Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

Recolha e conceptualização de experiências de atividades robóticas baseadas em planos para melhoria de competências no longo prazo

Robot learning is a prominent research direction in intelligent robotics. Robotics involves dealing with the issue of integration of multiple technologies, such as sensing, planning, acting, and learning. In robot learning, the long term goal is to develop robots that learn to perform tasks and continuously improve their knowledge and skills through observation and exploration of the environment and interaction with users. While significant research has been performed in the area of learning motor behavior primitives, the topic of learning high-level representations of activities and classes of activities that, decompose into sequences of actions, has not been sufficiently addressed. Learning at the task level is key to increase the robots’ autonomy and flexibility. High-level task knowledge is essential for intelligent robotics since it makes robot programs less dependent on the platform and eases knowledge exchange between robots with different kinematics. The goal of this thesis is to contribute to the development of cognitive robotic capabilities, including supervised experience acquisition through human-robot interaction, high-level task learning from the acquired experiences, and task planning using the acquired task knowledge. A framework containing the required cognitive functions for learning and reproduction of high-level aspects of experiences is proposed. In particular, we propose and formalize the notion of Experience-Based Planning Domains (EBPDs) for long-term learning and planning. A human-robot interaction interface is used to provide a robot with step-by-step instructions on how to perform tasks. Approaches to recording plan-based robot activity experiences including relevant perceptions of the environment and actions taken by the robot are presented. A conceptualization methodology is presented for acquiring task knowledge in the form of activity schemata from experiences. The conceptualization approach is a combination of different techniques including deductive generalization, different forms of abstraction and feature extraction. Conceptualization includes loop detection, scope inference and goal inference. Problem solving in EBPDs is achieved using a two-layer problem solver comprising an abstract planner, to derive an abstract solution for a given task problem by applying a learned activity schema, and a concrete planner, to refine the abstract solution towards a concrete solution. The architecture and the learning and planning methods are applied and evaluated in several real and simulated world scenarios. Finally, the developed learning methods are compared, and conditions where each of them has better applicability are discussed.Aprendizagem de robôs é uma direção de pesquisa proeminente em robótica inteligente. Em robótica, é necessário lidar com a questão da integração de várias tecnologias, como percepção, planeamento, atuação e aprendizagem. Na aprendizagem de robôs, o objetivo a longo prazo é desenvolver robôs que aprendem a executar tarefas e melhoram continuamente os seus conhecimentos e habilidades através da observação e exploração do ambiente e interação com os utilizadores. A investigação tem-se centrado na aprendizagem de comportamentos básicos, ao passo que a aprendizagem de representações de atividades de alto nível, que se decompõem em sequências de ações, e de classes de actividades, não tem sido suficientemente abordada. A aprendizagem ao nível da tarefa é fundamental para aumentar a autonomia e a flexibilidade dos robôs. O conhecimento de alto nível permite tornar o software dos robôs menos dependente da plataforma e facilita a troca de conhecimento entre robôs diferentes. O objetivo desta tese é contribuir para o desenvolvimento de capacidades cognitivas para robôs, incluindo aquisição supervisionada de experiência através da interação humano-robô, aprendizagem de tarefas de alto nível com base nas experiências acumuladas e planeamento de tarefas usando o conhecimento adquirido. Propõe-se uma abordagem que integra diversas funcionalidades cognitivas para aprendizagem e reprodução de aspetos de alto nível detetados nas experiências acumuladas. Em particular, nós propomos e formalizamos a noção de Domínio de Planeamento Baseado na Experiência (Experience-Based Planning Domain, or EBPD) para aprendizagem e planeamento num âmbito temporal alargado. Uma interface para interação humano-robô é usada para fornecer ao robô instruções passo-a-passo sobre como realizar tarefas. Propõe-se uma abordagem para extrair experiências de atividades baseadas em planos, incluindo as percepções relevantes e as ações executadas pelo robô. Uma metodologia de conceitualização é apresentada para a aquisição de conhecimento de tarefa na forma de schemata a partir de experiências. São utilizadas diferentes técnicas, incluindo generalização dedutiva, diferentes formas de abstracção e extração de características. A metodologia inclui detecção de ciclos, inferência de âmbito de aplicação e inferência de objetivos. A resolução de problemas em EBPDs é alcançada usando um sistema de planeamento com duas camadas, uma para planeamento abstrato, aplicando um schema aprendido, e outra para planeamento detalhado. A arquitetura e os métodos de aprendizagem e planeamento são aplicados e avaliados em vários cenários reais e simulados. Finalmente, os métodos de aprendizagem desenvolvidos são comparados e as condições onde cada um deles tem melhor aplicabilidade são discutidos.Programa Doutoral em Informátic

Semantic-based adaptive mission planning for unmanned underwater vehicles

Current underwater robotic platforms rely upon waypoint-based scripted missions which are described by the operator a-priori. This renders systems incapable of reacting to the unexpected. In this thesis, we claim that the ability to autonomously adapt the decision making process is the key to facilitating the change over from human intervention to intelligent autonomy. We identify goal-based declarative mission planning as an attractive solution to autonomous adaptability because it combines autonomous decision making with higher levels of human interaction. Goal-based mission planning requires the use of abstract knowledge representation and situation awareness to link the prior knowledge provided by the operator with the information coming from the processed sensor data. To achieve this, we propose a semantic-based knowledge representation framework that allows this integration of prior and processed information among all different agents available in the platform. In order to evaluate adaptive mission planning techniques, we also introduce a novel metric which measures the proximity between plans. We demonstrate that this metric is better informed than previous metrics for measuring the adaptation process. In this thesis we implement three different approaches to goal-based mission planning in order to investigate which approach is most appropriate under different circumstances. The first approach, continuous mission planning, focusses on long-term deployment. This approach is based on a continuous re-assessment of the status of the mission environment. Using our proximity metric, we evaluated this approach and show that there is a high degree of similarity between our approach and the humanly driven adaptation, both in a known static environment and in a partially-known dynamic discoverable environment. The second, service-oriented mission planning, makes use of the semantic framework to provide autonomous mission planning for the dynamic discovery of the services published by the different agents in the system. This allows platform independence, easing the manual creation of mission plans, and robustness to changes. We show that this approach produces the same plans as the baseline which was explicitly provided with the platform configuration. The last approach, mission plan repair, handles the scenario where small changes occur in the mission environment and there are limited resources for planning. We develop and deploy a mission plan repair approach within a semantic-based autonomous planning system in a real underwater vehicle. Experiments demonstrate that the integrated system is capable of providing mission adaptation for maintaining the operability of the host platform in the face of unexpected events

Approaches to learning and cognitive processes

This study examined traditional instruction and problem-based learning (PBL) approaches to teaching and the extent to which they foster the development of desirable cognitive processes, including metacognition, critical thinking, physical intuition, and problem solving among undergraduate physics students. The study also examined students' approaches to learning and their perceived role as physics students. The research took place in the context of advanced courses of electromagnetism at a Canadian research university. The cognitive science, expertise, physics and science education, instructional psychology, and discourse processes literature provided the framework and background to conceptualize and structure this study. A within-stage mixed-model design was used and a number of instruments, including a survey, observation grids, and problem sets were developed specifically for this study. A special one-week long problem-based learning (PBL) intervention was also designed. Interviews with the instructors participating in the study provided complementary data.Findings include evidence that students in general engage in metacognitive processes in the organization of their personal study time. However, this potential, including the development of other cognitive processes, might not be stimulated as much as it could in the traditional lecture instructional context. The PBL approach was deemed as more empowering for the students. An unexpected finding came from the realisation that a simple exposure to a structured exercise of problem-solving (pre-test) was sufficient to produce superior planning and solving strategies on a second exposure (post-test) even for the students who had not been exposed to any special treatment. Maturation was ruled out as a potential threat to the validity of this finding. Another promising finding appears to be that the problem-based learning (PBL) intervention tends to foster the development of cognitive competencies, particularly physical intuition, even if it was only implemented for a short period of time. Other findings relate to the nature of the cognitive actions and activities that the students engage in when learning to solve electromagnetism problems in a PBL environment for the first time and the tutoring actions that guide students in this context

Aspects spatial et temporel de l'intégration visuelle au niveau de la voie dorsale du système visuel du chat : le cortex suprasylvien latéral comme modèle

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal