Agent-based Architecture Research Articles

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

Cognitive Systems for Education: Architectures, Innovations, and Comparative Analyses

In recent years, education has been closely linked to the continued development of technology, especially smart systems based on the use of artificial intelligence with cognitive capabilities. The emphasis here is on the significant potential of cognitive computing in the domain of education and learning. This association implies a transformative impact on how education is delivered, accessed, and personalized through the integration of advanced cognitive systems in the learning and teaching process. This article conducts an overview of the several cognitive computing technologies in the context of education used to enhance learning and teaching activities. This study identifies three conceptual architectures of these systems, Layered Architecture, Agent-Based Architecture, and Hybrid Architecture; then describes their components. Finally, it explores well-known platforms that are used in the education field namely IBM Watson, Kenwton, Carnegie Learning, and DreamBox Learning.

MAS-Cloud+: A novel multi-agent architecture with reasoning models for resource management in multiple providers

Nowadays, scientific and commercial applications are often deployed to cloud environments requiring multiple resource types. This scenario increases the necessity for efficient resource management. However, efficient resource management remains challenging due to the complex nature of modern cloud-distributed systems since resources involve different characteristics, technologies, and financial costs. Thus, optimized cloud resource management to support the heterogeneous nature of applications balancing cost, time, and waste remains a challenge. Multi-agent technologies can offer noticeable improvements for resource management, with intelligent agents deciding on Virtual Machine (VM) resources. This article proposes MAS-Cloud+, a novel agent-based architecture for predicting, provisioning, and monitoring optimized cloud computing resources. MAS-Cloud+ implements agents with three reasoning models including heuristic, formal optimization, and metaheuristic. MAS-Cloud+ instantiates VMs considering Service Level Agreement (SLA) on cloud platforms, prioritizing user needs considering time, cost, and waste of resources providing appropriate selection for evaluated workloads. To validate MAS-Cloud+, we use a DNA sequence comparison application subjected to different workload sizes and a comparative study with state-of-the-art work with Apache Spark benchmark applications executed on the AWS EC2. Our results show that to execute the sequence comparison application, the best performance was obtained by the optimization model, whereas the heuristic model presented the best cost. By providing the choice among multiple reasoning models, our results show that MAS-Cloud+ could provide a more cost-effective selection of the instances reducing ≈58% of execution average cost of WorkdCount, Sort, and PageRank BigDataBench benchmarking workloads. As for the execution time, the WorkdCount and PageRank present reduction, the latter with ≈58%. The results indicate a promising solution for efficient cloud resource management.

A multi-agent architecture for learning paths-based personalized e-learning systems

The application of artificial intelligence and semantic models for increasing learning and tutoring quality by personalization is an emerging research area. Мulti-agent frameworks facilitate the communication between the different components and ontological models can be used as knowledge sources for intelligent agents. In this research we analyse knowledge models and software architectures, outline trends in the personalized learning area and propose an agent-based architecture for e-learning systems that can conduct learning by generating and recommending personalized learning paths. Initial evaluation of prototype system is proposed and learning path generation scenarios are discussed.

An Agent-Based Architecture of the Digital Twin for an Emergency Department

The concept of Digital Twin (DT) seems promising to improve the management of patient pathways in Emergency Departments (EDs). This article proposes an agent-based architecture of a DT designed for that purpose. The core of this DT is its Information System (IS), which is regularly synchronised on the IS of the Physical Twin (PT). The agents model the ED’s resources (equipment and staff) and patients in the DT and update this information in the DT’s IS. This article shows how such a DT may operate in three modes: (0) “Digital Shadow” to monitor the ED’s current state in real time, (1) “Synchronised DT” to monitor the ED’s current and future states according to a predictive simulation, and (2) “Exploratory DT” in order to perform Monte Carlo simulations of various future states. Mode (1) is the main contribution. This proposition is illustrated in a simulation of the ED in order to demonstrate the capabilities.

Design and implementation of agent-based robotic system for agile manufacturing: A case study of ARIAC 2021

We share our experience and lessons learned from participating in the Agile Robotics for Industrial Automation Competition (ARIAC), 2021. We won 1st place in the finals and benefited from an agent-based architecture, task assignment strategy, and modular software programming design method based on the Unified Modeling Language . • An agent-based architecture is proposed to implement an agile industrial robotics system. • Instant task assignment strategy for scheduling in proposed to handle the unknown disturbances. • The development takes loosely-coupled software modules to reduce the design effort.

HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring

Thermal power plants, TPP, are one of the main players in the phosphoric acid and fertilizer production value chain. The control of power plant assets involves considerable complexity and is subject to several constraints, affecting the asset’s reliability and, most importantly, plant operators’ safety. The main focus of this paper is to investigate the potential of an agent-based digital twin architecture for collaborative prognostic of power plants. Based on the ISO 13374:2015 scheme for smart condition monitoring, the proposed architecture consists of a collaborative prognostics system governed by several smart DT agents connected to both physical and virtual environments. In order to apprehend the potential of the developed agent-based architecture, experiments on the architecture are conducted in a real industrial environment. We show throughout the paper that our proposed architecture is robust and reproduces TPP static and dynamic behavior and can contribute to the smart monitoring of the plant in case of critical conditions.

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

FAtiMA Toolkit: Toward an Accessible Tool for the Development of Socio-emotional Agents

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this article, we describe the FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers, and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects, and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, the FAtiMA Toolkit’s library-based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components, and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of the FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.

Agent-Based Distributed Data Analysis in Industrial Cyber-Physical Systems

Cyber-Physical System (CPS) is a key concept in the fourth industrial revolution, acting as the backbone infrastructure to develop Industry 4.0 compliant solutions based on decentralized networks of cyber-physical entities. In such systems, the distribution of intelligence and data analytics capabilities by different computational layers assumes a crucial relevance to support monitoring, diagnosis, prediction, and optimization tasks. Multiagent Systems (MAS) is a suitable approach to support this distribution through vertical and horizontal dimensions. In this context, this article introduces a modular agent-based architecture to balance the distribution of data analysis capabilities in industrial CPS. The proposed MAS solution was implemented in an industrial automotive factory plant, which applicability, novelty, and benefits were recognized, e.g., through its inclusion in the Innovation Radar of the Great EU-funded Innovations due to its contribution for the implementation of Zero Defects Manufacturing strategies in multistage production systems.

Comparison of Three Agent-Based Architectures for Distributed Additive Manufacturing

Models of distributed agent-based Additive Manufacturing (AM) systems have suggested that considerable productivity, reactivity, and resiliency gains can be realised. However, there remains scepticism and real-world validation is required to demonstrate the benefits, practicalities, and challenges of implementing agent-based manufacturing systems. It is only then that potential gains can be evidenced and the Return on Investment fully appraised.This paper discusses the underlying communication architecture required for the creation of a hardware technology demonstrator that enables the evaluation of distributed, agent-based, AM. Three architectures to create the desired network (Host-Client, Peer-to-Peer, and Digital Shadow) are discussed, with a focus on implementation and operational factors. The paper continues by describing the future development needed for the creation of a demonstrator aimed at exploring the ease of adoption of agent-based approaches and the potential for integration into existing AM workflows. The planned demonstrator is to be implemented at the University of Bristol to co-ordinate production across multiple sites in order to support their education and research activities as well as provide a platform for public and industry engagement.

Responding to rapidly changing product demand through a coordinated additive manufacturing production system: a COVID-19 case study

COVID-19’s lockdown policies saw Modern Manufacturing Practice (MMP) – batch/mass/just-in-time supply chains – severed and societal demands rapidly change from products such as vehicles and clothing to Personal Protection Equipment (PPE), ventilators and equipment for remote working. The critical and, in many cases, life preserving, need for responsive manufacturing resulted in government and frontline services turning to society’s Additive Manufacturing (AM) capability in homes, schools, universities, and industry to provide essential products and product replacements. While AM managed to respond and support some government and frontline services, the highly distributed and diverse nature of the nation’s AM resources resulted in potentially avoidable production inefficiencies and delays. This paper develops and evaluates a series of strategies for coordinating AM for rapidly changing product demand to further enhance the responsiveness and productivity of AM. The strategies presented in the paper employ a host-client agent-based architecture that enable local governance of production thereby enabling distributed AM resource to come together to tackle society’s production needs without the need for centralised coordination. To enable and support local governance, it is necessary to understand how the combination of production logics impact the overall performance of the production system. Correspondingly, the contribution of this paper lies in the characterisation and quantification of the impacts of production logic through the metrics of Time in System, Lateness and Jobs in Queue and the consequences for responsive AM production systems.

Agent-Based Coordinated Control of Power Electronic Converters in a Microgrid

This paper presents the implementation of an agent-based architecture suitable for the coordination of power electronic converters in stand-alone microgrids. To this end, a publish-subscribe agent architecture was utilized as a distributed microgrid control platform. Over a distributed hash table (DHT) searching overlay, the publish-subscribe architecture was identified based on a numerical analysis as a scalable agent-based technology for the distributed real-time coordination of power converters in microgrids. The developed framework was set up to deploy power-sharing distributed optimization algorithms while keeping a deterministic time period of a few tens of milliseconds for a system with tens of converters and when multiple events might happen concurrently. Several agents participate in supervisory control to regulate optimum power-sharing for the converters. To test the design, a notional shipboard system, including several converters, was used as a case study. Results of implementing the agent-based publish-subscribe control system using the Java Agent Development Framework (JADE) are presented.

Agent Based Onboard Firefighting System

This paper presents a model of agent-based architecture for fighting fires on ships. The introduction of agent technology in firefighting decision-making is a step towards safe autonomous vessels. The human factor can be excluded through the introduction of agent-based technology for the detection and extinguishing of fires onboard ships. The aim is to reduce the number of injuries and deaths, and minimize loss of ships and cargo. Another advantage of agent-based technology is its easy interoperability with other automated onboard systems. The presented model was implemented on a prototype in a simulation environment. The results of the experiment conducted on the implemented prototype are also presented.

A collaborative predictive multi-agent system for forecasting carbon emissions related to energy consumption

Energy production and consumption are one of the largest sources of greenhouse gases (GHG), along with industry, and is one of the highest causes of global warming. Forecasting the environmental cost of energy production is necessary for better decision making and easing the switch to cleaner energy systems in order to reduce air pollution. This paper describes a hybrid approach based on Artificial Neural Networks (ANN) and an agent-based architecture for forecasting carbon dioxide (CO2) issued from different energy sources in the city of Annaba using real data. The system consists of multiple autonomous agents, divided into two types: firstly, forecasting agents, which forecast the production of a particular type of energy using the ANN models; secondly, core agents that perform other essential functionalities such as calculating the equivalent CO2 emissions and controlling the simulation. The development is based on Algerian gas and electricity data provided by the national energy company. The simulation consists firstly of forecasting energy production using the forecasting agents and calculating the equivalent emitted CO2. Secondly, a dedicated agent calculates the total CO2 emitted from all the available sources. It then computes the benefits of using renewable energy sources as an alternative way to meet the electric load in terms of emission mitigation and economizing natural gas consumption. The forecasting models showed satisfying results, and the simulation scenario showed that using renewable energy can help reduce the emissions by 369 tons of CO2 (3%) per day.

An intelligent agent-based architecture for resilient digital twins in manufacturing

Digital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturing.

An evolutionary approach to modeling and control of space heating and thermal storage systems

Home Energy Management systems are in a rapid development curve, supported by the advancements in computational intelligence, smart appliances, and new smart-grid frameworks. These systems are a fundamental part to implement demand-side management strategies and to shift local energy demand to periods of lower consumption effectively. In this paper, we explore the application of distributed co-evolutionary optimization algorithms and an agent-based architecture to reduce the consumption profile signature of the heating system during the critical peak demand periods, by reducing costs and respecting the comfort constraints of the occupants. The proposed control architecture targets the typical baseboard space heating systems and electrical thermal storage systems, as these represent a large portion of the energy usage in Nordic countries and are commonly controlled by room independent thermostats, which could be easily replaced by smart devices running an algorithm as the one presented in this work. Results prove the strategy proposed getting a cost reduction of up to 23% and a peak-to-average ratio decrease of up to 25% for reference scenarios. Also, an emulation Simulink model is developed to recreate a house and the different heating loads studied in this paper and an experimental test bed is built to model a real ETS system, two different complexity degree RC models are proposed to describe such systems.

A survey on agent learning architecture that adopts internet of things and wireless sensor networks

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

A deep-Q learning approach to mobile operator collaboration

Next-generation mobile connectivity services include a large number of devices distributed across vast geographical areas. Mobile network operators will need to collaborate to fulfill service requirements at scale. Existing approaches to multi-operator services assume already-established collaborations to fulfill customer service demand with specific quality of service (QoS). In this paper, we propose an agent-based architecture, where establishment of collaboration for a given connectivity service is done proactively, given predictions about future service demand. We build a simulation environment and evaluate our approach with a number of scenarios and in context of a real-world use case, and compare it with existing collaboration approaches. Results show that by learning how to adapt their collaboration strategy, operators can fulfill a greater part of the service requirements than by providing the service independently, or through pre-established, intangible service level agreements.

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.