Scalable Intelligence for Scheduling Systems

A personalização é um aspeto chave de uma interação homem-computador efetiva. Numa era em que existe uma abundância de informação e tantas pessoas a interagir com ela, de muitas maneiras, a capacidade de se ajustar aos seus utilizadores é crucial para qualquer sistema moderno. A criação de sistemas adaptáveis é um domínio bastante complexo que necessita de métodos muito específicos para ter sucesso. No entanto, nos dias de hoje ainda não existe um modelo ou arquitetura padrão para usar nos sistemas adaptativos modernos. A principal motivação desta tese é a proposta de uma arquitetura para modelação do utilizador que seja capaz de incorporar diferentes módulos necessários para criar um sistema com inteligência escalável com técnicas de modelação. Os módulos cooperam de forma a analisar os utilizadores e caracterizar o seu comportamento, usando essa informação para fornecer uma experiência de sistema customizada que irá aumentar não só a usabilidade do sistema mas também a produtividade e conhecimento do utilizador. A arquitetura proposta é constituída por três componentes: uma unidade de informação do utilizador, uma estrutura matemática capaz de classificar os utilizadores e a técnica a usar quando se adapta o conteúdo. A unidade de informação do utilizador é responsável por conhecer os vários tipos de indivíduos que podem usar o sistema, por capturar cada detalhe de interações relevantes entre si e os seus utilizadores e também contém a base de dados que guarda essa informação. A estrutura matemática é o classificador de utilizadores, e tem como tarefa a sua análise e classificação num de três perfis: iniciado, intermédio ou avançado. Tanto as redes de Bayes como as neuronais são utilizadas, e uma explicação de como as preparar e treinar para lidar com a informação do utilizador é apresentada. Com o perfil do utilizador definido torna-se necessária uma técnica para adaptar o conteúdo do sistema. Nesta proposta, uma abordagem de iniciativa mista é apresentada tendo como base a liberdade de tanto o utilizador como o sistema controlarem a comunicação entre si. A arquitetura proposta foi desenvolvida como parte integrante do projeto ADSyS - um sistema de escalonamento dinâmico - utilizado para resolver problemas de escalonamento sujeitos a eventos dinâmicos. Possui uma complexidade elevada mesmo para utilizadores frequentes, daí a necessidade de adaptar o seu conteúdo de forma a aumentar a sua usabilidade. Com o objetivo de avaliar as contribuições deste trabalho, um estudo computacional acerca do reconhecimento dos utilizadores foi desenvolvido, tendo por base duas sessões de avaliação de usabilidade com grupos de utilizadores distintos. Foi possível concluir acerca dos benefícios na utilização de técnicas de modelação do utilizador com a arquitetura proposta.Personalization is a key aspect of effective Human-Computer Interaction. The ability to adjust itself to its users is crucial to any modern system, in an era where there is so much information and so many people interacting in so many ways. The creation of adaptable systems is a complex domain that requires very specific methods in order to be successful. However, still today there is no standard model or architecture to use on a modern adaptive system. The main motivation of this dissertation is to propose an architecture for user modelling that is able to incorporate separate modules required to create a scalable intelligence system with user modelling techniques. The modules cooperate in order to analyse users and characterize their behaviour, using that information to provide a customized system experience that will increase not only the usability of the system but also the user’s productivity and knowledge. The proposed architecture is composed by three components: a user information unit, a mathematical structure able to classify users and the technique to use when adapting content. The user information unit is responsible for knowing the several types of individuals that can use the system, for capturing every part of relevant interaction between itself and its users and also contains the database which stores that information. The mathematical structure is the user classifier and is in charge of analysing the users and classifying them into one of three roles: beginner, intermediate or expert. Both Bayesian and Artificial Neural Networks are used, and an explanation on how to prepare and train them to deal with user information is provided. With the user role defined, a proper technique to adapt system’s content is required. In this work, a Mixed-Initiative approach is detailed which is based on allowing both the user and the system to gain control in the communication between them. The proposed architecture was developed as part of the ADSyS project. ADSyS is a Dynamic Scheduling system to solve scheduling problems subject to dynamic events. It has a high complexity even for frequent users, hence the need for the adaptation of its content to increase its usability. In order to evaluate the contribution of this work, a computational study of the user recognition was developed, as well as two usability evaluation sessions with distinct users. It was possible to conclude about the benefits of employing user modelling techniques with the proposed architecture

Selection Constructive based Hyper-heuristic for Dynamic Scheduling

A função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real. Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização – Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico- para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica.Scheduling plays an important role in manufacturing systems. It produces a scheduling plan, in order to share resources to produce several different products in the same time period. However, dynamic adaptation and optimization is a critical need in real-world manufacturing scheduling systems, since contemporary manufacturing organizations have a dynamic nature, where disturbances on working conditions and requirements occur on a continuous basis. Disturbances often arise unexpectedly, and can be for example: urgent job arrival, job cancelation, due date change, delay in the arrival, among others. These dynamic events must be taken into account, since they may have a major impact on the scheduling plan, they can disorder the plan making it ineffective. Therefore, manufacturing environments require immediate response to these dynamic events, using a real-time rescheduling method, in order to minimize the effect of such unexpected events in the performance of the production’ system. As result, scheduling systems should have the ability of automatically and intelligently maintain real-time adaptation and optimization to efficiently update the scheduling plan to the unexpected events. This way, the organization keeps clients satisfied and achieves its objectives (costs minimized and profits maximized). This dissertation addresses the problem of incorporating new tasks in a scheduling plan already generated by the scheduling system. Therefore, it proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with dynamic events that can occur over time in a manufacturing environment, with the main goal of maintaining the current scheduling plan feasible and most robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. The viability of the proposed approach is tested by performing a set of experiments and analysing the results achieved. From the obtained results it is possible to conclude that the use of a selection constructive hyper-heuristic could be advantageous on solving dynamic adaptation optimization problems

A review and classification of computer-based manufacturing scheduling tools

Computer-based manufacturing scheduling tools can play a key role in the management of industrial operations, as obtaining economic and reliable schedules is at the core of excellence in customer service and of efficiency in manufacturing companies. As a consequence, this topic has been receiving an increasing interest in the last decades, resulting in a number of case studies and descriptions of implementation of these tools. However, to the best of our knowledge, there is no review of these cases in order to classify existing references and to identify relevant issues still not properly addressed. Therefore, in this paper we carry out a systematic review of case studies of manufacturing scheduling tools. In order to provide a coherent taxonomy for the analysis of these tools, we develop a classification based on the functionalities of the manufacturing scheduling tools. Using this framework, existing contributions are classified and discussed, and a number of conclusions and open issues are identified. We hope that our work can establish a coherent picture of the topic so it serves as a starting point for future research

Intelligent Mobile Learning Interaction System (IMLIS): A Personalized Learning System for People with Mental Disabilities

The domain of learning context for people with special needs is a big challenge for digi- tal media in education. This thesis describes the main ideas and the architecture of a system called Intelligent Mobile Learning Interaction System (IMLIS) that provides a mobile learning environment for people with mental disabilities. The design of IMLIS aims to enhance personalization aspects by using a decision engine, which makes deci- sions based on the user s abilities, learning history and reactions to processes. It allows for adaptation, adjustment and personalization of content, learning activities, and the user interface on different levels in a context where learners and teachers are targeting autonomous learning by personalized lessons and feedback. Due to IMLIS dynamic structure and flexible patterns, it is able to meet the specific needs of individuals and to engage them in learning activities with new learning motivations. In addition to support- ing learning material and educational aspects, mobile learning fosters learning across context and provides more social communication and collaboration for its users. The suggested methodology defines a comprehensive learning process for the mentally disabled to support them in formal and informal learning. We apply knowledge from the field of research and practice to people with mental disabilities, as well as discuss the pedagogical and didactical aspects of the design

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Supporting Multiple Stakeholders in Agile Development

Agile software development practices require several stakeholders with different kinds of expertise to collaborate while specifying requirements, designing and modeling software, and verifying whether developers have implemented requirements correctly. We studied 112 requirements engineering (RE) tools from academia and the features of 13 actively maintained behavior-driven development (BDD) tools, which support various stakeholders in specifying and verifying the application behavior. Overall, we found that there is a growing tool specialization targeted towards a specific type of stakeholders. Particularly with BDD tools, we found no adequate support for non-technical stakeholders —- they are required to use an integrated development environment (IDE) —- which is not adapted to suit their expertise. We argue that employing separate tools for requirements specification, modeling, implementation, and verification is counter-productive for agile development. Such an approach makes it difficult to manage associated artifacts and support rapid implementation and feedback loops. To avoid dispersion of requirements and other software-related artifacts among separate tools, establish traceability between requirements and the application source code, and streamline a collaborative software development workflow, we propose to adapt an IDE as an agile development platform. With our approach, we provide in-IDE graphical interfaces to support non-technical stakeholders in creating and maintaining requirements concurrently with the implementation. With such graphical interfaces, we also guide non-technical stakeholders through the object-oriented design process and support them in verifying the modeled behavior. This approach has two advantages: (i) compared with employing separate tools, creating and maintaining requirements directly within a development platform eliminates the necessity to recover trace links, and (ii) various natively created artifacts can be composed into stakeholder-specific interactive live in-IDE documentation. These advantages have a direct impact on how various stakeholders collaborate with each other, and allow for rapid feedback, which is much desired in agile practices. We exemplify our approach using the Glamorous Toolkit IDE. Moreover, the discussed building blocks can be implemented in any IDE with a rich-enough graphical engine and reflective capabilities

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks