Agent-Based Semantic Web Service Composition

Overview of Multi-Agent Systems

A multi-agent system is a system in which multiple agents (AI characters, robots, virtual characters, etc.) interact and work together to achieve goals such as collaboration, competition, and cooperation, and is widely used in areas such as games, simulations, robotics The system is widely used in games, simulations, robotics, and other areas.

In a multi-agent system, individual agents act autonomously and make decisions in response to their surroundings and interactions with other agents. Agents share information and communicate with each other, forming consensus or competing with each other to achieve the goals of the overall system.

The design and implementation of a multi-agent system involves several elements, including

• Agent Behavior: Each agent has a set of behaviors that control its own goals and actions. This may involve the use of algorithms such as behavior trees, state machines, reinforcement learning, etc.
• Environmental model: Agents have sensors or observation modules to collect information about their surrounding environment. This allows the agent to understand its interactions with other agents and objects and choose appropriate actions.
• Communication: Agents use communication mechanisms to share information and intentions with other agents. This facilitates interactions such as collaboration, cooperation, and competition.
• Cooperation and Competition Mechanisms: In a multi-agent system, mechanisms to manage cooperation and competition among agents are important. This may involve methods such as negotiation, competition, cooperation, and distributed problem solving.

Multi-agent systems may be used to solve complex real-world problems. This includes, for example, traffic simulation, economic modeling, and crowd behavior modeling. In games, they are also applied to cooperative and competitive play, NPC behavior, etc.

When implementing a multi-agent system using Unity, it is necessary to program the control, interaction, and information sharing of each agent, taking into account the above elements, and it is also important to represent the agents using game objects and components, and to implement simulation and conflict rules. Implementation of simulation and conflict rules is also important.

Overview of Semantic Web Technologies

Semantic Web technology is a set of technologies that make it easier for computers to understand the meaning and relationships of information. By using this technology, information on the Web can be represented in a format that machines can interpret, and the relationships between resources can be clarified, allowing for more effective integration, retrieval, and analysis of information.

The main elements of Semantic Web technologies are as follows

• RDF (Resource Description Framework): RDF will be the data model for representing information. It represents information in a subject-predicate-object (Subject-Predicate-Object) triple format and describes relationships among resources.
• OWL (Web Ontology Language): OWL is a language for expressing ontologies (formalization of concepts and their relationships) on the Semantic Web. This enables information consistency and inference.
• SPARQL (SPARQL Protocol and RDF Query Language): SPARQL is a query language for querying RDF data. SPARQL can be used to extract specific information from RDF data or to search for related resources.
• Linked Data: Linked Data is one of the principles of the Semantic Web and provides a way to explicitly express relationships between resources from different data sources. Linked Data is data in RDF format that is published and linked to other data sources to create a global information network.

Semantic Web technologies are used in a variety of application areas, including information integration and retrieval, knowledge base building, and data interoperability. Examples of these include corporate data integration, digital archiving of cultural heritage, and integration and analysis of medical data. Semantic Web technologies are also used as a foundation for knowledge graph construction and natural language processing for AI and machine learning.

Incorporating Semantic Web Approaches into Agent Systems

Incorporating Semantic Web approaches into agent systems can enhance information sharing and coordination among agents, resulting in more flexible and effective agent-based systems.

The following describes some of the approaches for incorporating the Semantic Web into agent systems.

• Information representation using RDF: Representing information and knowledge held by agents in RDF format: RDF represents information in a subject-predicate-object triplet format, which allows agents’ knowledge to be structured and relevance to be explicitly expressed. This facilitates information sharing and collaboration with other agents.
• Use of ontologies: Shared ontologies (formalizations of concepts and their relationships) are used to enhance mutual understanding among agents. An ontology is a formal model for defining common vocabulary and relationships that supports communication and reasoning among agents.
• Information retrieval using SPARQL: SPARQL, the query language of the Semantic Web, can be used by agents to retrieve the information they need. Agents can execute SPARQL queries against RDF data to retrieve needed information from other agents or external data sources.
• Leveraging Linked Data: Leveraging the principle of linked data, agents form links with different data sources. This allows agents to explicitly express their relevance to other agents and data sources, thereby increasing the interoperability of information.

Combined, these approaches facilitate information sharing and collaboration among agents, allowing agents to leverage Semantic Web technologies to build richer information and relevant knowledge bases for more flexible and effective decision making and actions.

Agent-based Semantic Web Service Composition

From Agent-Based Semantic Web Service Composition.

The fundamental purpose of the Semantic Web is to create a layer on top of the existing Web that allows for highly automated processing of Web content, further enabling the sharing and processing of data by both humans and software. Semantic Web services can be defined as self-sufficient, reusable software components that can be used to perform specific tasks.

In real-world scenarios, no single service component alone can satisfy the client’s requirements. In such cases, service discovery and selection is used to select the most appropriate service component, and then service composition is used to generate a collection of selected service components according to the requested task. There are various types of approaches to provide semantic web service composition.

Here, we focus primarily on agent-based Semantic Web service compositions. Multi-agent-based Semantic Web service composition is based on the argument that a multi-agent system can be regarded as a service composition system, where different involved agents represent different individual services. Services are viewed as intelligent agent capabilities implemented as self-contained software components.

This document is organized as follows. Chapter 1 briefly introduces basic topics related to the Semantic Web, including the Web, Semantic Web services, Semantic Web service compositions, and ontologies. Chapter 2 provides a general introduction to terms such as agents, multi-agent systems, and negotiation. Chapter 3 describes the basics of agent-based service composition. It also provides an overview of the multi-agent-based Semantic Web service composition approach in the literature. The chapter also presents a preliminary model of agent-based Semantic Web service composition, essentially with respect to the use of coordinator agents in the composition process. It also outlines a service selection model that formulates various quality of service (QoS) parameters and agent cognitive parameters to select the most appropriate service provider agent. Chapter 4 begins with a brief discussion of multi-attribute bargaining and an overview of available multi-attribute bargaining approaches. Agent-based, utility-based, and multi-attribute negotiation approaches provide for negotiation between Semantic Web services. The agent-based, utility-based, and multi-attribute negotiation approaches that provide negotiation between Semantic Web services are described in detail. Finally, in Chapter 5, a negotiated agreement-based Semantic Web service selection and composition approach is proposed. A mathematical model is also proposed that provides multi-attribute negotiation-based service selection using negotiated agreement evaluations. It is hoped that this book will not only serve as an introductory reference, but also provide the reader with a broad and deep understanding of the topic.