Análise de pós-design para aplicações de planejamento em IA.

Abstract

AcknowledgementsFirst and above all, I praise God, the almighty for providing me this opportunity and granting me the capability to proceed successfully. This thesis appears in its current form due to the guidance and contribution, both directly and indirectly, of several people. I would therefore like to offer my sincere thanks to all of them.I would firstly like to thank my supervisors, Professor José Reinaldo Silva and Professor J. Christopher Beck, for being great advisors and role models, for all of your suggestions and ideas, and for taking the time to explain and discuss all aspects of research to me.Thanks to all my colleagues in the following labs: Design Lab at the University of São Paulo, TIDEL at the University of Toronto, and IAAA at the University Center of FEI -you have all helped me at some point.Thanks to my parents, Gilberto and Sônia, for years of support and love.Last and most, thanks to my lovely sweet wife Patrícia for all her love and support, and for her great patience and understandings. AbstractSince the end of the 1990s there has been an increasing interest in the application of AI planning techniques to solve real-life problems. In addition to characteristics of academic problems, such as the need to reason about actions, real-life problems require detailed knowledge elicitation, engineering, and management. A systematic design process in which Knowledge and Requirements Engineering techniques and tools play a fundamental role is necessary in such applications. Research on Knowledge Engineering for planning and scheduling has created tools and techniques to support the design process of planning domain models. However, given the natural incompleteness of the knowledge, practical experience in real applications such as space exploration has shown that, even with a disciplined process of design, requirements from different viewpoints (e.g. stakeholders, experts, users) still emerge after plan generation, analysis and execution.The central thesis of this dissertation is that an post-design analysis phase in the development of AI planning applications leads to richer knowledge models and, consequently, to high-performance and high-quality plans. In this dissertation, we investigate how hidden knowledge and requirements can be acquired and re-used during a plan analysis phase that follows model design and how they affect planning performance. We describe a post-design framework called postDAM that combines (1) a knowledge engineering tool for requirements acquisition and plan evaluation, (2) a virtual prototyping environment for the analysis and simulation of plans, (3) a database system for storing plan evaluations, and (4) an ontological reasoning system for knowledge re-use and discovery.Our framework demonstrates that post-design analysis supports the discovery of missing requirements and guides the model refinement cycle. We present three case studies using benchmark domains and eight state-of-the-art planners. Our results demonstrate that significant improvements in plan quality and ...