Andrew Koster

Liar liar, pants on fire; or how to use subjective logic and argumentation to evaluate information from untrustworthy sources

This paper presents a non-prioritized belief change operator, designed specifically for incorporating new information from many heterogeneous sources in an uncertain environment. We take into account that sources may be untrustworthy and provide a principled method for dealing with the reception of contradictory information. We specify a novel Data-Oriented Belief Revision Operator, that uses a trust model, subjective logic, and a preference-based argumentation framework to evaluate novel information and change the agent’s belief set accordingly. We apply this belief change operator in a collaborative traffic scenario, where we show that (1) some form of trust-based non-prioritized belief change operator is necessary, and (2) in a direct comparison between our operator and a previous proposition, our operator performs at least as well in all scenarios, and significantly better in some.

Using trust and possibilistic reasoning to deal with untrustworthy communication in VANETs

VANETs allow for unprecedented amounts of information to be sent between participants in traffic. Unfortunately, without countermeasures, they also allow selfish agents to take advantage of communication to improve their own utility. In this paper we present a novel framework for dealing with potentially untrustworthy information. The framework consists primarily of two components: a computational trust model for estimating the amount of uncertainty in received information and a possibilistic beliefs-desires-intentions agent system for reasoning about this uncertain information in order to achieve the drivers goals. We demonstrate the frameworks effectiveness in an easy to understand but realistic scenario of a freeway system in which we also show that deceit may have a larger impact on traffic flow than previously thought.

Social Computing in Digital Education

This work presents amodel for the selection of Collaborative Learning (CL) techniques considering specific characteristics and needs of the activity that teachers want to perform within their educational practice. This model considers the representation of the activity in terms of the required competencies defined from Bloom’s taxonomy. Then, using the characterization of a set of techniques conducted by experts, an algorithm is used for providing an affinity measure, doing a recommendation of the technique to use. A validation of the model from three case studies is also described, carried out by comparing experimental and control groups. The results show that CL allows for achieving better academic performance, but also that those techniques proposed by the recommendation model exhibited higher performance.

Trust and argumentation in multi-agent systems

This survey is the first to review the combination of computational trust and argumentation. The combination of the two approaches seems like a natural match, with the two areas tackling different aspects of reasoning in an uncertain, social environment. We discuss the different areas of research and describe the approaches taken so far, analysing both how they address the problems and the challenges that are unaddressed.

Incentivising Crowdsourced Parking Solutions

The problem of finding parking slots imposes both societal and infrastructural issues in modern cities. It is a daily hurdle that affects millions of people, but existing approaches fail to solve this conundrum. Thus, there is an urgent demand for reputable, motivated, and replicable solutions that can be used by cities of any size. We are proposing an experiment to analyse the interplay between incentive mechanisms, user participation, and the truthfulness of reports. For that, we are developing the “wePark application” based on concepts of crowd sourcing and social regulation. As a differential, we are examining alternative methods to motivate adoption, such as reciprocity, reputation, altruism, and money. In this paper, we analyse the requirements of the solution, propose a development test bed, and an experimental environment for this study.

Advances in Social Computing and Digital Education

SCALA system is an effort to integrate Internet of Things (IoT) and Social Computing to provide an alternative communication system to improve literacy of people with autism. After introducing SCALA, this work presents a review on the state of the art about the application of IoT technologies to Autism, carried out in five international scientific databases. Based on this study, it is proposed a model for integrating SCALA to IoT with a focus on questions about the literacy process for children with autism. SCALA currently operates on web, desktop and tablet platforms. The proposed technological integration with IoT aims to bring additional functionalities for SCALA, which will enable mediation with its subjects by means of physical objects. Results can be of use for developers of similar systems.

Towards a Platform for Testing and Developing Privacy-Preserving Data Mining Applications for Smart Grids

In this paper we analyse the trade-off between privacy-preservation methods and the quality of data mining applications, within the specific context of the smart grid. The use of smart meters to automate data collection is set to solve the problem of electricity theft, which is a serious concern in developing nations. Nevertheless, the unlimited use of data from smart meters allows for potentially private information to be discovered. There is a demand for methods to quantify the trade-off between privacy-preservation and quality of a classification model. We describe the research and development of an agent-based simulation platform to evaluate the balance between privacy-preservation mechanisms and methods for electricity theft detection. We have implemented a proof-of-concept model and validated it against real data collected from smart meters.