Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
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Artigo de evento 11 Citação(ões) na Scopus On the use of UML.P for modeling a real application as a planning problem(2006) VAQUERO, T. S.; Flavio Tonidandel; BARROS, L. N. DE; SILVA, J. R.There is a great interest in the planning community to apply all developments already achieved in the area to real applications. Such scenario makes the community focus on Knowledge Engineering (KE) applied in modeling of planning problems and domains. In this paper, we propose the use of UML for Planning Approach, denominated UML.P, during planning domain modeling process. We also discuss the exposure of UML.P to a real application, e.g., the sequencing car problems in an assembly line. This modeling experience, using a classical manufacturing problem, provides some insights and considerations that can contribute to a general KE process for planning. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.Artigo de evento 38 Citação(ões) na Scopus itSIMPLE2.0: An integrated tool for designing planning domains(2007-09-26) VAQUERO, T. S.; ROMERO, V.; Flavio Tonidandel; SILVA, J. R.A great effort has been made today in the area of Artificial Intelligence for defining reliable automated planning systems that can be applied in real life applications. That leads to the need of a systematic design process, in which the initial phases are not neglected and where Knowledge and Requirement Engineering tools have a fundamental role for supporting designers. Following this principle, this paper presents the evolution of the tool itSIMPLE which implements a KE integrated environment where designers can perform knowledge acquisition, domain modeling, domain model analysis, model testing, maintenance and plan analysis processes by using different well-known languages such as UML, Petri Nets, PDDL and XML, each one of them with its best contribution. The tool supports users in an organized object-oriented domain design process with a friendly and easy-to-use interface. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.- The Inclusive Potential of Educational Robotics(2022-10-18) CANET, A. M. D.; Flavio Tonidandel© 2022 IEEE.Educational Robotics favors the development of reasoning, teamwork and the socialization of knowledge. In this work, using this educational technology as a learning mediator, its inclusive potential for students with disabilities in elementary school questioned. The objective of this study is verify the inclusive potential of educational robotics and what are the difficulties the robotics teachers present in the inclusion of students with disabilities. This research is qualitative and quantitative. It involves a bibliographic research and a questionnaire answered by the Professor of Support to Pedagogical Projects of Educational Technologies (PAPP TEC) of the Municipal Schools of São Bernardo do Campo. The questionnaire used the free Google Forms tool. The multiple-choice answers received statistical treatment and the open question analyzed using Bardin's content analysis. In the questionnaire and in the semi-structured interview, the teachers pointed out the need for training actions, collaborative action by support professionals (Technical Guidance Team and specialist teachers in the areas of disabilities) and access to assistive technology for the development of the inclusive potential of robotics.
- Case adaptation by segment replanning for case-based planning systems(2005-08-26) Flavio Tonidandel; RILLO, M.An adaptation phase is crucial for a good and reasonable Case-Based Planning (CBP) system. The adaptation phase is responsible for finding a solution in order to solve a new problem. If the phase is not well designed, the CBP system may not solve the desirable range of problems or the solutions will not have appropriate quality. In this paper, a method called CASER - Case Adaptation by Segment Replanning - is presented as an adaptation rule for case-based planning system. The method has two phases: the first one completes a retrieved case as an easy-to-generate solution method. The second phase improves the quality of the solution by using a generic heuristic in a recursive algorithm to determine segments of the plan to be replanned. The CASER method does not use any additional knowledge, and it can find as good solutions as those found by the best generative planners. © Springer-Verlag Berlin Heidelberg 2005.
- Reading PDDL, writing an object-oriented model(2006-10-27) Flavio Tonidandel; VAQUERO, T. S.; SILVA, J. R.There are many efforts towards a combination of planning systems and real world applications. Although the PDDL is in constant evolution, which improves its capability to describe real domains, it is still a declarative language that is not so simple to be used by the non-planning community. This paper describes a translation process that reads a domain specification in PDDL and transforms it into an object-oriented model, more specifically into a version of UML for planning approaches. This translation process can let a designer read PDDL domains and verify it with some powerful tool like itSIMPLE or GIPO, or it can allow a planning system that only reads object-oriented models to run in domains described in PDDL originally. © Springer-Verlag Berlin Heidelberg 2006.
- Analysis of the PSO Parameters for a Robots Positioning System in SSL(2019-07-08) LAUREANO, M. A. P.; Flavio Tonidandel© 2019, Springer Nature Switzerland AG.The changes in the Small Size League rules have brought greater possibilities of playing. With the increased complexity of soccer matches, the positioning of the robots has become important as a defense and attack mechanism. The learning of opposing team game playing has been shown to be effective, but an SSL soccer match indicates the need for solutions that analyze the strategy of the opposing team during the game and make any necessary adaptations. This paper proposes the use of the Particle Swarm Optimization (PSO) algorithm as an option to determine the positioning during the match. A prototype has been developed to validate the configuration parameters. Experiments in a simulator, analysis of game logs and results in a real matches have demonstrated the feasibility of applying the PSO algorithm to find the robots positions.