment satisfiedor local commitment droppedor motivation for local commitment no longer present theninform all related commitments Invoke when: Joint commitment to a joint goal • status of commitment to joint goal changes • status of commitment to attaining joint goal in the team context changes • status of commitment of another team member changes R1: ifstatus of commitment to joint goal changesor status of commitment in the team context changes theninform all other team members of the change R2: ifstatus of commitment of another team member changes thendetermine whether joint commitment is still viable 12

  13. 3.3 An example of joint action and conventions GRATE System (Generic Rules and Agent model Testbed Environment, Jennings, 1994) ARCHON electricity distribution management cement factory control Electricity distribution management of the traffic network • distinguish between disturbances and pre-planned maintenance operations • identify the type (transient or permanent), origin and extend of faults when they occur • determine how to restore the network after a fault 3 agents AAA - the Alarm Analysis Agent perform diagnosis to different levels BAI - the Blackout Area Identifier of precision and on different info CSI - Control System Interface detects the disturbance initially and then monitors the network evolving state 13

  14. Acquaintance Models Self Model Information store GRATE Agent Architecture Inter-agent communication Communication Manager CONTROL DATA COOPERATION MODULE Cooperation & Control Layer SITUATION ASSESMENT MODULE CONTROL MODULE Domain Level System Task1 Task3 Task2 14

  15. (a) Agent behavior 1. Select goal and develop plan to achieve goal 2. Determine if plan can be executed individually or cooperatively (a) joint action is needed (joint goal) or (b) action solved entirely locally 3. if (a) then the agent becomes the organiser 3.1. Establish joint action - the organiser carries on the distributed planning protocol 3.2. Perform individual actions in joint action 3.3. Monitor joint action 4. if (b) then perform individual actions 5. if request for joint action then the agent becomes team-member 5.1. Participate in the planning protocol to establish joint action 5.2. Perform individual actions in joint actions (3.2 and 5.2 adequately sequenced) 15

  16. (b) Establish joint action GRATE Distributed Planning Protocol PHASE 1 1. Organiser detects need for joint action to achieve goal G and determines that plan P is the best means of attaining it - SAM 2. Organiser contacts all acquaintances capable of contributing to P to determine if they will participate in the joint action - CM 3. Let L  set of willing acquaintances PHASE 2 4. for all actions B in P do - select agent AL to carry out action B - calculate time tB for B to be performed based on temporal orderings of P - send (B, tB) proposal to A - receive reply from A - if not rejected then - if time proposal modified then update remaining actions by t - eliminate B from P 5.if B is not empty then repeat step 4 Agent A 1. Evaluate proposal (B, tB) against existing commitments 2.if no conflicts then create commitment CB to (B, tB) 3.if conflicts ((B, tB), C) and priority(B) > priority(C) then create CB and reschedule C 4.if conflicts ((B, tB), C) and priority(B) < priority(C) then if freetime (tB+ t) then note CB and return (tB+ t) else return reject 16

  17. Joint intention - Phase 1 for agent AAA Name: Diagnose-fault Motivation: Disturbance-detection-message Plan: { S1: Identify_blackout_area, S2: Hypothesis_generation, S3: Monitor_disturbance, S4: Detailed_diagnosis, S2 < S4} Start time: Maximum end time: Duration: Priority: 20 Status:Establish group Outcome: Validated_fault_hypothesis Participants: ((Self organiser agreed_objective) (CSI team-member agreed_objective) (BAI team-member agreed_objective)) Bindings: NIL Proposed contribution: ((Self (Hypothesis_generation yes) (Detailed_diagnosis yes)) (CSI (Monitor_disturbance yes) (BAI (Identify_blackout_area yes))) 17

  18. Joint action - Phase 2 for agent AAA Name: Diagnose-fault Motivation: Disturbance-detection-message Status:Establish plan Start time: 19 Maximum end time: 45 Bindings: ((BAI Identify_blackout_area 19 34) (Self Hypothesis_generation 19 30) (CSI Monitor_disturbance 19 36) (Self Detailed_diagnosis 36 45)) …. • BAI's individual intention for producing the blackout area Name: Achieve Identify_blackout_area Motivation: Satisfy Joint Action Diagnose-fault Start time: 19 Maximum end time: 34 Duration: 15 Priority: 5 Status: Pending Outcome: Blackout_area 18

  19. (c) Monitor the execution of joint action Recognize situations that change commitments and impact joint action R1match: if task t has finished executing and t has produced the desired outcome of the joint action then the joint goal is satisfied R2match: if receive information i and i is relevant to the triggering conditions for joint goal G and i invalidates beliefs for wanting G then the motivation for G is no longer present Social conventions R1inform: if joint action has successfully finished then inform all team members of successful completion and see if result should be disseminated outside the team R2inform: if motivation for joint goal G is no longer present then inform other members of the team that G needs to be abandoned Rules to indicate what to do if change in commitments ……….. 19

  20. References • Multiagent Systems - A Modern Approach to Distributed Artificial Intelligence, G. Weiss (Ed.), The MIT Press, 2001, Ch.2.3, 8.5-8.7 • V.R. Lesser. A retrospective view of FA/C distributed problem solving. IEEE Trans. On Systems, Man, and Cybernetics, 21(6), Nov/Dec 1991, p.1347-1362. • N.R. Jennings. Coordination techniques for distributed artificial intelligence. In Foundations of Distributed Artificial Intelligence, G. O'hara, N.R; Jennings (Eds.), John Wiley&Sons, 1996. • N.R. Jennings. Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artificial Intelligence 72(2), 1995. • E.H. Durfee. Scaling up agent coordination strategies. IEEE Computer, 34(7), July 2001, p.39-46. 20

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