Multi-Agent SystemsLecture 9Lecture 9
University “Politehnica” of Bucarest2007 - 2008
Adina Magda [email protected]
http://turing.cs.pub.ro/blia_08curs.cs.pub.ro
Working together Working together Lecture outlineLecture outline
1 Coordination strategies1 Coordination strategies2 Modeling coordination by shared mental 2 Modeling coordination by shared mental
statesstates3 Joint action and conventions3 Joint action and conventions
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1 Coordination strategies Coordination Coordination = the process by which an agent reasons about
its local actions and the (anticipated) actions of others to try to ensure the community acts in a coherent manner
CoordinationCoordination
CollectivelyCollectivelymotivated agentsmotivated agentscommon goals
Self-interestedSelf-interestedagentsagents
own goals
Cooperation toCooperation toachieve common goalachieve common goal
Coordination forCoordination forcoherent behaviorcoherent behavior
Neutral to one anotherNeutral to one anotherdisjunctive goals
CompetitiveCompetitiveconflicting goals
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Centralized coordination Distributed coordination
Model Protocol Communication
Tightly coupled interactions - distributed search Cognitive agents – DPS (distributed planning, task
sharing, resource sharing) Heterogeneous agents - interaction protocols: Contract
Net, KQML conversations, FIPA protocols Dynamic interactions – Shared mental states,
commitments and conventions Complex interactions - organizational structure to
reduce complexity
Unpredictable interactions - social laws Conflict of interests - interaction protocols: voting,
auctions, bargaining, market mechanisms, extended Contract Net, coalition formation
CooperativeCooperative
Neutral orNeutral orcompetitivecompetitive
Collective mental statesCollective mental states(a) Common knowledge Every member in group G knows p EGp aiGKaip
- shared knowledge Every member in G knows EGp, E2
Gp EG(EGp) Every member knows that every member knows that every
…Ek+1
Gp EG(EKGp) k1
Common knowledgeCGp p EGp E2
Gp … EkGp ...
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2 Modeling coordination by shared mental states
(b) Mutual belief EGp aiGaiBelp - Every one in group G believes p -
shared belief
E2Gp EG(EGp)
Ek+1Gp EG(EK
Gp) k1
MGp EGp E2Gp … Ek
Gp … - Mutual belief
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(c) Joint intentions C1) each agent in the group has a goal p
aiG 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
aiG aiInt (A Fp) A ( aiInt(A Fp) (MG(Achieve p) MG(Achieve p)))
C3) conditions (C1) and (C2) are mutually believedMG(C1) MG(C2)
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F - eventuallyG - alwaysA - inevitableE - optional
(d) Joint commitmentsAgents 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
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3 Joint action and conventions3.1 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
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CommitmentsCommitments 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
ConventionsConventions 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
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3.2 Specifying conventionsReasons for re-assessing the commitment commitment satisfied commitment unattainable motivation for commitment no longer present
ActionsR1: if commitment satisfied or commitment unattainable or
motivation for commitment no longer present then drop 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
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Social ConventionsInvoke when: Inter-dependent goalsInter-dependent goals local commitment dropped local commitment satisfied motivation for local commitment no longer present
R1: if local commitment satisfied or local commitment dropped or
motivation for local commitment no longer present then inform all related commitments
Invoke when: Joint commitment to a joint goalJoint 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: if status of commitment to joint goal changes or status of commitment in the team context changes then inform all other team members of the change
R2: if status of commitment of another team member changes then determine whether joint commitment is still viable
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3.3 An example of joint action and conventions3.3 An example of joint action and conventionsGRATE System (Generic Rules and Agent model Testbed Environment, Jennings, 1994)
ARCHON electricity distribution managementcement 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 agentsAAA - 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
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COOPERATIONMODULE
SITUATIONASSESMENT
MODULE
AcquaintanceModels
SelfModel
Informationstore
CONTROL MODULE
Task2 Task3Task1
Communication ManagerInter-agentcommunication
Cooperation &Control Layer
Domain LevelSystem
CONTROLDATA
GRATE Agent ArchitectureGRATE Agent Architecture
(a) Agent behavior(a) Agent behavior1. 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 organiser3.1. Establish joint action - the organiser carries on the distributed
planning protocol3.2. Perform individual actions in joint action3.3. Monitor joint action
4. if (b) then perform individual actions
5. if request for joint action then the agent becomes team-member5.1. Participate in the planning protocol to establish joint action5.2. Perform individual actions in joint actions
(3.2 and 5.2 adequately sequenced)
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(b) (b) Establish joint actionEstablish joint actionGRATE Distributed Planning ProtocolPHASE 11. Organiser detects need for joint action to achieve goal G and determines that
plan P is the best means of attaining it - SAM2. Organiser contacts all acquaintances capable of contributing to P to determine
if they will participate in the joint action - CM3. Let L set of willing acquaintances
PHASE 24. for all actions B in P do
- select agent AL to carry out action B- calculate time tB for B to be performedbased 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
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Agent A1. Evaluate proposal (B, tB) againstexisting commitments2. 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 C4. 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
Joint intention - Phase 1 for agent AAA
Name: Diagnose-faultMotivation: Disturbance-detection-messagePlan: { S1: Identify_blackout_area, S2: Hypothesis_generation,
S3: Monitor_disturbance, S4: Detailed_diagnosis, S2 < S4}Start time: Maximum end time:Duration: Priority: 20Status: Establish groupOutcome: Validated_fault_hypothesisParticipants: ((Self organiser agreed_objective)
(CSI team-member agreed_objective) (BAI team-member agreed_objective))
Bindings: NILProposed contribution:
((Self (Hypothesis_generation yes) (Detailed_diagnosis yes)) (CSI (Monitor_disturbance yes) (BAI (Identify_blackout_area yes)))
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Joint action - Phase 2 for agent AAAName: Diagnose-faultMotivation: Disturbance-detection-messageStatus: Establish planStart time: 19Maximum end time: 45Bindings: ((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 areaName: Achieve Identify_blackout_areaMotivation: Satisfy Joint Action Diagnose-faultStart time: 19 Maximum end time: 34Duration: 15 Priority: 5Status: PendingOutcome: Blackout_area
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(c) Monitor the execution of joint action(c) Monitor the execution of joint actionRecognize situations that change commitments and impact joint actionR1match: if task t has finished executing and
t has produced the desired outcome of the joint action then the joint goal is satisfiedR2match: 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 conventionsR1inform: if joint action has successfully finished then inform all team members of successful completion and
see if result should be disseminated outside the teamR2inform: 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………..
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ReferencesReferences 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.
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