Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Plan Acquisition Through Intentional Learning in BDI Multi-Agent Systems

Multi-Agent Systems (MAS), a technique emanating from Distributed Artificial Intelligence, is a suitable technique to study complex systems. They make it possible to represent and simulate both elements and interrelations of systems in a variety of domains. The most commonly used approach to develop the individual components (agents) within MAS is reactive agency. However, other architectures, like cognitive agents, enable richer behaviours and interactions to be captured and modelled. The well-known Belief-Desire-Intentions architecture (BDI) is a robust approach to develop cognitive agents and it can emulate aspects of autonomous behaviour and is thus a promising tool to simulate social systems. Machine Learning has been applied to improve the behaviour of agents both individually or collectively. However, the original BDI model of agency, is lacking learning as part of its core functionalities. To cope with learning, the BDI agency has been extended by Intentional Learning (IL) operating at three levels: belief adjustment, plan selection, and plan acquisition. The latter makes it possible to increase the agent’s catalogue of skills by generating new procedural knowledge to be used onwards. The main contributions of this thesis are: a) the development of IL in a fully-fledged BDI framework at the plan acquisition level, b) extending IL from the single-agent case to the collective perspective; and c) a novel framework that melts reactive and BDI agents through integrating both MAS and Agent-Based Modelling approaches, it allows the configuration of diverse domains and environments. Learning is demonstrated in a test-bed environment to acquire a set of plans that drive the agent to exhibit behaviours such as target-searching and left-handed wall-following. Learning in both decision strata, single and collective, is tested in a more challenging and socially relevant environment: the Disaster-Rescue problem

Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles

This record contains the (video) data and source code created in relation to the submitted thesis of the same title.The videos included in this collection have been derived using the 3D view components included in the BSF software framework, during a number of scenarios explained more fully in the related thesis: "Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles" Additional views such as the graph views have been created from the rdfUtilities package. These scenarios can be re-run by using the included version of the BSF framework which is provided as zip file. From the command line, run "ant -p" to see available projects, which includes the traffic simulation, institutions, 3D view, and more

Coordination Issues in Complex Socio-technical Systems: Self-organisation of Knowledge in MoK

The thesis proposes the Molecules of Knowledge (MoK) model for self-organisation of knowledge in knowledge-intensive socio-technical systems. The main contribution is the conception, definition, design, and implementation of the MoK model. The model is based on a chemical metaphor for self-organising coordination, in which coordination laws are interpreted as artificial chemical reactions ruling evolution of the molecules of knowledge living in the system (the information chunks), indirectly coordinating the users working with them. In turn, users may implicitly affect system behaviour with their interactions, according to the cognitive theory of behavioural implicit communication, integrated in MoK. The theory states that any interaction conveys tacit messages that can be suitably interpreted by the coordination model to better support users' workflows. Design and implementation of the MoK model required two other contributions: conception, design, and tuning of the artificial chemical reactions with custom kinetic rates, playing the role of the coordination laws, and development of an infrastructure supporting situated coordination, both in time, space, and w.r.t. the environment, along with a dedicated coordination language

Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence

There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework

Smart Reconfiguration of Distribution Grids using Agent-based Technology

The multi-Agent system designed based on automatic reconfiguration principles was programmed as a set of AgentSpeak codes in which work and cooperate inside a common simulated distribution grid environment. Creating the application meant programming the agents on the one side, including the actions logic control to be executed relying on the environment changes, and on the other side the environment itself. That stated, the environment notion was solely based on the chosen distribution grid concept and explored according to the proposed problematic

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains