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Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA)

WP3: Organisations. Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA) Juan Antonio Rodríguez (IIIA). www.agreement-technologies.org. WP1. WP10. WP9. WP1. WP2. WP8. WP2. 4%. 2%. 9%. WP3. 10%. 8%. WP7. WP4. 11%. WP5. WP6. WP6. WP7. 12%. WP3.

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Marc Esteva (IIIA) Eva Onaindia (UPV) Sascha Ossowski (URJC) Enric Plaza (IIIA)

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

  2. WP1 WP10 WP9 WP1 WP2 WP8 WP2 4% 2% 9% WP3 10% 8% WP7 WP4 11% WP5 WP6 WP6 WP7 12% WP3 WP8 WP5 WP4 21% 7% 16% WP9 WP10 WP3: Organisations • 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. 90 80 70 60 50 40 30 20 10 0 Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5 IIIA UPV URJC 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 • Management: • Identify interrelations between WP3 tasks • so as to avoid duplication of work/ identify synergies

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

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

  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

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

  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 foropen 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 negotiationor 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?

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

  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 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. Year 1 Requirement Analysis: Study of relevant aspects in social choice theory, argumentation models, aggregation procedures, and organizational and institutional models. Deliverable D3.3.1 Requirement analysis for deliberative agreement. Month 12 Years 2-5

  20. Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS 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. 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)

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

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

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

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

  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 regardingorganisational 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

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

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