marc esteva (iiia) eva onaindia (upv) sascha ossowski (urjc) enric plaza (iiia) juan antonio...
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Marc Esteva (IIIA)Eva Onaindia (UPV)
Sascha Ossowski (URJC)Enric Plaza (IIIA)
Juan Antonio Rodríguez (IIIA)
WP3: Organisations
www.agreement-technologies.org
WP3: Organisations
WP1
9% WP2
8%
WP321%
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?
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
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Task 3.1 Task 3.2 Task 3.3 Task 3.4 Task 3.5
IIIA UPV URJC
Management:
• Identify interrelations between WP3 tasks
• so as to avoid duplication of work/ identify synergies
Juan Antonio Rodríguez Aguilar (IIIA)
Autonomic Electronic Institutions
www.agreement-technologies.org
Task 3.1
Autonomic Electronic Institutions
• Introduction: Aims and scope
• Activities
• Planning
Overview
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. »
Introduction
• Example: Supply chain automationTier 3
Raw MaterialsTier 2
Fabrication
Tier 1Comp. Assbly
VehicleAssembly
MixingCenter
Dealers
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
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
Plan
Activity M0 M6 M12 M18 M24 M30
Negotiation models for norm agreement
Empirical evaluation (configuration models)
Learning models for reconfiguration
Empirical evaluation (reconfiguration models)
Eva Onaindia (UPV)
Agreement Planning
www.agreement-technologies.org
Task 3.2
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 LogicsTask 4.2. Real-Time agreements Task 4.3. CBR-based Mediating AgentTask 4.4 Planning and scheduling capabilities for an agent (SOLVING TECHNIQUES, COMPUTATION)Task 4.5 Agreement management with Data Mining
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)
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)– ….
Where planning meets negotiation?PLANNING NEGOTIATION
AGENTS Single-agent Multiple planning entities (humans, soft agents, …)
Collaborative frameworkDistributed planningMulti-agent planning
KNOWLEDGE Perfect knowledge Global knowledge/Partial knowledgeSharable / Private / HiddenDistributed planning
GOALS Joint goals Joint 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, resources Negotiation elements
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
Enric Plaza (IIIA)
Deliberative Agreement
www.agreement-technologies.org
Task 3.3
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.
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 agreement Month 54
Years 2-5
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.
Task 3.3. Deliberative Agreement: social choice and collective judgment models for open MAS
Interconnected causal judgments.
Marc Esteva (IIIA)
3D Electronic Institutions
www.agreement-technologies.org
Task 3.4
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T3.4 3D Electronic Institutions
Electronic Institutions•Structured Interactions•Strong methodology•Guarantees that participants comply with
institutional rules and fulfill their obligations
3D Virtual Worlds•Social•Personalized experience•Location awareness
•Unstructured interactionsNo methodological support
3D Electronic Institutions•Social•Structured, regulated interactions
T3.4 3D EI Example
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
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
T3.4 Run Time Architecture
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)
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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)
Sascha Ossowski (URJC)
Mechanisms for Efficient Organisational Teamwork
www.agreement-technologies.org
Task 3.5
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.
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
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)
Task 3.5: Workplan
Activity M0 M6 M12 M18 M24 M30
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
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
Marc Esteva (IIIA)Eva Onaindia (UPV)
Sascha Ossowski (URJC)Enric Plaza (IIIA)
Juan Antonio Rodríguez (IIIA)
WP3: Organisations
www.agreement-technologies.org