Tool for simulating reputation management algorithms in multiagent systems

Efficient service-oriented inter-enterprise collaboration focuses on the business processes of the enterprises and hides the technology differences between them. However, such collaboration induces two main challenges. First, there should be an accessible and functioning infrastructure available for the collaboration. Second, as the growing number of participants can lead to the growing level of misbehaving among them, and thus market deterioration, that is why the parties should have common understanding of the behavior that is appropriate and can be trusted. We focus on the trust relationships between the agents' interactions. Agents make a trust decision before interacting, and this decision among other factors is based on an estimation of another agent's reputation. A reputation management system collects and analyzes interactions experience between agents. Let us call a person, company or any other possible entity whose goal is to build an infrastructure for the interactions between enterprises using one of the trust and reputation management algorithms as an infrastructure builder. For an infrastructure builder it is important to evaluate and compare reputation management systems, choosing one of them based on the evaluation and comparison results. This remains an open question in research. The thesis aims at supporting the decision-making process of this kind. We suggest evaluation criteria for a trust or reputation management systems' evaluation. We implement a generic tool which can plug in a trust or reputation management algorithm and simulate the behavior of the multiagent system where every agent follows the same algorithm. We illustrate the tool's support for some of the suggested evaluation criteria. We provide some recommendations for further development of the generic tool for evaluating and comparing the above-mentioned behavior characteristics of different trust and reputation management algorithms. ACM Computing Classification System (CCS): Human-centered computing → Collaborative and social computing → Collaborative and social computing systems and tools → Reputation systems Computing methodologies → Artificial intelligence → Distributed artificial intelligence → Multi-agent system

The enhancement of collaborative learning through integrated knowledge management systems: E-learning model

There are still a few educational platforms that apply a Knowledge Management System (KMS) concept in conducting its operational work. In addition, several obstacles associated with e-learning implementation trigger the in-effectiveness of collaborative learning. However, the concept of Knowledge Management (KM) from a Sharia perspective has significant implications for education systems. This research, therefore, explored the relevance of the Learning Management System (LMS), KM theory, and Sharia education perspective on the development of the Integrated Knowledge Management System (IKMS) Framework. The IKMS components and structures are literature reviewed and then qualitatively justified through the focus group discussion which involved some students, lectures, and experts from two Sharia-based Universities in Indonesia. To verify and test the framework, an IKMS-Edu system was developed by focusing on the adoption of a controlling agent system in the online discussion. Herein, filtering and summarization technology was embedded into IKMS-Edu towards a smart controlling agent. This agent adopted the operational work of IKMS-Edu framework leveraging in four constructs activities viz., knowledge creation and knowledge acquisition (construct 1), knowledge organization and knowledge storage (construct 2), knowledge dissemination and knowledge retrieval (construct 3), and knowledge evaluation and feedback (construct 4). To date, the statistical evaluation of the IKMS-Edu system’s acceptance is conducted by disseminating the questionnaires. The mean scores revealed 40.45% of the respondents strongly agreed, and 42.18% agreed on the proposed framework and prototype system thus the framework aided in performing the IKMS during the collaborative learning activities. As such, this evidence provides the strong support that IKMS-Edu significantly enhanced the effectiveness of collaborative learning by considering the Sharia values of trust, knowledge, virtue, psychosocial, and civilization development into knowledge management activities

Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200