Web-based learning systems are one of the most interesting topics in the area of the application of computers to education. Collaborative learning, as an important principle in constructivist learning theory, is an im...Web-based learning systems are one of the most interesting topics in the area of the application of computers to education. Collaborative learning, as an important principle in constructivist learning theory, is an important instruction mode for open and distance learning systems. Through collaborative learning, students can greatly improve their creativity, exploration capability, and social cooperation. This paper used an agent-based coordination mechanism to respond to the requirements of an efficient and motivating learn-ing process. This coordination mechanism is based on a Web-based constructivist collaborative learning system, in which students can learn in groups and interact with each other by several kinds of communica-tion modes to achieve their learning objectives efficiently and actively. In this learning system, artificial agents represent an active part in the collaborative learning process; they can partially replace human in-structors during the multi-mode interaction of the students.展开更多
In complex product design,lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts.However,since complex produ...In complex product design,lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts.However,since complex products involve intensive multi-domain knowledge,preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain.In recent years,enormous challenges are involved in the design process,within the increasing complexity of preference.This article mainly proposes a novel decision-making method based on generalized abductive learning(G-ABL)to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively.The proposed G-ABL framework,containing three cores:classifier,abductive kernel,and abductive machine,supports preference integration from data and fuzzy knowledge.In particular,a subtle improvement is presented for WK-means based on the entropy weight method(EWM)to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant.Furthermore,fuzzy comprehensive evaluation(FCE)and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels.Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set.Finally,an engineering application is provided to verify the effectiveness of the proposed method,and the superiority of which is illustrated by comparative analysis.展开更多
基金Supported by the 985 Foundation of Tsinghua University (No. JC2000011)
文摘Web-based learning systems are one of the most interesting topics in the area of the application of computers to education. Collaborative learning, as an important principle in constructivist learning theory, is an important instruction mode for open and distance learning systems. Through collaborative learning, students can greatly improve their creativity, exploration capability, and social cooperation. This paper used an agent-based coordination mechanism to respond to the requirements of an efficient and motivating learn-ing process. This coordination mechanism is based on a Web-based constructivist collaborative learning system, in which students can learn in groups and interact with each other by several kinds of communica-tion modes to achieve their learning objectives efficiently and actively. In this learning system, artificial agents represent an active part in the collaborative learning process; they can partially replace human in-structors during the multi-mode interaction of the students.
基金the National Key R&D Program of China(2018YFB1700900).
文摘In complex product design,lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts.However,since complex products involve intensive multi-domain knowledge,preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain.In recent years,enormous challenges are involved in the design process,within the increasing complexity of preference.This article mainly proposes a novel decision-making method based on generalized abductive learning(G-ABL)to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively.The proposed G-ABL framework,containing three cores:classifier,abductive kernel,and abductive machine,supports preference integration from data and fuzzy knowledge.In particular,a subtle improvement is presented for WK-means based on the entropy weight method(EWM)to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant.Furthermore,fuzzy comprehensive evaluation(FCE)and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels.Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set.Finally,an engineering application is provided to verify the effectiveness of the proposed method,and the superiority of which is illustrated by comparative analysis.
