Intelligent tutoring systems (ITSs) are effective to provide instruction for students in several situations. Many works have been using gamification by adding game elements to learning contexts aiming to engage students and to drive desired learning behaviours. However, the design of gamified ITS should deal with a huge variability. Software product lines (SPLs) promise to offer rapid product development and more affordable development costs to build software from the same family. A key factor to successfully implement a product-line approach is to structure commonalities and variabilities into a product line architecture (PLA). In this paper, we propose a PLA for developing gamified ITSs that uses an ontology-driven feature modelling strategy. We illustrate how our architecture could be applied to instantiate a product on the basic math domain. We also discuss a set of implications of using it as well as how it could support the evolution/changing of gamified ITSs.
Derramamentos de óleo são desastres ambientes com potencial de ocasionar impactos severos, não apenas no ambiente, mas também na economia e na sociedade. Um grande derramamento ocorreu na costa brasileira em 2019, impactando diversas localizações por vários meses. Uma estratégia de resposta padrão é a implantação de sistemas de supervisão e monitoramento, principalmente usando tecnologias de sensoriamento remoto. A aplicação de veículos autônomos, desempenhando tarefas de monitoramento e rastreamento do processo de dispersão de óleo, oferece informações de forma eficiente, flexível e em tempo real, sendo essencial para o emprego de outras estratégias de combate ao derramamento de óleo. Essa abordagem é proposta no âmbito de uma ação emergencial envolvendo veículos autônomos aéreos e de superfície aquática para o monitoramento de ambientes marítimos. Este trabalho apresenta os resultados de uma revisão em três componentes essenciais para o projeto de sistemas para esse contexto: busca, rastreamento e sensoriamento.
Intelligent tutoring systems (ITSs) are effective to provide instruction for students in several situations. Many works have been using gamification by adding game elements to learning contexts aiming to engage students and to drive desired learning behaviours. However, the design of gamified ITS should deal with a huge variability. Software product lines (SPLs) promise to offer rapid product development and more affordable development costs to build software from the same family. A key factor to successfully implement a product-line approach is to structure commonalities and variabilities into a product line architecture (PLA). In this paper, we propose a PLA for developing gamified ITSs that uses an ontology-driven feature modelling strategy. We illustrate how our architecture could be applied to instantiate a product on the basic math domain. We also discuss a set of implications of using it as well as how it could support the evolution/changing of gamified ITSs.
This work describes a distributed solution using Model Predictive Control (MPC), including the Optimal Reciprocal Collision Avoidance (ORCA) algorithm applied to mobile robots following individual trajectories. Differential drive robots are used, defined by their position on the plane and controlled by velocity commands. Based on an explicit system model and velocity constraints designated by the collision avoidance algorithm, the MPC-ORCA computes optimal control actions to minimize a cost function over a prediction horizon. The methodology can efficiently handle multivariable control systems using state-space model representation and convex quadratic programming (QP). Simulation results show that the combined strategy MPC-ORCA provided smooth and collision-free trajectories in a changing environment. It also requires no trajectory replanning neither direct communication between agents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.