Trung Dong Huynh

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.

Certified reputation: how an agent can trust a stranger

Current computational trust models are usually built either on an agents direct experience of an interaction partner (interaction trust) or reports provided by third parties about their experiences with a partner (witness reputation). However, both of these approaches have their limitations. Models using direct experience often result in poor performance until an agent has had a sufficient number of interactions to build up a reliable picture of a particular partner and witness reports rely on self-interested agents being willing to freely share their experience. To this end, this paper presents Certified Reputation (CR), a novel model of trust that can overcome these limitations. Specifically, CR works by allowing agents to actively provide third-party references about their previous performance as a means of building up the trust in them of their potential interaction partners. By so doing, trust relationships can quickly be established with very little cost to the involved parties. Here we empirically evaluate CR and show that it helps agents pick better interaction partners more quickly than models that do not incorporate this form of trust.

A personalized framework for trust assessment

The number of computational trust models has been increasing quickly in recent years yet their applications for automating trust evaluation are still limited. The main obstacle is the difficulties in selecting a suitable trust model and adapting it for particular trust modeling requirements, which varies greatly due to the subjectivity of human trust. The Personalized Trust Framework (PTF) presented in this paper aims to address this problem by providing a mechanism for human users to capture their trust evaluation process in order for it to be replicated by computers. In more details, a user can specify how he selects a trust model based on information about the subject whose trustworthiness he needs to evaluate and how that trust model is configured. This trust evaluation process is then automated by the PTF making use of the trust models flexibly plugged into the PTF by the user. By so doing, the PTF enable users reuse and personalize existing trust models to suit their requirements without having to reprogram those models.

Network analysis on provenance graphs from a crowdsourcing application

Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.

ProvStore: A Public Provenance Repository

ProvStore is the first online public provenance repository supporting the new PROV standards by W3C. It allows users and applications to store and optionally publish the provenance of their data on the Web. Provenance documents can be transformed, visualized, and shared in various serializations, with all the functionality also available to third-party applications via a RESTful API OAuth supported.

A Templating System to Generate Provenance

prov-templateis a declarative approach that enables designers and programmers to design and generate provenance compatible with the prov standard of the World Wide Web Consortium. Designers specify the topology of the provenance to be generated by composing templates, which are provenance graphs containing variables, acting as placeholders for values. Programmers write programs that log values and package them up in sets of bindings, a data structure associating variables and values. An expansion algorithm generates instantiated provenance from templates and sets of bindings in any of the serialisation formats supported by prov. A quantitative evaluation shows that sets of bindings have a size that is typically 40 percent of that of expanded provenance templates and that the expansion algorithm is suitably tractable, operating in fractions of milliseconds for the type of templates surveyed in the article. Furthermore, the approach shows four significant software engineering benefits: separation of responsibilities, provenance maintenance, potential runtime checks and static analysis, and provenance consumption. The article gathers quantitative data and qualitative benefits descriptions from four different applications making use of prov-template. The system is implemented and released in the open-source library ProvToolbox for provenance processing.

An Online Validator for Provenance: Algorithmic Design, Testing, and API

Provenance is a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing. The W3C Provenance Working group has just published the prov family of specifications, which include a data model for provenance on the Web. The working group introduces a notion of valid prov document whose intent is to ensure that a prov document represents a consistent history of objects and their interactions that is safe to use for the purpose of reasoning and other kinds of analysis. Valid prov documents satisfy certain definitions, inferences, and constraints, specified in prov-constraints. This paper discusses the design of ProvValidator, an online service for validating provenance documents according to prov-constraints. It discusses the algorithmic design of the validator, the complexity of the algorithm, how we demonstrated compliance with the standard, and its rest api.

Provenance for Online Decision Making

It is commonly believed that provenance can be utilised to form assessments about the quality, reliability or trustworthiness of data. Once presented with contradictory or questionable information, users can seek further validation by referring to its provenance. While there has been some effort to design principled methods to analyse provenance, the focus has mostly been on offline use of provenance. How to use provenance at runtime, i.e., as the application runs, to help users make decisions, has been barely investigated. In this paper, we propose a generic and application-independent approach to interpret provenance of data to make online decisions. We evaluate the system in CollabMap, an online crowd-sourcing mapping application, to make decisions about the quality of its data and to determine when the crowds contributions to a task are deemed to be complete.

UML2PROV: Automating Provenance Capture in Software Engineering

In this paper we present UML2PROV, an approach addressing the gap between application design, through UML diagrams, and provenance design, using PROV-Template. PROV-Template is a declarative approach that enables software engineers to develop programs that generate provenance following the PROV standard. The main contributions of this paper are: (i) a mapping strategy from UML diagrams (UML State Machine and Sequence diagrams) to templates, (ii) a code generation technique that creates libraries, which can be deployed in an application by creating suitable artefacts for provenance generation, and (iii) a demonstration of the feasibility of UML2PROV implemented with Java, and a preliminary quantitative evaluation that shows benefits regarding aspects such as design, development and provenance capture.

Provenance for the People: An HCI Perspective on the W3C PROV Standard through an Online Game

In the information age, tools for examining the validity of data are invaluable. Provenance is one such tool, and the PROV model proposed by the World Wide Web Consortium in 2013 offers a means of expressing provenance in a machine readable format. In this paper, we examine from a users standpoint notions of provenance, the accessibility of the PROV model, and the general attitudes towards history and the verifiability of information in modern data society. We do this through the medium of an online-game designed to explore these issues and present the findings of the study along with a discussion of some of its implications.