WP5: Trust

WP description: People involved: Carles Sierra (WP leader) Jordi Sabater-Mir Marco Schorlemmer Eva Armengol Isaac Pinyol (PhD student) Manuel Atencia (PhD student) Adrian Perreau de Pinninck (PhD student) Andrew Koster (PhD student) Inspired by social theories and information theory, we will deepen in the most advanced currently developed computational trust and reputation models that will permit to model social security.

Different approaches to control the interaction Security approach Institutional approach Social approach Trust and reputation mechanisms are at this level. They are complementary and cover different aspects of interaction.

At an individual level, computational trust and reputation models are used by software agents: As a mechanism for partner selection As an incentive in the decision making process to honour a contract. At a collective level: They act as control mechanisms to avoid frauds and isolate individuals with a non desireable behaviour.

Characteristics of trust and reputation mechanisms Each agent is a norm enforcer and is also under surveillance by the others. No central authority needed. Their distributed nature allows to arrive where laws and central authorities cannot. Punishment is based usually in ostracism. Therefore, exclusion must be a punishment for the outsider.

Computational trust and reputation mechanisms Objective: Integrate different mechanisms (social, information-based, semantics) to improve security in virtual societies. Existing models – Game theory – Decision theory – Social Network Analysis – Cognitive theories – Probabilistic/possibilistic

Task 5.1 – Dynamic semantic alignment of trust and reputation models Motivation: A lot of models. Each model is associated with a concrete conception of how to deal with trust and reputation elements. Even the basic notions of what reputation or trust is differs from model to model. Previous work: Static common ontology for trust and reputation.

Task 5.1 – Dynamic semantic alignment of trust and reputation models We propose to semantically align trust and reputation models at run time and relative to the particular interaction the agents are engaged with. This has the advantage of capturing the subjective view of trust and reputation of an agent and enabling a more accurate alignment for that particular interaction.

Task 5.1 – Dynamic semantic alignment of trust and reputation models Technologies: Computational trust and reputation models Semantic alignment of ontologies (WP1) Argumentation (WP4) TASK 5.1 Learning (CBR)(T4.3)

Task 5.1 – Dynamic semantic alignment of trust and reputation models Plan for the first year: –Analysis of current static ontologies on trust and reputation. –Definition of the shared commodity on which the semantic alignment will be based. –Definition of an argumentation language for trust and reputation. –Definition of several scenarios for testing. Deliverables: –D5.1.1 Formalization of the trust and reputation alignment problem (Month 12)

Task 5.1 – Dynamic semantic alignment of trust and reputation models Andrew Koster. New PhD Dr. Marco Schorlemmer PhD Manuel Atencia Dr. Jordi Sabater-Mir (Responsable de tarea) PhD Isaac Pinyol TASK 5.1 Prof. Carles Sierra Dr. Eva Armengol ????????

Task 5.2 – Integrated trust model Motivation: Help negotiation tasks: what offer to select? What negotiation partner to select? How much details do we include in agreements? Previous work: Game theoretical models, information-based models, possibilistic models, social approaches.

Objectives: –Thorough analysis of the current models –Generate an integrated model for negotiation to help on dialogue moves in a normative context (WP2, WP3). Technologies: –Information theory –Game theory –Social network analysis Task 5.2 – Integrated trust model

Phase 1 (M 12): –Current SOA –Requirements for an integrated model Phase 2 (M 24, 48): –Integrated model of trust –Software component design –Evaluation in the case studies Task 5.2 – Integrated trust model

People –Carles Sierra –Nardine Osman –John Debenham Connections –LiquidPub –ARC grant –OK2 Proposal Task 5.2 – Integrated trust model

Related work ReGreTRepage Reputation model Witness reputation System reputation Neigh- bourhood reputation ODB Direct Trust Credibility IDBSDB Trust The ReGreT system