Executable specication of open multi-agent systems

Multi-agent systems where the agents are developed by parties with competing interests, and where there is no access to an agent's internal state, are often classi ed as `open'. The members of such systems may inadvertently fail to, or even deliberately choose not to, conform to the system speci cation. Consequently, it is necessary to specify the normative relations that may exist between the members, such as permission, obligation, and institutional power. We present a framework being developed for executable speci cation of open multi-agent systems. We adopt a bird's eye view of these systems, as opposed to an agent's perspective whereby it reasons about how it should act. This paper is devoted to the presentation of various examples from the NetBill protocol formalised in terms of institutional power, permission and obligation. We express the system speci cation in the Event Calculus and execute the speci cation by means of a logic programming implementation. We also give several example formalisations of sanctions for dealing with violations of permissions and obligations. We distinguish between an open multi-agent system and the procedure by which an agent enters and leaves the system. We present examples from the speci cation of a role-management protocol for NetBill, and demonstrate the interplay between such a protocol and the corresponding multi-agent system

Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series

Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics

Competence Checking for the Global E-Service Society Using Games

We study the problem of checking the competence of communicative agents operating in a global society in order to receive and offer electronic services. Such a society will be composed of local sub-societies that will often be semi-open, viz., entrance of agents in a semi-open society is conditional to specific admission criteria. Assuming that a candidate agent provides an abstract description of their communicative skills, we present a test that a controller agent could perform in order to decide if a candidate agent should be admitted. We formulate this test by revisiting an existing knowledge representation framework based on games specified as extended logic programs. The resulting framework finds useful application in complex and inter-operable web-services construed as semi-open societies in support of the global vision known as the Semantic Web

An Agent Architecture for Concurrent Bilateral Negotiations

Abstract. We present an architecture that makes use of symbolic decision-making to support agents participating in concurrent bilateral negotiations. The architecture is a revised version of previous work with the KGP model [23, 12], which we specialise with knowledge about the agent’s self, the negotiation opponents and the environment. Our work combines the specification of domain-independent decision-making with a new protocol for concurrent negotiation that revisits the well-known alternating offers protocol [22]. We show how the decision-making can be specialised to represent the agent’s strategies, utilities and prefer-ences using a Prolog-like meta-program. The work prepares the ground for supporting decision-making in concurrent bilateral negotiations that is more lightweight than previous work and contributes towards a fully developed model of the architecture

Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

Minimizing efforts in validating crowd answers

In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the experts work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20% of the questions