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Betting On Hope
One clutzy advice columnist.
One champion cowboy.
And an entire small town rooting for love.
Advice columnist Maggie Hope never dreamed sheâ€™d be shaking hands with champion horse trainer Tru Monahan over a high-stakes bet, especially one that involves horses. And saddles. And everything else a city girl like Maggie feels uncomfortable around. But after filling in for a coworker and interviewing the handsome cowboy, she finds herself doing just that. Anything to save her advice column.
Despite Maggieâ€™s two left feet, Tru is bound and determined to bring out her inner cowgirl by teaching her to ride a cutting horse, trained to separate cattle from the rest of the herd. While her riding improves, their attraction intensifi es, but Tru knows he can never let her into his heartâ€”for her own good.
In Wishing Springs, a community full of meddling but well-meaning townsfolk, Maggie discovers the home sheâ€™s always longed for. But sheâ€™s holding something backâ€”a secret that could destroy her reputation and any future sheâ€™s ever hoped for with the cowboy she mightust love.
About the Author
Debra Clopton is a multiaward winning novelist who was first published in 2005 and has more than 22 novels to her credit. Along with her writing, Debra helps her husband teach the youth at their local Cowboy Church. Debra is the author of the acclaimed Mule Hollow Matchmaker Series, the place readers tell her they wish was real. Her goal is to shine a light toward God while she entertains readers with her words.
Reinforcement Learning For Adaptive Dialogue Systems
The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation.
This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies.
The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development - not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
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