Achieving Interoperability in Energy Systems through Multi-Agent Systems and Semantic Web

Intelligent, autonomous agents are still not available in the Semantic Web at large scale today. Also the fields of Semantic Web and Multi-Agent Systems are not working together very closely although they could profit much from each other. Existing approaches merely use the Web as a transport layer and are not properly aligned to the architectural style of the Web. The Internet of Things, which would be very useful for agents to act upon Things in the real world, on the other side is very fragmented and not easily inter-operable. The Web of Things has emerged as an approach to use the (Semantic) Web as an application layer for Things. It is still unclear however, how agents on the WoT should look like. We propose that the Semantic Web is a suitable integration layer, both for agents and Things. We investigate how a Multi-Agent System in the Semantic Web of Things can be build by utilizing simple reflex agents and the communication paradigm of stigmergy. We map Things and artifacts to Web resources that are managed by a Web server and provide affordances to agents through hypermedia.KeywordsMulti-Agent SystemsWeb of ThingsSimple reflex agentsStigmergyRestHypermedia

Semantic Web supports a fire-new infrastructure for solving the problem of semantic information interoperability, and it promises to support an intelligent and automatic information-processing platform for multi-agent system whose ultimate objective is to provide better services for end-users, for example, interoperable information query. Therefore, except agent-to-agent interaction in multi-agent system, there is human-to-agent interaction. To unify the two kinds of interaction, this paper introduces Q language - a scenario description language for designing interaction among agents and users. A Q-based architecture, which integrates ontology servers, ontology-mapping servers, semantic information sources, and multi-agent query system, is presented as a system solution to semantic information interoperability on Semantic Web. Furthermore, we investigate key technologies of interoperability: domain ontology, ontology-mapping service, and related multi-agent system, and give an implementation to demonstrate how our architecture works.

The book consists of 29 chapters which have been selected and invited from the submissions to the 1st International Conference on Collective Intelligence - Semantic Web, Social Networks & Multiagent Systems (ICCCI 2009). All chapters in the book discuss various examples of applications of computational collective intelligence and related technologies to such fields as semantic web, information systems ontologies, social networks, agent and multiagent systems. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants to courses on Soft Computing, Multi-Agent Systems and Robotics. This book can also be useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases intelligent collectives. Many such challenges are suggested by particular approaches and models presented in particular chapters of this book.

Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. There are a number of methodologies have been proposed for multi- agent engineering process such as Multi-agent System Engineering (MaSE). I have used the MaSE engineering process for the development of my ontology based multi- agent system for the Academic Institute. In the era of Semantic Web, the ontology has established as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous distributed systems. Both ontology and agent technologies are central to the semantic web, and their combined use will enable the sharing of heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. Ontology is used throughout the multi-agent system to assist the interactions among different agents as well as to improve the quality of the service provided by each agent. This paper focuses on the utilization of combining both Ontology and Multi-Agent System (MAS) structure towards system integration for University teaching environment. In this paper, I include system prototype for ontology based multi-agent system. I have used Web Ontology Language (OWL) for the development of domain ontology for the Academic Institute and KQML as an agent communication language. Finally I have developed ontology-based multi- agent system for the university teaching environment by taking the benefits of both renowned technologies.

The common access to the broadband Internet allows small and medium enterprises (SME) simpler participation in various supply chains [3]. However the main problem still is lack of consistency between processes which are undertaken by various supply chain participants. The aim of this paper is to propose the concept of broader cooperation between manufacturing companies based on semantic web and multi-agent system. The background of this paper refers to previous experiences of the authors by implementation of the CORELOG project. It promotes cooperation in logistics services among manufacturing companies located in the Bologna metropolitan area in order to increase companies’ competitiveness as well as environmental sustainability through the rationalization of the logistics processes. The application of semantic web and multi-agent system in the above mentioned project allows the efficient automatic data collection about logistics services providers and their resources, as presented in the Internet.

Development of agent systems is naturally a complex task due to the fundamental characteristics of agents. In addition, agent internals and inter-agent behavior models inside Multi-agent Systems (MAS) may become even more difficult to implement when interactions of agents with web services on the Semantic Web are taken into account. Our approach consists of the utilization of a Domain-specific Modeling Language (DSML) during MAS development in order to cope with the abovementioned challenge. This paper describes how the formal semantics of this DSML can be defined by especially focusing on its viewpoint on agent-semantic service interactions and discusses the use of this semantics definition on MAS validation. Determined semantic rules are both defined and implemented by using Alloy specification language which has a strong description capability based on both relational and first-order logic.

Cross-community interoperation between knowledge-based multi-agent systems: A study on EMERALD and Rule Responder

Semantic Web Agents have been considered as main type of software to consume the semantic data since the Semantic Web concept was raised first time in the well-known "The Semantic Web" article in 2001. More than a decade passed and there is no collaboration between multi-agent systems and semantic web or its current realization: linked data web communities that can be considered important. In this paper, it is argued that initial vision was right and two communities need each other to scale up their current practice. Thus, a conceptual framework is proposed to establish necessary links between agent and linked data web infrastructures. Environment abstraction has a special role in this framework and this role is especially discussed throughout the paper.

Following software engineering approaches for the Semantic Web (SW) and also Hendler’s vision [3], I believe that the SW will not be a unique large complex Web, but it will be composed, mainly, of several small contextualized domain applications. These domain applications will be, in my opinion, Multi-Agent Systems (MAS) [5]. MAS have emerged as a promising approach for the development of information systems, which are constituted of cooperative goal-oriented problem-solving entities (named agents) [6]. Agent-based computing is rapidly emerging as a powerful technology for the development of distributed and complex information systems.

Hybrid Renewable Energy Systems (HRES) are increasingly adopted to provide reliable electricity in rural and remote areas lacking access to centralized grids. This chapter explores the development and validation of a Multi-Agent System (MAS) framework designed to enhance the control and coordination of off-grid HRES integrating solar photovoltaic, wind turbines, battery storage, a diesel generator, and an inverter. The decentralized architecture assigns autonomous agents to each energy component, overseen by a central Control Agent that ensures optimal energy dispatch, fuel minimization, and system stability. The framework was evaluated over a 92-day period using real meteorological and load data. The MAS-based control strategy demonstrated significant improvements in operational efficiency, achieving over 90% reduction in fuel consumption and operating costs compared to conventional diesel-only systems. Solar photovoltaic emerged as the dominant energy source, with battery storage playing a key role in mitigating renewable intermittency and maintaining reliability. Economic assessment showed a 25% reduction in total system cost and a levelized cost of energy of $0.276/kWh, with an investment payback period of less than 2 years. These findings highlight the potential of MAS as an intelligent, scalable, and cost-effective solution for managing decentralized energy systems in underserved regions. The approach supports broader goals of enhancing energy access, promoting renewable integration, and advancing sustainable development in off-grid contexts.

Advent of technologies like semantic web, multi-agent systems, web mining has changed the internet as knowledge provider. Web personalization offers a solution to the information overload problem in current web by providing users a personalized experience, considering their interest, behavior, context and emotions. Semantic web technology is based on use of software agents, ontologies and reasoning to add meaning to web information. An important technology for achieving personalization is the use of independent intelligent software agents. This work reviews, web personalization in the light of semantic web and software agent technology. A comparative study of recent works in the domain of web personalization has been carried out for this purpose. This review highlights ample scope for application of intelligent agents in the web personalization domain for solving many existing issues like personalized content management, user profile learning, modeling and adaptive interactions with users.

The autonomous business decision systems are now the centre of organisational competitiveness as organisations continue to work in volatile and increasingly data-intensive and interdependent environments. The use of multi-agent systems (MAS) can provide an effective paradigm, which facilitates decentralised, adaptive and collaborative intelligence with distributed agents perceiving environments and negotiating actions and autonomous optimisation. This paper is a critical assessment of how multi-agent AI models, such as logic agents, multi-agent reinforcement learning (MARL), agents based on large language models, agent development systems, simulation systems, and self-evolving agents' systems, have contributed to autonomous business decision systems. Systematic literature reviews, simulations, enterprise case studies and foundation-model based research and decision intelligence (DI) frameworks evidence shows how multi agent strategies provides increased robustness, scalability and responsiveness to the complex organisational domains. Simulation-based research using MAS is seen to be able to model emerging behaviours, test organisational conditions and uncertainty within energy system, construction, logistics and financial fields. The further reinforcement learning and cooperative decision modelling enhance MAS autonomy as it provides the ability to optimise the learning of dynamic environments. Similar results are presented by the research of Automated Machine Learning (AutoML) that demonstrates that automated model development helps lower technical barriers and speeds up its deployment and enables adaptive decision pipelines. Decision Intelligence literature displays how organisations can restructure decision-making with the help of AI to construct data pipelines, and provide human-computer interaction. Although issues remain, such as explainability, cost of coordination and data governance, there is strong evidence that MAS constitute a backbone architecture to next generation autonomous decision ecosystems according to all sources. The given synthesis illustrates how multi-agent AI may be used as a strategic foundation of predictive, operational and strategic automation of decisions in contemporary businesses.

In the past, many knowledge representation systems failed because they were too monolithic and didn't scale well, whereas other systems failed to have an impact because they were small and isolated. Along with this trade‐off in size, there is also a constant tension between the cost involved in building a larger community that can interoperate through common terms and the cost of the lack of interoperability. The semantic web offers a good compromise between these approaches as it achieves wide‐scale communication and interoperability using finite effort and cost. The semantic web is a set of standards for knowledge representation and exchange that is aimed at providing interoperability across applications and organizations. We believe that the gathering success of this technology is not derived from the particular choice of syntax or of logic. Its main contribution is in recognizing and supporting the fractal patterns of scalable web systems. These systems will be composed of many overlapping communities of all sizes, ranging from one individual to the entire population that have internal (but not global) consistency. The information in these systems, including documents and messages, will contain some terms that are understood and accepted globally, some that are understood within certain communities, and some that are understood locally within the system. The amount of interoperability between interacting agents (software or human) will depend on how many communities they have in common and how many ontologies (groups of consistent and related terms) they share. In this article we discuss why fractal patterns are an appropriate model for web systems and how semantic web technologies can be used to design scalable and interoperable systems.

The task of developing effective public energy policy is becoming increasingly difficult. Economic liberalization favors competitive markets, private interests, and light regulation, while climate protection and sustainable land usage have created new public interests. Decentralized technologies have added to the analytical challenge and include weather-dependent energy sources, embedded generation and cogeneration, end-use efficiency, and status-aware demand. Smart grids and intelligent control look likely to radically alter system architectures over the coming decades. These new circumstances also offer opportunities for innovative public policy responses to motivate and guide the transition toward more sustainable and effective energy systems. This thesis presents a new energy systems framework that combines a range of technical and microeconomic processes at high resolution within the one simulation. A class library provides modelers with a coherent set of energy technologies, plant operations, dispatch methods, supply agreements, commodity types, and differentiated surroundings. These entities can then be used to populate a systems model that then evolves sequentially in fixed steps of between 5 min and 24 h and over one year typically. Structural change, including new capital works, is necessarily exogenous, but can be investigated via scenarios. Scenarios are run against some selected reference case and the resulting public interest performances are then reported by difference. Sets of scenarios can be interactively screened for Pareto dominance to assist with ranking and assessment. High-resolution models are data intensive but are indicated whenever fine-grained network effects and intertemporal dynamics are potentially present and need capturing and/or operator-level decisioning requires portrayal. The method itself draws on topics from graph theory, dynamical systems theory, multi-agent systems, engineering design, mixed-integer programming, project finance, and object-oriented software development. These ideas are encapsulated in the command-line application xeona, conceived and written as part of this project. Revision 9218 contains 58000 source lines of C++. The program was tested with a proof-of-concept model based on a simplification of the New Zealand national energy system. Although limited to 24 technical assets, 18 operators, 6 commitment domains, 5 commodities, and 6 environmental contexts, the nine scenarios deployed produced interesting and subtle results. A key motivation behind the development of xeona was the need to better understand end-user options and responsiveness in relation to the status of the system at large. The customer-side-of-the-meter is not well represented by mainstream policy analysis and high-resolution modeling applied to this area may well reveal untapped and worthwhile public interest opportunities.

Semantic integration has gained considerable attention in geographic information system (GIS) interoperability with the goal of conquering semantic heterogeneity. Over the past two decades, ontology engineering has been widely used to resolve semantic integration obstacles. However, most of the previous ontology integration approaches mainly focused on the same type of data with formal ontology. A key challenge is to identify the semantics of cross-domain data generated by different domains from their own perspectives. Given that formal ontology is not always available, this paper proposes a novel semi-automatic approach to determine the semantic relationship of cross-domain concepts based on lightweight ontologies. The semantics of each concept is presented by an extendable and structural definition framework composed of a number of resource description framework triple statements formed by common vocabularies extracted from the free-text definition of the concept. We further propose algorithms to compare the structural definition of concepts and to determine the semantic relationship of concepts between different domains. Finally, the semantic relationship of concepts from different domains is formally presented by the bridge ontology to serve future application needs. The bridge ontology of two GIS-based data, topographic map data and land use data, was developed to demonstrate the feasibility of the proposed approach. The preliminary result shows a faithful and highly automatic semantic integration for cross-domain data.