4,779 research outputs found

    Professor\u27s Role in Motivating Students to Attend Class

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    Current trends in early intervention programs for handicapped children birth to 3 years

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    How can early intervention for handicapped children from birth to age 3 be effectively implemented? The author collected information related to this topic. The writer was teaching in an Early Childhood classroom for handicapped children 3-5 years old. The possibility that children from birth to 3 could be educated in a public school setting was of great interest. It seemed that because of the requirements of PL 99-457, the public schools might take more of a role in servicing the birth to 3 handicapped population in the future. How could infants and toddlers be serviced? What strategies were being used effectively with the very young? What were the requirements of the new law? These were some of the questions the researcher endeavored to answer. The author wanted a first hand look at what was happening in early intervention locally. Interviews and observations at selected early intervention centers added information about what practices were being used in southeastern Wisconsin. Readers of this research will find factual information about Part H, about Wisconsin\u27s method of implementation, about research in early intervention, and about current practices in Wisconsin

    Reinforcement Learning

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    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

    Communication: A Human Factor

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    Multiagent Learning Through Indirect Encoding

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    Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding

    Examination of Eco-Behavioral Assessments Designed for Understanding Complex Behaviors and Environments.

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    Second-generation intervention research requires methods for overcoming challenges to understanding complex learning ecologies and interactions of students. Eco-behavioral assessments (EBAs) are one solution to past intervention research challenges. EBAs record the effects of ecological variables in students’ behavior and daily interactions. The utility of EBAs in second-generation research has increased substantially. Numerous EBAs now exist for use with all ages of learners and provide a valid, reliable, and cost effective method for intervention research. This paper examines 18 EBAs as well as software systems designed to support and enhance the use of EBAs. The examination serves as a comprehensive resource to better understand how EBAs can be used in answering complex questions about students’ learning and for advancing second-generation research

    Education, Society, and the K-12 Learner

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    An exploration of selected components of the education profession: purpose of education. American education system, education and the legal system, child and adolescent development, and diversity. Part 1: Educational History and Policy Part 2: Educational Psychologyhttps://egrove.olemiss.edu/open/1001/thumbnail.jp
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