Multi-Agent SystemsLecture 9Lecture 9

University “Politehnica” of Bucarest2007 - 2008

Adina Magda Floreaadina@cs.pub.ro

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

3

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

4

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 ...

5

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

6

(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)

7

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

8

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

9

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

10

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

11

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

12

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

13

14

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)

15

(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

16

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)))

17

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

18

(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………..

19

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.

20