An Agent Approach to Manage Heterogeneous and Distributed Knowledge

2020 ◽  
Vol 10 (1) ◽  
pp. 27-48
Author(s):  
Davy Monticolo ◽  
Inaya Lahoud
Keyword(s):  
21St Century ◽  
Information Sources ◽  
Information Network ◽  
Multi Agent System ◽  
Agent System ◽  
Semantic Approach ◽  
Distributed Knowledge ◽  
Technical Report ◽  
Multi Agent ◽  
Heterogeneous Knowledge

Emphasis on knowledge and information is one of the challenges of the 21st century to differentiate the intelligent business enterprises. Enterprises have to develop their organization in order to capture, manage, and use information in a context of continually changing technology. Indeed, knowledge and information are completely distributed in the information network of the company. In addition, knowledge is, by nature, heterogeneous, since it is provided from different information sources like the software, the technical report, the meeting statements, etc. The authors present in this article the architecture of a multi-agent system, which allows the capitalization of the distributed and heterogeneous knowledge. They then present how the agents help business experts to design ontologies in detailing this problem and how the agents extract knowledge from different user databases by using a semantic approach.

A Semantic Approach for Multi-Agent System Design

2017 ◽  
pp. 941-968
Author(s):  
Rosario Girardi ◽  
Adriana Leite
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Code Generation ◽  
Tax Law ◽  
Multi Agent System ◽  
Agent System ◽  
Semantic Approach ◽  
First Case ◽  
Multi Agent ◽  
Application Design

Automating software engineering tasks is crucial to achieve better productivity of software development and quality of software products. Knowledge engineering approaches this challenge by supporting the representation and reuse of knowledge of how and when to perform a development task. Therefore, knowledge tools for software engineering can turn more effective the software development process by automating and controlling consistency of modeling tasks and code generation. This chapter introduces the description of the domain and application design phases of MADAE-Pro, an ontology-driven process for agent-oriented development, along with how reuse is performed between these sub-processes. Two case studies have been conducted to evaluate MADAE-Pro from which some examples of the domain and application design phases have been extracted and presented in this chapter. The first case study assesses the Multi-Agent Domain Design sub-process of MADAE-Pro through the design of a multi-agent system family of recommender systems supporting alternative (collaborative, content-based, and hybrid) filtering techniques. The second one evaluates the Multi-Agent Application Design sub-process of MADAE-Pro through the design of InfoTrib, a Tax Law recommender system that provides recommendations based on new tax law information items using a content-based filtering technique.

A Semantic Approach for Multi-Agent System Design

2015 ◽  
pp. 192-218
Author(s):  
Rosario Girardi ◽  
Adriana Leite
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Code Generation ◽  
Tax Law ◽  
Multi Agent System ◽  
Agent System ◽  
Semantic Approach ◽  
First Case ◽  
Multi Agent ◽  
Application Design

Automating software engineering tasks is crucial to achieve better productivity of software development and quality of software products. Knowledge engineering approaches this challenge by supporting the representation and reuse of knowledge of how and when to perform a development task. Therefore, knowledge tools for software engineering can turn more effective the software development process by automating and controlling consistency of modeling tasks and code generation. This chapter introduces the description of the domain and application design phases of MADAE-Pro, an ontology-driven process for agent-oriented development, along with how reuse is performed between these sub-processes. Two case studies have been conducted to evaluate MADAE-Pro from which some examples of the domain and application design phases have been extracted and presented in this chapter. The first case study assesses the Multi-Agent Domain Design sub-process of MADAE-Pro through the design of a multi-agent system family of recommender systems supporting alternative (collaborative, content-based, and hybrid) filtering techniques. The second one evaluates the Multi-Agent Application Design sub-process of MADAE-Pro through the design of InfoTrib, a Tax Law recommender system that provides recommendations based on new tax law information items using a content-based filtering technique.

A Semantic Approach for Multi-Agent System Design

2018 ◽  
pp. 1711-1739
Author(s):  
Rosario Girardi ◽  
Adriana Leite
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Code Generation ◽  
Tax Law ◽  
Multi Agent System ◽  
Agent System ◽  
Semantic Approach ◽  
First Case ◽  
Multi Agent ◽  
Application Design

Automating software engineering tasks is crucial to achieve better productivity of software development and quality of software products. Knowledge engineering approaches this challenge by supporting the representation and reuse of knowledge of how and when to perform a development task. Therefore, knowledge tools for software engineering can turn more effective the software development process by automating and controlling consistency of modeling tasks and code generation. This chapter introduces the description of the domain and application design phases of MADAE-Pro, an ontology-driven process for agent-oriented development, along with how reuse is performed between these sub-processes. Two case studies have been conducted to evaluate MADAE-Pro from which some examples of the domain and application design phases have been extracted and presented in this chapter. The first case study assesses the Multi-Agent Domain Design sub-process of MADAE-Pro through the design of a multi-agent system family of recommender systems supporting alternative (collaborative, content-based, and hybrid) filtering techniques. The second one evaluates the Multi-Agent Application Design sub-process of MADAE-Pro through the design of InfoTrib, a Tax Law recommender system that provides recommendations based on new tax law information items using a content-based filtering technique.

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