Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks Through Natural Interactions

Gluck, Kevin A., Laird, John E., Lupp, Julia

  • 出版商: Summit Valley Press
  • 出版日期: 2019-09-10
  • 售價: $1,575
  • 貴賓價: 9.8$1,544
  • 語言: 英文
  • 頁數: 354
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 026203882X
  • ISBN-13: 9780262038829
  • 相關分類: 機器人製作 Robots
  • 立即出貨(限量) (庫存=2)

商品描述

Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other.

Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche capabilities of current machine learning systems and the generality, flexibility, and in situ robustness of human instruction and learning. Drawing on expertise from multiple disciplines, this Str ngmann Forum Report explores how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other.

The contributors consider functional knowledge requirements, the ontology of interactive task learning, and the representation of task knowledge at multiple levels of abstraction. They explore natural forms of interactions among humans as well as the use of interaction to teach robots and software agents new tasks in complex, dynamic environments. They discuss research challenges and opportunities, including ethical considerations, and make proposals to further understanding of interactive task learning and create new capabilities in assistive robotics, healthcare, education, training, and gaming.

Contributors
Tony Belpaeme, Katrien Beuls, Maya Cakmak, Joyce Y. Chai, Franklin Chang, Marc Destefano, Mark d'Inverno, Kenneth D. Forbus, Simon Garrod, Kevin A. Gluck, Wayne D. Gray, James Kirk, Kenneth R. Koedinger, Parisa Kordjamshidi, John E. Laird, Christian Lebiere, Stephen C. Levinson, Elena Lieven, John K. Lindstedt, Aaron Mininger, Tom Mitchell, Shiwali Mohan, Ana Paiva, Katerina Pastra, Peter Pirolli, Charles Rich, Katharina J. Rohlfing, Paul S. Rosenbloom, Nele Russwinkel, Dario D. Salvucci, Matthew-Donald D. Sangster, Matthias Scheutz, Julie A. Shah, Catherine Sibert, Candace Sidner, Michael Spranger, Luc Steels, Suzanne Stevenson, Terrence C. Stewart, Arthur Still, Andrea Stocco, Niels A. Taatgen, Andrea L. Thomaz, J. Gregory Trafton Han L. J. van der Maas, Paul Van Eecke, Kurt VanLehn, Anna-Lisa Vollmer, Janet Wiles, Robert E. Wray III, Matthew Yee-King

商品描述(中文翻譯)

各領域的專家探討人類和人工智能代理如何通過彼此之間的自然互動快速學習全新任務。

人類並不受限於一組固定的先天或預編程任務。我們通過語言和其他形式的自然互動快速學習,提高自己的表現並教授他人我們所學。理解自然互動中獲得新任務的機制是一個持續的挑戰。人工智能、認知科學和機器人技術的進步正在引領我們走向具有人類能力的未來系統。然而,當前機器學習系統高度專門化的能力與人類指導和學習的普遍性、靈活性和現場韌性之間存在巨大差距。本《Str ngmann Forum Report》結合多個學科的專業知識,探討人類和人工智能代理如何通過彼此之間的自然互動快速學習全新任務。

貢獻者們考慮了功能知識需求、互動任務學習的本體論以及在多個抽象層次上的任務知識表示。他們探討了人類之間的自然互動形式,以及在複雜、動態環境中使用互動教導機器人和軟體代理新任務的方法。他們討論了研究挑戰和機會,包括倫理考慮,並提出了進一步理解互動任務學習並在輔助機器人、醫療保健、教育、培訓和遊戲方面創造新能力的建議。

貢獻者包括:Tony Belpaeme、Katrien Beuls、Maya Cakmak、Joyce Y. Chai、Franklin Chang、Marc Destefano、Mark d'Inverno、Kenneth D. Forbus、Simon Garrod、Kevin A. Gluck、Wayne D. Gray、James Kirk、Kenneth R. Koedinger、Parisa Kordjamshidi、John E. Laird、Christian Lebiere、Stephen C. Levinson、Elena Lieven、John K. Lindstedt、Aaron Mininger、Tom Mitchell、Shiwali Mohan、Ana Paiva、Katerina Pastra、Peter Pirolli、Charles Rich、Katharina J. Rohlfing、Paul S. Rosenbloom、Nele Russwinkel、Dario D. Salvucci、Matthew-Donald D. Sangster、Matthias Scheutz、Julie A. Shah、Catherine Sibert、Candace Sidner、Michael Spranger、Luc Steels、Suzanne Stevenson、Terrence C. Stewart、Arthur Still、Andrea Stocco、Niels A. Taatgen、Andrea L. Thomaz、J. Gregory Trafton、Han L. J. van der Maas、Paul Van Eecke、Kurt VanLehn、Anna-Lisa Vollmer、Janet Wiles、Robert E. Wray III、Matthew Yee-King。