Bootstrapping trust evaluations through stereotypes

Publisher PD

Trustworthy advice

Abstract 'If you go to Ferran Adria's restaurant you will have the time of your life!' 'If you study everyday for two hours you will get very good marks next semester.' These are examples of advice. We say an advice has two components: a plan to perform and a goal to achieve. In dynamic logic, an advice could be formalised as: [Pη]G. That is, if η performs plan P, then goal G will necessarily be achieved. An adviser is an entity which provides such advice. An adviser may be an agent, a planner, or a complex recommender system. This paper proposes a novel trust model for assessing the trustworthiness of advice and advisers. It calculates the expectation of an advice's outcome by assessing the probabilities of the advised plan being picked up and performed, and the goal being achieved. These probabilities are learned from an analysis of similar past experiences using tools such as semantic matching and action empowerment. © 2015 Elsevier B.V. All rights reserved.This work is supported by the PRAISE project (funded by the European Commission under the FP7 STREP grant number 318770), and the Agreement Technologies project (funded by CONSOLIDER CSD 2007-0022, INGENIO 2010).Peer Reviewe

Intelligent Agents - a Tool for Modeling Intermediation and Negotiation Processes

Many contemporary problems as encountered in society and economy require advanced capabilities for evaluation of situations and alternatives and decision making, most of the time requiring intervention of human agents, experts in negotiation and intermediation. Moreover, many problems require the application of standard procedures and activities to carry out typical socio-economic processes (for example by employing standard auctions for procurement or supply of goods or convenient intermediation to access resources and information). This paper focuses on enhancing knowledge about intermediation and negotiation processes in order to improve quality of services and optimize performances of business agents, using new computational methods that combine formal methods with intelligent agents paradigm. Taking into account their modularity and extensibility, agent systems allow facile, standardized and seamless integration of negotiation protocols and strategies by employing declarative and formal representations specific to computer science.Business processes, Intelligent Agents, Intermediation and Negotiation, Formal Models.

Evaluating online trust using machine learning methods

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation model to evaluate recommenders\u27 trustworthiness and expertise; and constructs a distributed trust system and a global reputation model to achieve efficient trust computing and management. The experimental results show that the proposed three trust models are reliable. The contributions lie in: (1) novel application of neural networks in recommendation trust evaluation and distributed trust management; (2) adaptivity of the proposed neural network-based trust models to accommodate dynamic and multifacet properties of trust; (3) robustness of the neural network-based trust models to the noise in training data, such as deceptive recommendations; (4) efficiency and parallelism of computation and load balance in distributed trust evaluations; and (5) novel application of Hidden Markov Models in recommenders\u27 reputation evaluation