1. Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA) Juan Antonio Rodríguez (IIIA) WP3: Organisations www.agreement-technologies.org

2. WP3: Organisations WP1 9% WP2 8% WP3 21% WP4 16% WP5 7% WP6 12% WP7 11% WP8 10% WP9 2% WP10 4% WP1 WP2 WP3 WP4 WP5 WP6 WP7 WP8 WP9 WP10 Topics addressed: Organisation-centric point of view: How to design / learn, instantiate and evolve complex org. structures ? Agent-centric point of view: How to act alone / as part of a team within complex org. structures? User-centric point of view: How to assure the usability of complex org. structures?

3. WP3: Organisations Tasks: T3.1: Autonomic Electronic Institutions T3.2: Group Planning T3.3: Deliberative Agreement T3.4: 3D Electronic Institutions T3.5: Organisational Teamwork 0 10 20 30 40 50 60 70 80 90 Task 3.1Task 3.2Task 3.3Task 3.4Task 3.5 IIIAUPVURJC Management: Identify interrelations between WP3 tasks so as to avoid duplication of work/ identify synergies

4. Juan Antonio Rodríguez Aguilar (IIIA) Autonomic Electronic Institutions www.agreement-technologies.org Task 3.1

5. Autonomic Electronic Institutions Introduction: Aims and scope Activities Planning Overview

6. Introduction Research issue: How to endow an agent organisation or institution with autonomic capabilities to yield a dynamical answer to changing circumstances through the adaptation of its norms. « An autonomic computing system must configure and reconfigure itself under varying (and even unpredictable) conditions. System configuration or "setup" must occur automatically, as well as dynamic adjustments to that configuration to best handle changing environments. »

7. Introduction Example: Supply chain automation

8. Activities Negotiation models and strategies for self- configuration –Flexible negotiation models to reach agreements on the fly to respond to new goals Connections: –Task 3.5 in WP3 –Input: Techniques from WP2 and WP4 –Output: eProcurement demonstrator in WP8 Background: Work at IIIA on supply chain formation Current team: Jesús Cerquides, Jar

9. Activities Learning norm reconfiguration mechanisms in cooperative and competitive scenarios –to agree on how to respond to changing circumstances Connections: –Task 3.5 in WP3 –Output: eProcurement demonstrator in WP8 Background: Results of TIN project on “Autonomic Electronic Institutions Current team: –N.Salazar, J.L.Arcos, Jar: Evolutionary approach –J.L.Fernández, J.L.Arcos: Under construction –M.Vinyals, J.Cerquides, Jar: Under construction

10. Plan Activity M0M6M12M18M24M30 Negotiation models for norm agreement Empirical evaluation (configuration models) Learning models for reconfiguration Empirical evaluation (reconfiguration models)

11. Eva Onaindia (UPV) Agreement Planning www.agreement-technologies.org Task 3.2

12. Task 3.2 Agreement planning WP3: Organisations (Leader: Sascha Ossowski – URJC) Task 3.1. Autonomic Electronic Institutions.) Task 3.2: Agreement Planning (DESIGN, ORGANIZATION, MODEL) Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS Task 3.4 3D Electronic Institutions (3DEI) Task 3.5: Mechanisms for Efficient Organisational Teamwork WP4: Argumentation and negotiation (Leader: Lluís Godo – IIIA) Task 4.1. Agreement Logics Task 4.2. Real-Time agreements Task 4.3. CBR-based Mediating Agent Task 4.4 Planning and scheduling capabilities for an agent (SOLVING TECHNIQUES, COMPUTATION) Task 4.5 Agreement management with Data Mining

13. What is agreement planning? Planning by negotiation or Negotiation by planning? 1. Planning is the problem, negotiation is the technique (new planning framework) 2. Negotiation is the problem, planning is the method (traditional view of planning for solving a particular problem)

14. Planning decisions = agreements Which (actions … different alternatives) When (temporal allocation of actions) How (resources) And also … –Negotiation cycle + planning + execution –Time to reach the agreements (planning time) – ….

15. Where planning meets negotiation? PLANNINGNEGOTIATION AGENTS Single-agentMultiple planning entities (humans, soft agents, …) Collaborative framework Distributed planning Multi-agent planning KNOWLEDGE Perfect knowledgeGlobal knowledge/Partial knowledge Sharable / Private / Hidden Distributed planning GOALS Joint goalsJoint goals  collaborative work, joint negotiation for a common global purpose Independent goals  collaborative work, individual negotiation for individual interests Single-agent goal  collaborative work (??) Independent goals  collaborative + individual work (do one’s own thing) OPTIMIZATION Time, resourcesNegotiation elements

16. Task 3.2: Workplan Project report: –D3.2.1: Group planning agreements M36 Our proposal: Study the relation between planning and negotiation –M8: Analysis and identification of components Agents, Knowledge, Goals, Optimization Survey –M12: Discussion with “negotiation” people Planning needs introduced by negotiation Planning agreements between agents

17. Enric Plaza (IIIA) Deliberative Agreement www.agreement-technologies.org Task 3.3

18. Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS Objectives. The overall goal of this task is to analyze properties and develop mechanisms for collective decision making in human and artificial agents. The emphasis will be on tasks that require complex agreements, i.e. involving at least one of the following: (i) argumentation processes for deliberation, (ii) aggregation of sets of interconnected judgments or (iii) organizational division of labour. Technologies. This goal requires the integration of relevant contributions in social choice theory, argumentation models, aggregation procedures, organizational and institutional models.

19. Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS Deliverable D3.3.1 Requirement analysis for deliberative agreement. Month 12 Requirement Analysis: Study of relevant aspects in social choice theory, argumentation models, aggregation procedures, and organizational and institutional models. Year 1 Focus: while social choice models typically focus on aggregating individual preferences, we will focus on judgment aggregation. Judgment aggregation is new field that aims at finding collective judgments on logically interconnected propositions. Aggregation procedures for interconnected judgments.Month 24 Deliberation and aggregation for interconnected judgments.Month 40 Institutional models for deliberative agreementMonth 54 Years 2-5

20. Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS As an example of application, we consider the management of a common resource, namely the aquifers of a region, and the joint decision is whether or not a shortage of water for consumption is likely (e.g. to implement a ruling that changed the normal exploitation regime to a restricted exploitation regime) Any group that attempts to manage a common resource (e.g., aquifers, judicial systems, pastures) for optimal sustainable production must solve a set of problems in order to create institutions for collective action.

21. Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS Interconnected causal judgments.

22. Marc Esteva (IIIA) 3D Electronic Institutions www.agreement-technologies.org Task 3.4

23. 23 T3.4 3D Electronic Institutions 3D Virtual Worlds Social Personalized experience Location awareness Unstructured interactions No methodological support

24. T3.4 3D EI Example

25. T3.4 Goal Facilitate the integration (participation) of humans into MAS. –Graphical visualization of norms and interaction context –Humans and software agents collaboration. Extending the application domains of Virtual Worlds

26. T3.4 Goal Definition of a methodology for the construction of 3D Electronic Institutions : –specification of institutional rules –design and development of the Virtual World Development of algorithms and software tools to support the methodology Autonomic 3D Electronic Institutions

27. T3.4 Run Time Architecture

28. T3.4 3D EI In collaboration with the University of Technology Sydney, University of Western Sydney and University of Barcelona. New PhD student: Tomas Trescak (co- supervised with Inmaculada Rodriguez)

29. 29 T3.4 WorkPlan Goal: development of the execution environment for 3DEI. –T1. Study of available Virtual Worlds Clients and selection of what to use in this project. Definition of 3DEI in the representation language of the chosen VW client. (Feb - May 2008). –T2. Adaptation of the map generation algorithm (Jun - Jul 2008) –T3. Implementation of the Causal Connection Server to connect AMELI with the chosen VW Client (Jul – Oct 2008) –T4 Deployment and testing of the system (Oct 2008 – Jan 2009) –T5. Norm and Context Visualization (Nov 2008 – Jan 2009)

30. Sascha Ossowski (URJC) Mechanisms for Efficient Organisational Teamwork www.agreement-technologies.org Task 3.5

31. Task 3.5: Characteristics Objectives (DoW): to study how organizational structures can improve and accelerate co-ordination processes in open environments. to study the effect of organizational regulation on the quality and flexibility of teamwork Activities (DoW): Design and implementation of micro-level mechanisms Design and implementation of macro-level mechanisms Deliverables (DoW): : D3.5.1 : Design and analysis of organisational structures. M18. D3.5.2 : Micro-level mechanisms M24. D3.5.3 : Macro-level mechanisms M40. D3.5.4 : Implementation of mechanisms. M54.

32. Task 3.5: Previous work Organisational structures: Decision making in teamwork  Social dependence networks  Mapping to bargaining theory Abstraction for design/specification  Model MAS/DPS structures in terms of roles, interactions, etc.  Specify dynamics in terms of role-playing relations etc. Similarity measure for trust models  Determine confidence in similar roles etc. (e.g. for bootstrapping) Service descriptions in SOMAS  Role-based matchmaking  Service composition filters

33. Task 3.5: Research lines Challenges regarding organisational structures: Ch1: Language – Beyond roles and interaction hierarchies Ch2: Model – Design vs. learning of organisational structures Ch3: Exploitation – Beyond partner selection in two-sided interactions Activities: A1: Study of complex org structure models (Ch1) A2: Learning (extensions of) org. ontologies (Ch2) A3: Implications for teamwork planning: trust, filtering, … (Ch3) A4: Org. structures for Probability Collectives (Ch3) A5: Effect of run-time org. information on the efficiency of teamwork (Ch3) A6: Adapt/extend simulation environments (Ch2+3)

34. Task 3.5: Workplan Activity M0M6M12M18M24M30 A1: Study of complex org structure models A2: Learning org. ontologies A3: Implications for teamwork planning A4: Org. structures for Prob. Collectives A5: Effect of run-time org. information A6: Simulation environments

35. Task 3.5: Relation to other WPs/Tasks “Strong”: T3.2: Group Planning (Eva Onaindia) T6.1: Design of a MAS methodology based on org. concepts (C.A. Iglesias) WP5: Trust (Carles Sierra) T1.3: Scalable Methods for Semantic Service Coordination (Alberto Fernández) “Medium”: T3.3: Deliberative Agreement (Enric Plaza). T3.1: Autonomic Electronic Institutions (Juan Antonio Rodriguez) T2.2: Individual Reasoning over normative systems (Pablo Noriega) T2.3: Declarative Specification of EIs (Marc Esteva) Other WP6+7: Tools + Infrastructure

36. Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA) Juan Antonio Rodríguez (IIIA) WP3: Organisations www.agreement-technologies.org