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.

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system

PROV-TEMPLATE is 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% 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. ! 1 INTRODUCTION P ROVENANCE has gained a lot of traction lately in various areas including the Web, legal notices 1 , climate science 2 , scientific workflows [1], [2], [3], computational reproducibility [4], emergency response [5], medical applications 3 , geospatial domain 4 , art and food. The recent standard PROV [6] of the World Wide Web Consortium defines provenance as "as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing." In an increasing number of applications, provenance has become crucial in making systems accountable, by exposing how information flows in systems, and in helping users decide whether information is to be trusted. Provenance is not restricted to computer systems, it can also be used to describe how objects are transformed and people are involved in a physical system [5].Applications and use cases for provenance are well documented in the literature [7], [8], [9], [10]. They include making systems more auditable and accountable [11], reproducing results [12], deriving trust and classification [13], asserting attribution and generating acknowledgments [14], supporting predictive analytics [13], and facilitating traceability [15]. To enable such a powerful functionality, however, one needs to adapt or write applications, so that they generate provenance information, which can then be exploited to offer new benefits to their users.A number of approaches have been proposed to generate provenance: run-time, compile-time, and retrospectively. Runtime generation typically requires applications to be instrumented, and provenance generated accordingly [16],...

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).