1. Governance in Multi-Agent Systems Using Testimonies to Enforce the Behavior of Agents Fernanda Duran, Viviane Torres da Silva

2. © LES/PUC-Rio Agenda Proposed Approach Proposed Architecture Proposed Framework Conclusions Next Steps

3. © LES/PUC-Rio Motivation The high complexity of recent problems that have been solved by software is leading to the development of systems that are composed by many heterogeneous subsystems designed and implemented independently; It is very important to an open system to control the agents actions in order to achieve the society goals; All agents in an environment must obey common norms to avoid conflicts between their goals and to make possible to the environment to achieve the society goals; Norms exists in order to tell what agents can do, can not do and are required to do.

4. © LES/PUC-Rio State of Art The agents’ activities control and the law enforcement are achieved by the control of the interaction between agents (message interception): Law-Governed Interaction (LGI) (Minsky e Ungureanu) Eletronic Institutions (Sierra, Rodriguez-Aguillar, Noriega, Arcos e Esteva)

5. © LES/PUC-Rio Proposed Approach Governance mechanism based on testimonies: –Action execution can be perceived as a fact or event. –This fact or event implies a norm violation; –Agents can witness these facts and they know may be related to norm violations (breaking of an interaction protocol or to a disallowed resource); –Agents can provide testimonies about actions that may be a violation of a norm; –There should exist a mechanism to judge the testimonies and apply penalties to the agent that has violated a norm. It does not actively try to prevent a norm violation. It lets the system execute normally and, if norm violations are reported, it checks and penalizes the agent that misbehaved.

6. © LES/PUC-Rio Proposed Approach (Assumptions) The approach is based on the following propositions: –Every agent knows every norm applied to itself; –Every agent can give testimonies about norm violations; –Every testimony must have a chargeable person; –Agents can give false testimony; –Some violations might be ignored or not observed; –Some violation might not be punished; –The mechanism can have a law-enforcement agent (e.g. policeman); –The strategy used by the judging mechanism is based on agent’s reputation.

7. © LES/PUC-Rio Proposed Architecture

8. © LES/PUC-Rio Proposed Framework – Judgment Subsystem

9. © LES/PUC-Rio Proposed Framework – Basic Judgment Steps /* step 1 - check if the witness is a policeman */ if witness is a policeman then create a verdict stating the defendant is 100% guilty go to step 5 /* step 2 - check if the norm applies to the defendant */ if broken law applies to a specific role then if specific broken law role is different from defendant role create a verdict stating the defendant is 100% not guilty go to step 5 /* step 3 - ask the defendant if he or she is guilty */ send a message to defendant, containing the testimony, asking if he/she is guilty about the it get the defendant's response if defendant's response is guilty then create a verdict stating the defendant is 100% guilty go to step 5 /* step 4 - continue with specific judging (APPLICATION-DEPENDANT) */ calls the action that implements this step : executeSpecificJudging() /* step 5 - sends the verdict to anyone that needs it send the verdict along with the testimony to mediator

10. © LES/PUC-Rio Instantiating the Framework

11. © LES/PUC-Rio Case Study – Expert Committee The Expert Committee is an open multi-agent system that provides support for managing paper submission and revision; The system supports different activities: paper submission, reviewer assignment, review submission, notification of acceptance and rejection and so on; Since the system being implemented is an open system, it is necessary to define a set of norms to regulate the behavior of the diverse agents (chairs, authors and reviewers) that participate in the system.

12. © LES/PUC-Rio Case Study – Expert Committee (cont.) Some laws that can be violated and witness motivation about those violations: –Ex. 1: Chair anticipates the submission deadline Some Author witness the fact. –Ex. 2: Some Researcher do not answer the Chair invitation to be a Reviewer. The Chair witness the fact. –Ex. 3: Some Author do not send the camera-ready in time. The Chair witness the fact. –Ex. 4: Chair do not send the papers to some Reviewer. The Reviser witness the fact. –Ex. 5: Some Reviewer do not send the revision in time. The Chair witness the fact.

13. © LES/PUC-Rio Case Study – Expert Committee (cont.) Ex. 1: Chair anticipates the submission 1.Some Author witness the fact; 2.The “Judge” asks the Chair about the fact; 3.The Judge may ask other authors about the fact; 4.Based on Authors' and Chair's reputation, the Judge gives its verdict; 5.The Judge sends the verdict to the Mediator.

14. © LES/PUC-Rio Case Study – Expert Committee (cont.) Chair anticipates the submission AnswerReputation Witnessguilty0.4 Defendantinnocent1 Author 1guilty0.2 Author 2guilty0.3 Innocent result: 1 * 1 = 1 Guilty result: 0.9 * 3 = 2.7 Final Result: (2.7 / (2.7 + 1)) * 100  73% Guilty ∑ reputation# testimony Innocent11 Guilty0.93

15. © LES/PUC-Rio Case Study – Expert Committee (cont.) Ex. 2: Some Researcher do not answer the Chair invitation to be a Reviewer 1.The Chair witness the fact; 2.The Judge checks over the Chair’s Invitations X Reviewer’s Invitation resource if the invitation was really sent to the researcher and if the researcher did not answer the invitation; 3.If the Chair’s Invitations and Reviewer’s Invitation are contradictory, the Judge may check over their reputation; 4.The Judge sends the verdict to the Mediator.

16. © LES/PUC-Rio Case Study – Expert Committee (cont.) Some Researcher do not answer the Chair invitation to be a Reviewer DepositionReputation Witnessguilty0.7 Defendantinnocent0.4 ∑ reputation# testimony Innocent0.71 Guilty0.41 Innocent result: 0.4 * 1 = 0.4 Guilty result: 0.7 * 1 = 0.7 Final Result: (0.7 / (0.7 + 0.4)) * 100  64% Guilty

17. © LES/PUC-Rio Conclusions Pros: –It doesn’t interfere in the agent’s privacy and in the application performance; –It can regulate not only agents’ interactions protocols but actions also (e.g. related to resource access); –The judgment subsystem is flexible enough to be customized for different applications. Cons: –It doesn’t prevent norms from being violated; –It may be difficult or impossible to judge some testimonies; –As agents are not forced to testifying, some agents may not be punished.

18. © LES/PUC-Rio Conclusions (cont.) When this approach should be used ? –In a context where norm violation prevention is not mandatory; –In a context concerned with norms related to other aspects besides interaction protocols (e.g. access to resources); –In a context concerned with privacy of the exchanged messages; –In a context where the governance aspect should not interfere in the performance aspect;