1. Multi-Agent Systems University “Politehnica” of Bucarest Spring 2010 Adina Magda Florea http://turing.cs.pub.ro/mas_10 curs.cs.pub.ro

2. Lecture 6: Agent coordination Lecture outline 1 Coordination strategies 2 BDI Logic 3 Modeling coordination by shared mental states 4 A system with joint actions and conventions

3. 3 1 Coordination strategies n Coordination n Coordination = the process by which an agent reasons about its local actions and the (anticipated) actions of others to try to ensure that the community acts in a coherent manner Coordination Collectively motivated agents common goals Self-interestedagents own goals Cooperation to achieve common goal Coordination for coherent behavior Neutral to one another disjunctive goals Competitive conflicting goals

4. 4 n Perfect coordination ??? n Centralized coordination ? n Distributed coordination o Model o Protocol o Communication n Tightly coupled interactions - distributed search n Complex agents - distributed planning, task sharing, resource sharing n Heterogeneous agents - interaction protocols: Contract Net, KQML conversations, FIPA protocols n Dynamic interactions - Meta-level information exchange, commitments and conventions n Complex interactions - organizational structures to reduce complexity n Unpredictable interactions - social laws n Conflict of interests - interaction protocols: voting, auctions, bargaining, market mechanisms, Contract Net, coalition formation Cooperative Neutral or competitive

5. 5 2 BDI logic L B - set of moment formula L S - set of path-formula Modal operators Bel, Des, Int, (K w ) L I based on L B (formula) and L S (path) A - set of agents if p  L S and x  A then xBelp, xIntp, xDesp, xK w p  L I M =

6. 2.1 BDI operators B - belief accessible relation - belief accessible worlds; the worlds the agent believes possible D - desire (goal) accessible relation  Each situation w t has associated a set of desire (goal) - accessible worlds - realism I - intention accessible relation  Intentions - sets of intention-accessible worlds - these are the worlds the agent has committed to realize.  Corresponding to each goal-accessible world w at some time t there must be an intention-accessible world that is a subworld of w at time t Ex: xDes(A Fwin)  xInt(E Fwork)   xBel(A Fwin) 6

7. rsrs s sqsq rsrs psqpsq rsqrsq s rsrs psqpsq s belief accessible world goal accessible world intention accessible world 7 rsrs rsqrsq r - Alice is in Italy p -Alice visits Paris s – Paris is the capital of Franceq - it is spring time

8. M |= t x Bel p iff (  t': (t,t')  B(x,t)  M |= t' p) an agent x has a belief p in a given moment t if and only if p is true in all belief accessible worlds of the agent in that moment M |= t x Des p iff (  t': (t,t')  D(x,t)  M |= t' p) an agent x has a desire p in a given moment t if and only if p is true in all goal accessible worlds of the agent in that moment M |= t x Int p iff (  s: s  I(x,t)  M |= s,t Fp) at each moment t, I assigns a set of paths that the agent x has selected or preferred, i.e., if the agent has selected p as an intention, p will hold eventually in the future 8

9. Belief-goal compatibility If an agent adopts p as a goal, the agent believes that there is a path on which p will be true (as it is an adopted desire) xDesp  (xBel (E G p) Goal-intention compatibility If an agent adopts p as an intention, it should have adopted it as a goal to be achieved xIntp  xDesp No infinite deferral The agent should not procrastinate with respect to its intentions; if the agent forms an intention, then sometimes in the future it will give up this intention xIntp  A F(  xIntp)) 9 F - eventually G - always A - inevitable E - optional

10. 2.2 Commitments as change n Desires (goals) and intentions are quite similar in their semantic structure. n The difference arises in their relationships with the other modalities and in terms of how they may evolve over time. n An agent is treated as being committed to its intention but, cf. no infinite deferral, it will give up these intentions eventually - when? n Different types of agents will have different commitment strategies. 10

11. Blindly committed agent maintains its intentions until it believes it has achieved them xInt(A Fp)  A (xInt(A Fp)  xBelp) Single-minded committed agent maintains its intentions as long as it believes they are still options xInt(A Fp)  A (xInt(A Fp)  (xBelp   xBel(E Fp))) Open-minded committed agent maintains its intentions as long as these intentions are still its desires (goals) xInt(A Fp)  A (xInt(A Fp)  (xBelp   xBel(E Fp   xDes(E Fp))) 11 F - eventually G - always A - inevitable E - optional

12.  Based on the view of intentional stance agents  Example of intentional coordinated action 3.1 Collective mental states Common knowledge n Every member a i in group G knows pE G p   ai  G K ai p - shared knowledge n Every member in G knows E G pE 2 G p  E G (E G p) n Every member knows that every member knows that every … E k+1 G p  E G (E K G p)k  1 n Common knowledge C G p  p  E G p  E 2 G p  …  E k G p ... 12 3 Modeling coordination by shared mental states

13. Mutual belief n E G p   ai  G a i Belp - Every one in group G believes p - shared belief n E 2 G p  E G (E G p) n E k+1 G p  E G (E K G p)k  1 n M G p  E G p  E 2 G p  …  E k G p  …- Mutual belief  Perfectly shared mental state but mutual beliefs (as common knowledge) can not be guaranteed because communication between agents is not reliable in terms of delivery and delay Joint intentions (Levesque & Cohen) C 1 ) each agent in the group has a goal that p  a i  G a i Intp (and cf goal-intentions compatibility a i Intp  a i Desp) C 2 ) each agent will persist with this goal until it is mutually believed that p has been achieved or that p cannot be achieved  a i  G a i Int (A Fp)  A ( a i Int(A Fp)  (M G (Achieve p)  M G (  Achieve p))) C 3 ) conditions (C 1 ) and (C 2 ) are mutually believed M G (C 1 )  M G (C 2 ) 13

14. Commitments Blindly committed agent Single-minded committed agent Open-minded committed agent Joint commitments Agents in the group: the state of joint commitment is distributed have a joint goal the group becomes jointly committed agree they wish to cooperate to achieve the joint goal Joint intentions can be seen as a joint commitment to a joint goal while in a certain shared mental state 14

15. 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 n Commitments n 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 n Conventions n Conventions constrain the conditions under which commitments should be reassessed and specify the associated actions that should be undertaken: retain, rectify or abandon the commitment  the necessary degree of mutual support 15

16. Specifying conventions Reasons for re-assessing the commitment n commitment satisfied n commitment unattainable n motivation for commitment no longer present Actions R1: if commitment satisfied or commitment unattainable or motivation for commitment no longer present then drop commitment p 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 16

17. Social Conventions Invoke when: n commitment dropped commitment satisfied motivation for local commitment no longer present R1: if local commitment to joint goal G satisfied or local commitment to joint goal G dropped or motivation for local commitment to joint goal G no longer present then inform all members jointly committed to the joint goal 17

18. 4. A System with 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 18

19. 3 agents AAA - the Alarm Analysis Agent  perform diagnosis to different levels BAI - the Blackout Area Identifier- identifies environment info CSI - Control System Interface  detects the disturbance initially and then monitors the network evolving state 19

20. 20 COOPERATION MODULE SITUATION ASSESMENT MODULE Acquaintance Models Self Model Information store CONTROL MODULE Task2 Task3Task1 Communication Manager Inter-agent communication Cooperation & Control Layer Domain Level System CONTROL DATA GRATE Agent Architecture

21. 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 (next slide) 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) 21

22. 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 t B for B to be performed based on temporal orderings of P - send (B, t B ) 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 22 Agent A 1. Evaluate proposal (B, t B ) against existing commitments 2. if no conflicts then create commitment C B to (B, t B ) 3. if conflicts ((B, t B ), C) and priority(B) > priority(C) then create C B and reschedule C 4. if conflicts ((B, t B ), C) and priority(B) < priority(C) then if freetime (t B +  t) then note C B and return (t B +  t) else return reject

23. 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))) 23

24. 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)) …. n 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 24

25. Monitor the execution of joint action Recognize situations that change commitments and impact joint action R1 match : if task t has finished executing and t has produced the desired outcome of the joint action (assoc to G) then the joint goal G is satisfied R2 match : 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 R1 inform : if the joint goal G is satisfied then inform all team members of successful completion of G R2 inform : if motivation for joint goal G is no longer present then inform other team members that G needs to be abandoned 25

26. References o Multiagent Systems - A Modern Approach to Distributed Artificial Intelligence, G. Weiss (Ed.), The MIT Press, 2001, Ch.2.3, 8.5-8.7 o 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. o 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. o N.R. Jennings. Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artificial Intelligence 72(2), 1995. o E.H. Durfee. Scaling up agent coordination strategies. IEEE Computer, 34(7), July 2001, p.39-46. 26