Agent-Based Semantic Web Service Composition

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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.

1 Introduction

Semantic Web is the extension of current Web in which information is given well defined meaning better enabling computer and people to work in cooperation. It has a layered architecture. Various layers in the architecture follow the principles of downward compatibility and upward partial understanding. Among others, some of the Semantic Web languages are RDF, RDF-S, DAML- ONT (DARPA Agent Markup Language—Ontology), OIL (Ontology Inference Layer), DAML ? OIL, OWL (Web Ontology Language), DAML-S, and OWL-S. Semantic web services can be obtained from the augmentation of web service through semantic annotations. Various services related processes are service dis- covery, selection, composition, invocation and monitoring. In this chapter, a very brief introduction of some of the basic topics related to Semantic Web has been given.

1.1  Semantic Web
1.2  Web Versus Semantic Web
1.3  Layered Architecture of Semantic Web
1.4  Semantic Web Service
1.5  Semantic Web Service Composition
1.6  Ontology
References

2 Semantic Web Agents

Agent denotes the piece of software that possesses the properties of autonomy, social ability, reactivity, proactivity, temporal continuity, and goal- orientedness. Multi-agent system consists of a number of agents which are capable of interacting with each other. In these systems, the agents are capable to coop- erate, coordinate, and negotiate with each other. Various activities in the Semantic Web based systems are performed by Semantic Web agents. The inter-agent dependencies among these agents are managed using the process of negotiation. Negotiation is the process by which a group of agents come to a mutually acceptable agreement on some matter. In this chapter, a general introduction to the terms agents, multi-agent systems and negotiation has been given.

2.1  SemanticWebAgents
2.2  Multi-AgentSystems
2.3  Negotiation
References

3  Agent-Based Semantic Web Service Selection and Composition

The agent-based service composition system considers a service as an intelligent agent capability. In this system, a multi-agent system is considered as a Semantic Web Service composition system in which different agents represent the different individual services. The presented chapter discusses the basics of agent based service composition. An overview of some of the multi-agent based Semantic Web service composition approaches available in the literature has also been given. The chapter also presents models for agent-based Semantic Web service composition basically varying on the use of a coordinator agent in the composition process. Where, the coordinator agent is any agent that can control and coordinate all the different activities involved in the composition process. A brief overview of a service selection model providing formalization of various Quality of Service (QoS) parameters and cognitive parameters of agent for selecting the most appropriate service provider agent has also been presented.

3.1  Agent-Based Semantic Web Service Composition
3.2  Overview of Some Works on Agent-Based SWSComposition
3.3  Multi-Agent-Based Semantic Web Service
CompositionModel
3.4  Quality of Service and Cognitive Parameters-Based
ServiceSelectionModel

4  Multi-Attribute Negotiation Between Semantic Web Agents

In the real-life scenario, there are very common situations involving negotiation based upon the multiple issues simultaneously. In the same way, the negotiation before selection of any web service provider agent is based upon the multiple attributes of web service. This process of making a joint decision by two or more parties resulting into a mutually acceptable agreement on some matter involving multiple attributes is called as multi-attribute negotiation. After a brief discussion over the multi-attribute negotiation, the chapter presents an overview of some of available multi-attribute negotiation approaches. An agent-based, utility- based, multi-attribute negotiation approach providing negotiation between Semantic Web Services has been described in detail. This approach also presents the formalized modeling for utility calculation and the process for generation of proposals at different steps of negotiation.

4.1  Multi-Attribute Negotiation
4.2  Overview of Some Multi-Attribute Negotiation Approaches
4.3  AMulti-AttributeNegotiationApproach

5 A Multi-Agent Negotiation-Based Approach to Selection and Composition of Semantic Web Services

In the semantic web service composition process, the evaluation of negotiation-agreements resulting from the negotiation between the service requester and various service providers can be used for the selection of best service provider. The chapter presents a semantic web service composition model for the same purpose. A mathematical model providing multi-attribute negotiation-based service selection using evaluation of negotiation-agreements has also been proposed. A prototype system has been implemented based upon the proposed service selection and composition models.

5.1  Introduction
5.2  RelatedWorks
5.3  Negotiation-Agreement Based Composition Model
5.4  Negotiation-Agreement Based Selection Model
5.4.1 Calculation of Index of Selection
5.5  Evaluation
5.6  Implementation

Reference Information and Books

For more information on agent technology in general, see “Artificial Life and Agent Technology” and “Introduction to Multi-Agent Systems,” and for simulation applications, see “MAS (Multi-Agent Simulation System) by Pyhton.

For Semantic Web services, see “Modeling Semantic Web Services: The Web Service Modeling Language,

Enabling Semantic Web Services: The Web Service Modeling Ontology

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