DesignLab
  • Departamento de Engenharia Mecatrônica
    Escola Politécnica - Universidade de São Paulo

Formal Knowledge Engineering for Planniong: Pre and Post-desgn Analysis

Jose Reinaldo Silva; Javier Martinez Silva; Tiago Stegun Vaquero

Journal 
 
Autor 
Jose Reinaldo Silva; Javier Martinez Silva; Tiago Stegun Vaquero 
Book Title 
Knowledge Engineering Tools and Techniques for AI Planning 
Volume 
Página(s) 
47-65 
DOI 
10.1007/978-3-030-38561-3_3 
Published 
2020 
Month 
Tipo de Documento 
Seção de Livro 
Abstract 
The interest and scope of the area of autonomous systems have been steadily growing in the last 20 years. Artificial intelligence planning and scheduling is a promising technology for enabling intelligent behavior in complex autonomous systems. To use planning technology, however, one has to create a knowledge base from which the input to the planner will be derived. This process requires advanced knowledge engineering tools, dedicated to the acquisition and formulation of the knowledge base, and its respective integration with planning algorithms that reason about the world to plan intelligently. In this chapter, we shortly review the existing knowledge engineering tools and methods that support the design of the problem and domain knowledge for AI planning and scheduling applications (AI P&S). We examine the state-of-the-art tools and methods of knowledge engineering for planning & scheduling (KEPS) in the context of an abstract design process for acquiring, formulating, and analyzing domain knowledge. Planning quality is associated with requirements knowledge (pre-design) which should match properties of plans (post-design). While examining the literature, we analyze the design phases that have not received much attention, and propose new approaches to that, based on theoretical analysis and also in practical experience in the implementation of the system itSIMPLE. 
Keywords 
planning design; post-design analysis; planning automation; automation by planning 
Publisher 
Knowledge Engineering Tools and Techniques for AI Planning, M. Vallati and Diane Kitchin (eds.), Springer 
URL 
https://link.springer.com/chapter/10.1007/978-3-030-38561-3_3