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A Semantic Approach for Multi-Agent System Design

A Semantic Approach for Multi-Agent System Design

Rosario Girardi, Adriana Leite
Copyright: © 2015 |Pages: 27
ISBN13: 9781466664852|ISBN10: 1466664851|EISBN13: 9781466664869
DOI: 10.4018/978-1-4666-6485-2.ch010
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MLA

Girardi, Rosario, and Adriana Leite. "A Semantic Approach for Multi-Agent System Design." Human Factors in Software Development and Design, edited by Saqib Saeed, et al., IGI Global, 2015, pp. 192-218. https://doi.org/10.4018/978-1-4666-6485-2.ch010

APA

Girardi, R. & Leite, A. (2015). A Semantic Approach for Multi-Agent System Design. In S. Saeed, I. Bajwa, & Z. Mahmood (Eds.), Human Factors in Software Development and Design (pp. 192-218). IGI Global. https://doi.org/10.4018/978-1-4666-6485-2.ch010

Chicago

Girardi, Rosario, and Adriana Leite. "A Semantic Approach for Multi-Agent System Design." In Human Factors in Software Development and Design, edited by Saqib Saeed, Imran Sarwar Bajwa, and Zaigham Mahmood, 192-218. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6485-2.ch010

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Abstract

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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