Search Results (8)

Plovdiv faces significant air pollution challenges due to geographic, climatic, and industrial factors, making accurate air quality assessment critical. This study presents a hybrid multi-agent platform that integrates symbolic and sub-symbolic artificial intelligence to improve the reliability of air quality monitoring. The platform features a BDI agent, developed using JaCaMo, for processing real-time sensor measurements and a ReAct agent, implemented with LangChain, to incorporate external data sources and perform advanced analytics. By combining these AI approaches, the platform enhances data integration, detects anomalies, and resolves discrepancies between conflicting air quality reports. Furthermore, its scalable and adaptable architecture lays the foundation for future advancements in environmental monitoring. This research represents the first stage in developing an AI-powered system that supports more objective and data-driven decision-making for air quality management in Plovdiv. Full article

Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper. Full article

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent & artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty. Full article

The Internet of Things (IoT) allows the sharing of information among devices in a network. Hardware evolutions have enabled the employment of cognitive agents on top of such devices, which could help to adopt pro-active and autonomous IoT systems. Agents are autonomous entities from Artificial Intelligence capable of sensing (perceiving) the environment where they are situated. Then, with these captured perceptions, they can reason and act pro-actively. However, some agent approaches are created for a specific domain or application when dealing with embedded systems and hardware interfacing. In addition, the agent architecture can compromise the system’s performance because of the number of perceptions that agents can access. This paper presents three engineering approaches for creating IoT Objects using Embedded Multi-agent systems (MAS)—as cognitive systems at the edge of an IoT network—connecting, acting, and sharing information with a re-engineered IoT architecture based on the Sensor as a Service model. These engineering approaches use Belief-Desire-Intention (BDI) agents and the JaCaMo framework. In addition, it is expected to diversify the designers’ choice in applying embedded MAS in IoT systems. We also present a case study to validate the whole re-engineered architecture and the approaches. Moreover, some performance tests and comparisons are also presented. The study case shows that each approach is more or less suitable depending on the domain tackled. The performance tests show that the re-engineered IoT architecture is scalable and that there are some trade-offs in adopting one or another approach. The contributions of this paper are an architecture for sharing resources in an IoT network, the use of embedded MAS on top IoT Objects, and three engineering approaches considering agent and artifacts dimensions. Full article

Blockchains have emerged over time as a reliable and secure way to record transactions in an immutable manner in a wide range of application domains. However, current related solutions are not yet capable of appropriately checking the authenticity of data when their volumes are huge. They are not also capable of updating Blockchain data blocks and synchronizing them within reasonable timeframes. This is the case within the specific context of Blockchain vehicular networks, where these solutions are commonly cumbersome when attempting to add new vehicles to the network. In order to address these problems, we propose in this paper a new Blockchain-based solution that intelligently implement selective communication and collaborative endorsement approaches to reduce communications between vehicles. Our solution represents the vehicles of the Blockchain as intelligent software agents with a Belief–Desire–Intention (BDI) architecture. Furthermore, we propose an approach based on multi-endorsement levels to exchange data of varying sensitive categories. This approach, which is based on endorsing scores, is also used to shorten the admission of new vehicles into the Blockchain. We run simulations using the Hyperledger Fabric Blockchain tool. Results show the efficiency of our solution in reducing the processing times of transactions within two different scenarios. Full article

Working memory is an important function for human cognition since several day-to-day activities are related to it, such as remembering a direction or developing a mental calculation. Unfortunately, working memory deficiencies affect performance in work or education related activities, mainly due to lack of concentration, and, with the goal to improve this, many software applications have been developed. However, sometimes the user ends up bored with these games and drops out easily. To cope with this, our work explores the use of intelligent robotics and dynamic difficulty adjustment mechanisms to develop a novel working memory training system. The proposed system, based on the Nao robotic platform, is composed of three main components: First, the N-back task allows stimulating the working memory by remembering visual sequences. Second, a BDI model implements an intelligent agent for decision-making during the progress of the game. Third, a fuzzy controller, as a dynamic difficulty adjustment system, generates customized levels according to the user. The experimental results of our system, when compared to a computer-based implementation of the N-back game, show a significant improvement on the performance of the user in the game, which might relate to an improvement in their working memory. Additionally, by providing a friendly and interactive interface, the participants have reported a more immersive and better game experience when using the robotic-based system. Full article

Due to advances in mobile device and wireless networking technologies, it has already been possible to transfer agent technology into mobile computing environments. In this paper, we introduce the Linked Data Aware Agent Development Framework for Mobile Devices (LDAF-M), which is an agent development framework that supports the development of linked data aware agents that run on mobile devices. Linked data, which is the realization of the semantic web vision, refers to a set of best practices for publishing, interconnecting and consuming structured data on the web. An agent developed using LDAF-M has the ability to obtain data from the linked data environment and internalize the gathered data as its beliefs in its belief base. Besides linked data support, LDAF-M has also other prominent features which are its peer-to-peer based communication infrastructure, compliancy with Foundation for Intelligent Physical Agents (FIPA) standards and support for the Belief Desire Intention (BDI) model of agency in mobile device agents. To demonstrate use of LDAF-M, an agent based auction application has been developed as a case study. On the other hand, LDAF-M can be used in any scenario where systems consisting of agents in mobile devices are to be developed. There is a close relationship between agents and linked data, since agents are considered as the autonomous computing entities that will process data in the linked data environment. However, not much work has been conducted on connecting these two related technologies. LDAF-M aims to contribute to the establishment of the connections between agents and the linked data environment by introducing a framework for developing linked data aware agents. Full article

The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS) design framework to achieve smart house automation. The novelties of this work include the developments of (1) belief, desire and intention (BDI) agent behavior models; (2) a regulation policy-based multi-agent collaboration mechanism; and (3) a set of metrics for MAS performance evaluation. Simulations of case studies are performed using the Java Agent Development Environment (JADE) to demonstrate the advantages of the proposed method. Full article