Towards a Model of Social Coherence in Multi-Agent Organizations

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We propose a social coherence-based model and simulation framework to study the dynamics of multi-agent organizations. This model rests on the notion of social commitment to represent all the agents’ explicit inter-dependencies including roles and organizational structures. A local coherence-based approach is used that, along with a sanction policy, ensures social control in the system and the emergence of social coherence. We illustrate the model and the simulator with a simple experiment comparing two sanction policies.

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Towards a Model of Social Coherence in Multi-Agent Organizations

  1. 1. Towards a Model of Social Coherence In Multi-Agent Organizations Erick Martínez Ivan Kwiatkowski Philippe Pasquier {emartinez, pasquier}@sfu.ca
  2. 2. Contributions● Model ● Operational model where agent behaviour is driven by tractable coherence calculus ● Local coherence-driven agent behaviour drives the dynamics of multi-agent organizations; from where social coherence emerges ● Local coherence calculus of agents incorporates sanction policies● Implementation ● Java-based simulation framework for studying the dynamics of social systems● Experiments ● We illustrate our model by running some preliminary experiments, and contrasting two different sanction policies {emartinez, pasquier}@sfu.ca 2 / 21
  3. 3. Social ModellingRole =〈 Actions , SocCommitmentSchema〉Ag =〈 Roles Ag , Agenda , Prob Ag action〉Org =〈 Roles , Agents , f assign : Agents Roles〉 Customer Tech Cook Delivery Pizzeria (Organization) Pizza Delivery Example {emartinez, pasquier}@sfu.ca 3 / 21
  4. 4. Actions & Exogenous EventsActions: Exog. Events:● Performed by agents ● Not necessarily performed by agents● Discreet, instant-based, sequential model of time ● Periodicity > 0, max. period within which the● Duration time > 0 event will occur once 〈 orderPizza  , 1〉 x 〈 cleanOven , 5〉 x exog 〈 becomeHungry  , 5〉 x 〈 repairOven  , 30〉 x 〈 makeOvenDirty exog  , 100〉 x 〈 cookPizza  , 7〉 x 〈 deliverPizza , 20〉 x 〈 breakOvenexog   , 200 〉 x 〈 payOrder   , 1〉 x {emartinez, pasquier}@sfu.ca 4 / 21
  5. 5. Relationships Between ActionsRelationships inspired by TÆMS taxonomy [Hörling et al., 1999] exog breakOven   x disables cookPizza   x exog becomeHungry    x enables orderPizza    x makeOvenDirty exog    x hinders cookPizza    x cleanKitchenVent    x facilitates cookPizza   x orderPizza   x enables cookPizza  x cookPizza   x enables deliverPizza   x deliverPizza   x enables payOrder   x cleanOven  x disables cookPizza  x repairOven  x disables cookPizza  x {emartinez, pasquier}@sfu.ca 5 / 21
  6. 6. Social (Action) Commitments● Oriented responsibilities contracted by debtor towards creditor● Dynamics formalized as a finite state machine (FSM) [Pasquier et al., 2006]● Commitments can be manipulated (state / transitions) {emartinez, pasquier}@sfu.ca 6 / 21
  7. 7. Social Commitment Schema (SCS)Role =〈 Actions , SocCommitmentSchema〉action0  SC debtor , creditor , action1, duration , Sanctions debtor , Sanctions creditor orderPizza     SC cook , delivery , cookPizza   , 8, S cok , S delivery  x x exogbreakOven    SC tech , cook , repairOven  , 31, S tech , S cook  x xmakeOvenDirty exog     SC cook , tech , cleanOven  , 6, S cook , S tech  x x Role Cook {emartinez, pasquier}@sfu.ca 7 / 21
  8. 8. Pizza Delivery (SCS) Work-flow exogbecomeHungry    SC customer , cook , orderPizza  , 2, S customer , S cook  x xorderPizza   SC cook , delivery , cookPizza  , 8, S cook , S delivery  x xcookPizza    SC delivery , customer , deliverPizza  , 21, Sdelivery , S customer  x xdeliverPizza   SC customer , delivery , payOrder   , 2, S customer , Sdelivery  x xbreakOven exog    SC tech , cook , repairOven , 31, Stech , Scook  x xmakeOvenDirty exog     SC cook , tech , cleanOven   , 6, Scook , Stech  x x exogbecomeHungry    orderPizza   ... x x Main work-flowcookPizza    deliverPizza   ... x x captured by SCSpayOrder    x {emartinez, pasquier}@sfu.ca 8 / 21
  9. 9. Instantiated Soc. Commitments (ISC)orderPizza    SC cook , delivery , cookPizza   , 8, S cook , S delivery  x x [t inst , t inst  duration] ISC Yves : cook , Tom : delivery , deliverPizzaα i , [ 13, 21] , {0, 0, 0}yves , {0}tom breakOven exog     SC tech , cook , repairOven  , 31, S tech , S cook  x x ISC  Lee : tech, Yves : cook , repairOvenα j , [ 18, 49] , {0, 0, 0}lee , {0}yves  {emartinez, pasquier}@sfu.ca 9 / 21
  10. 10. Social Control Mechanisms ● Sanction-based: positive & negative incentives, decided a priori, static, centralized enforcement, applied at the time of violation ● Sanctions are embedded into the life-cycle of social commitments, e.g., Sanctions Creditor Sanctions DebtorISC Yves : cook , Tom : delivery , deliverPizza α i , [13, 21], {0 F , −1 C , −1V } yves , {0C }tom  F C V CISC  Lee : tech , Yves : cook , repairOvenα j , [18, 49] , {1 , −1 , −1 }lee , {−1 } yves  {emartinez, pasquier}@sfu.ca 10 / 21
  11. 11. Sanction Policy ● Determines what sanction gets associated to what transition σ SC : T  [−1, 1] T set of transitions FSM V CD CC Fσ SC t ={s t=5 ,s t= 2 ,s t =2 ,s t =7 } {emartinez, pasquier}@sfu.ca 11 / 21
  12. 12. Constraint Between ISC ● Constraints over ISC generated automatically from relationships between actions and time interval overlapping between ISC Hard constraints Soft constraints Disabling (w = 3) Hindering (w = 1) Overlapping (w = 2.5) Facilitating (w = 1) Enabling (w = 2) F C V CISC 0 Yves : cook , Tom: delivery , deliverPizzaα i  , [13, 21], {0 , −1 , −1 }yves , {0 }tom  F C V CISC 1  Lee : tech , Yves : cook , repairOvenα j , [18, 49] , {1 , −1 , −1 }lee , {−1 }yves repairOven   disables cookPizza   − x x  generates neg.constraint C ISC 1, ISC 0  {emartinez, pasquier}@sfu.ca 12 / 21
  13. 13. Time Overlap Constraint (ISC) ● Agents level of activity: # of accepted ISCs in its agenda at any given time ● Agents cannot do more than one thing at the time F C V CISC 0 Yves : cook , Tom: delivery , deliverPizzaα i  , [ 13, 21] , {0 , −1 , −1 }yves , {0 }tom  F C V CISC 1 Yves: cook , Liz : delivery , deliverPizza α j , [ 18, 26] , {0 , −1 , −1 }yves , {0 }liz  ISC 0 « ISC 1 time overlapping constraint {emartinez, pasquier}@sfu.ca 13 / 21
  14. 14. Coherence Degree● ISCs can have weighted constraints between them● An agent will do constraint optimization over the network of ISCs (agenda) its involved in● Coherence degree: total weight of satisfied constraints between ISC in agents agenda, divided by total weight of overall constraintsCoherenceDegree  Agenda = ∑ Weight  x , y  / ∑ Weight  x , y  x , y∈Sat  Agenda  x , y∈Con Agenda {emartinez, pasquier}@sfu.ca 14 / 21
  15. 15. Expected Utility Function● The expected utility for an agent to attempt to reach state W from state W (which only differs by the change of state of a single ISC x)G W  = CoherenceDegree W  − CoherenceDegreeW  − ResToChange x , T  where : ResToChange x , T  ≡ − σ SC T  Sanction Policy● For now, no probabilities. Decision making is myopic as agents only consider cancellation penalties● Utility function can be improved by incorporating uncertainty. E.g., considering probability of failure, rewards & penalties {emartinez, pasquier}@sfu.ca 15 / 21
  16. 16. Social Coherence● In order to maximize the coherence degree of its agenda (i.e., ISCs) an agent tries to do constraint optimization● Agent cycle: 1. Calculate CoherenceDegree  Agenda 2. For each active ISC x do 3. Calculate utility of flipping ISC x 4. End For 5. Return ISC x with higher utility gain if any ● Recursive local search algorithm, no backtracking, worst-case complexity is polynomial: O(mn2) ● n is the # of ICs ● m is the # of constraints between ICs {emartinez, pasquier}@sfu.ca 16 / 21
  17. 17. SC-JSim Simulator {emartinez, pasquier}@sfu.ca 17 / 21
  18. 18. Experimental Setting● Pizza delivery organization, with 4 agents: 1 cook, 2 delivery, & 1 technician; plus several customers● Simulation parameters: periodicity & sanction policy ● Changed periodicity of event <becomeHungryexog(x), p>, with p = 80, 40, 20, 10, 5, 2, 1 time steps → increases frequency of orders ● Two sanction policies: SPol 0  S debtor = {0 F , 0C , 0V }; S creditor = { 0C } SPol 1  S debtor = {0 F , −1C , −1V }; S creditor = { −1C }● Metric: overall % of ISCs fulfilled (efficiency) {emartinez, pasquier}@sfu.ca 18 / 21
  19. 19. ObservationsObservation 1. Desirable Observation 2. The Observation 3. Under SPol1agent behaviour results from efficiency of the organization the organization was morelocal coherence degraded from nearly efficient than without anymaximization. Macro-level optimal as frequency of sanctions (i.e., Spol0). This issocial coherence does orders and agents level ofemerge from local activity was increased. because the sanction policycoherence maximization. acts as deterrence for easy cancellations. {emartinez, pasquier}@sfu.ca 20 / 21
  20. 20. Future Work● Model (extensions): ● Introducing uncertainty reasoning into the coherence calculus; reasoning about time and actions ● Modelling agents with no knowledge, with partial knowledge, or with complete/shared knowledge ● Machine learning mechanisms would allow agents to progressively learn these probabilities● Experiments: ● Impact of different organizational structures (e.g., hierarchies, holarchies, societies, federations) ● Investigate other sanction policies {emartinez, pasquier}@sfu.ca 20 / 21
  21. 21. Acknowledgements● National Sciences & Engineering Research Council of Canada (NSERC)● Marek Hatala (SIAT, SFU)● Anonymous Reviewers {emartinez, pasquier}@sfu.ca 21 / 21

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