Agent-based Data Integration Framework

Combining data from diverse, heterogeneous sources while facilitating a unified access to it is an important (albeit difficult) task. There are various possibilities of performing it. In this publication, we propose and describe an agent-based framework dedicated to acquiring and processing distributed, heterogeneous data collected from diverse sources (e.g., the Internet, external software, relational, and document databases). Using this multi-agent-based approach in the aspects of the general architecture (the organization and management of the framework), we create a proof-of-concept implementation. The approach is presented using a sample scenario in which the system is used to search for personal and professional profiles of scientists

Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities

The modeling of smart grids using multi-agent systems is a common approach due to the ability to model complex and distributed systems using an agent-based solution. However, the use of a multi-agent system framework can limit the integration of new operation and management models, especially artificial intelligence algorithms. Therefore, this paper presents a study of available open-source multi-agent systems frameworks developed in Python, as it is a growing programming language and is largely used for data analytics and artificial intelligence models. As a consequence of the presented study, the authors proposed a novel open-source multi-agent system framework built for smart grid modeling, entitled Python-based framework for heterogeneous agent communities (PEAK). This framework enables the use of simulation environments but also allows real integration at pilot sites using a real-time clock. To demonstrate the capabilities of the PEAK framework, a novel agent ecosystem based on agent communities is shown and tested. This novel ecosystem, entitled Agent-based ecosystem for Smart Grid modeling (A4SG), takes full advantage of the PEAK framework and enables agent mobility, agent branching, and dynamic agent communities. An energy community of 20 prosumers, of which six have energy storage systems, that can share energy among them, using a peer-to-peer market, is used to test and validate the PEAK and A4SG solutions.The authors acknowledge the work facilities and equipment provided by the GECAD research center (UIDB/00760/2020) to the project team.info:eu-repo/semantics/publishedVersio

The SEWASIE architecture: a multi-agent system for data integration

In this paper we present the SEWASIE system, a multi-level agent-based architecture for querying heterogeneous data sources integrated by means of ontologies. Main features of this system are: two level data integration scheme, a query tool that supports the user in formulating a precise query, integrated tools for negotiation and information monitoring, and an agent infrastructure that provides a unifying framework for the architecture. In this work we focus on the agent infrastructure, from the user interface to the query answering mechanismEje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

An agent-based service oriented architecture for risk mining

University of Technology, Sydney. Faculty of Engineering and Information Technology.Risk Mining (RM) is the process of analyzing data including risk information by data mining methods, with the mining results for risk prevention. In the last few years, some researchers have proposed the combination of data mining and agent technology (agent mining) to improve the performance of data mining methodology in the heterogeneous business environments. However, problems exist for further research with the application of risk mining systems in real industry environments to enhance the robustness of system architect, dynamic business process and model accuracy etc. Therefore, in this thesis we present an Agent-based Service-oriented Risk Mining Architecture (ABSORM), which has been designed to facilitate the development of agent mining systems to address the above issues. This thesis focuses on developing the following strategies: • The integration of agent technology with web service. In this framework, we propose a new and easier method, by which the system functions are not integrated into the structure of the agents, rather modeled as distributed services and applications which are invoked by the agents acting as controllers and coordinators. Therefore, techniques developed in this framework can improve the interoperability between different modules, distribution of resources, and the lack of dependency of programming languages. • The integration of agent technology with business process management. In this work, we develop the autonomous agents that can collaborate in a business flow, which not only increases the reusability of the system, but also eases the system development in terms of re-usability of the computational resources. A group of agents solves problems in the following way: each individual agent solves the problem individually, and then interacts with each other to finalize a business process. • The integration of agent technology with ensemble learning methods. In this thesis, we are interested in developing agent-based ensemble learning strategies for risk mining: each ensemble agent individually gathers the evidence about model evaluation, and then ensembles learning methods like bagging and boosting is used to obtain prediction from the individually gathered evidence. Agent based ensemble learning can provide a critical boost to risk mining where predictive accuracy is more vital than model interpretability. The proposed architecture has been evaluated for building an online banking fraud detection system and a student risk management system. These two applications have been proved to be a sophisticated, yet user friendly, risk analysis and management tool. They are modular, interactive, dynamic and globally oriented

On-lattice agent-based simulation of populations of cells within the open-source chaste framework

Over the years, agent-based models have been developed that combine cell division and reinforced random walks of cells on a regular lattice, reaction-diffusion equations for nutrients and growth factors and ordinary differential equations (ODEs) for the subcellular networks regulating the cell cycle. When linked to a vascular layer, this multiple scale model framework has been applied to tumour growth and therapy. Here we report on the creation of an agent-based multiscale environment amalgamating the characteristics of these models within a Virtual Pysiological Human (VPH) Exemplar Project. This project enables re-use, integration, expansion and sharing of the model and relevant data. The agent-based and reactiondiffusion parts of the multiscale model have been implemented and are available for download as part of the latest public release of Chaste (“Cancer, Heart and Soft Tissue Environment”), (http://www.cs.ox.ac.uk/chaste/) version 3.1, part of the VPH Toolkit (http://toolkit.vph-noe.eu/). The environment functionalities are verified against the original models, in addition to extra validation of all aspects of the code. In this work, we present the details of the implementation of the agent-based environment, including the system description, the conceptual model, the development of the simulation model and the processes of verification and validation of the simulation results. We explore the potential use of the environment by presenting exemplar applications of the “what if” scenarios that can easily be studied in the environment. These examples relate to tumour growth, cellular competition for resources and tumour responses to hypoxia. We conclude our work by summarising the future steps for the expansion of the current system

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. The ModelScope-Agent library\footnote{https://github.com/modelscope/modelscope-agent} and online demo\footnote{https://modelscope.cn/studios/damo/ModelScopeGPT/summary} are now publicly available

An architecture for modular distributed simulation with agent-based models

Agent-based simulations are an increasingly popular means of exploring and understanding complex social systems. In order to be useful, these simulations must capture a range of aspects of the modeled situation, each possibly requiring distinct expertise. Moreover, different paradigms may be useful in modelling, ranging from those that use many lightweight reactive agents, to those that use cognitive agents, to those that focus on agent teams and organisational structures. There is need for an architecture which supports the development of a large simulation, through the integration of separately developed modules. This paper describes a framework and architecture which facilitates the integration of multiple agent-based simulations into a single global simulation. This architecture naturally supports distributed simulation and incremental development, which are ways of addressing the computational and conceptual complexity of such systems. In this paper we focus particularly on how to ensure proper management of simulation data that is affected by agents in different modules, at the same logical time. We also provide some preliminary performance evaluation addressing scalability, as well as a comparison of how other available systems handle the issue of shared data

RANCANG BANGUN SISTEM PEMANTAUAN KUALITAS UDARA BERBASIS TEKNOLOGI WIRELESS SENSOR NETWORK

The increment in discuss contamination fortifies the direness of creating an proficient discuss quality observing framework. Remote Sensor Organize (WSN) and Web of Things (IoT) advances are key arrangements to address this challenge. WSN empowers remote discuss quality information collection, whereas IoT expands this capability by joining physical objects into the web organize. The integration of WSN and IoT offers a strong framework for discuss quality observing with real-time get to, energetic investigation, and broader data for partners. The integration of Remote Sensor Arrange framework in discuss quality checking framework empowers uniform and real-time information collection, as well as more precise and agent investigation of natural conditions. Past inquire about has created sensor hubs and checking framework based on WSN and IoT to degree discuss quality parameters with tall exactness, fortifying the establishment for the advancement of way better frameworks within the future