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Dissertation

Sistema de Medida de Confianca para Selecc ¸ ˜ ao de Empresas em Sistemas B2B

TL;DR: The designing and specification of a trust and contextual information aggregation model, intended to be a reliable alternative to the trust aggregation models already existing, and trying to set apart from those by including rules to emulate human common sense regarding trust building, and mechanisms to obtain a recommendation grade concerning how likely is a potential partner to perform as the authors desire in the fulfilment of a given contract.
Abstract: The study of trust aggregation mechanisms to assist the selection of companies in Business-to-Business systems, is becoming increasingly important to researchers in the areas of Multi-Agent Systems and Electronic Business, because it has been proved that it can provide means to increase the performance and reliability of the existing electronic business communities, by endowing them with human-like social defence mechanisms. The study we present in this document concerns the designing and specification of a trust and contextual information aggregation model, intended to be a reliable alternative to the trust aggregation models already existing, and trying to set apart from those by including rules to emulate human common sense regarding trust building, and mechanisms to obtain a recommendation grade concerning how likely is a potential partner to perform as we desire in the fulfilment of a given contract. This dissertation has three main parts. In the first, we present the trust and contextual information model, showing how we use an S-shaped curve to aggregate the past contract results of a given entity. From there we can retrieve a degree of trust which represents, in an abstract and simplified way, how likely is a given entity to fulfil the next contract, given how well she fulfilled the previous ones. The aggregation of contextual information can act as a disambiguation tool, because the information of the past contracts is treated concerning the context in which they were celebrated, providing, this way, a mean to assess if a given company is the most adjusted to do business with, regarding the specificities of the contract, and independently of how much trust do we deposit in them. In the second part we specify the application that we developed to simulate the process of company selection. This application implements the models that we propose as solution together with a third one, developed by another research group, to compare the performance and utility of our model. We simulate a fabric market, in which a group of buyers needs to buy certain quantities from sellers. In this process, each buyer is going to need the degree of trust and the degree of recommendation for each candidate seller, deciding which one(s) to buy from depending on that information. In the third part we demonstrate and analyze the results that we got from the simulations we have made. We developed three kinds of validation tests for the models: how fast were they identifying the companies violating fewer contracts, how well they react to an abrupt company behaviour change, and how much will the use of a recommendation grade affect the process of selecting a business partner. The results we got from the simulations show that our system for trust and contextual information measure represents a reliable option as a trust aggregation models, since, when compared to other model, it proves to be capable of selecting more times the best business partner, which understandably ends up in fewer violated contracts by the selected seller and higher business utility for the buyer.

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Book
01 Jan 2020
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Abstract: The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

16,983 citations

Journal ArticleDOI
TL;DR: In this paper, a model is presented to account for the natural selection of what is termed reciprocally altruistic behavior, and the model shows how selection can operate against the cheater (non-reciprocator) in the system.
Abstract: A model is presented to account for the natural selection of what is termed reciprocally altruistic behavior. The model shows how selection can operate against the cheater (non-reciprocator) in the system. Three instances of altruistic behavior are discussed, the evolution of which the model can explain: (1) behavior involved in cleaning symbioses; (2) warning cries in birds; and (3) human reciprocal altruism. Regarding human reciprocal altruism, it is shown that the details of the psychological system that regulates this altruism can be explained by the model. Specifically, friendship, dislike, moralistic aggression, gratitude, sympathy, trust, suspicion, trustworthiness, aspects of guilt, and some forms of dishonesty and hypocrisy can be explained as important adaptations to regulate the altruistic system. Each individual human is seen as possessing altruistic and cheating tendencies, the expression of which is sensitive to developmental variables that were selected to set the tendencies at a balance ap...

9,318 citations

Book
01 Jan 2017

2,909 citations

Book ChapterDOI
31 Oct 2002
TL;DR: Examination of a large data set from 1999 reveals several interesting features, including a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.
Abstract: One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance, though eBay's net feedback statistic is far from the best predictor available. Fourth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.

1,948 citations

Journal ArticleDOI
TL;DR: A research model for explaining trust in global virtual teams is advanced, and strategies that were used by the three highest trust teams, but were used infrequently or not at all by theThree lowest trust teams suggest the presence of "swift" trust.
Abstract: A global virtual team is an example of a boundaryless network organization form where a temporary team is assembled on an as-needed basis for the duration of a task and staffed by members from different countries. In such teams, coordination is accomplished via trust and shared communication systems. The focus of the reported study was to explore the antecedents of trust in a global virtual-team setting. Seventyfive teams, consisting of four to six members residing in different countries, interacted and worked together for eight weeks. The two-week trust-building exercises did have a significant effect on the team members' perceptions of the other members' ability, integrity, and benevolence. In the early phases of teamwork, team trust was predicted strongest by perceptions of other team members' integrity, and weakest by perceptions of their benevolence. The effect of other members' perceived ability on trust decreased over time. The members' own propensity to trust had a significant, though unchanging, effect on trust. A qualitative analysis of six teams' electronic mail messages explored strategies that were used by the three highest trust teams, but were used infrequently or not at all by the three lowest trust teams. The strategies suggest the presence of "swift" trust. The paper advances a research model for explaining trust in global virtual teams.

1,931 citations