Qualitative Representations: How People Reason and Learn about the Continuous World (The MIT Press)
暫譯: 質性表徵:人們如何推理和學習連續世界 (麻省理工學院出版社)
Kenneth D. Forbus
- 出版商: MIT
- 出版日期: 2019-01-29
- 售價: $2,240
- 貴賓價: 9.5 折 $2,128
- 語言: 英文
- 頁數: 440
- 裝訂: Hardcover
- ISBN: 0262038943
- ISBN-13: 9780262038942
海外代購書籍(需單獨結帳)
相關主題
商品描述
An argument that qualitative representations―symbolic representations that carve continuous phenomena into meaningful units―are central to human cognition.
In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations―symbolic representations that carve continuous phenomena into meaningful units―are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language.
Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.
商品描述(中文翻譯)
一個論點是,質性表徵——將連續現象切割成有意義單位的符號表徵——對人類認知至關重要。
在這本書中,Kenneth Forbus 提出質性表徵是認知科學中最深奧的謎題之一的關鍵:我們如何推理和學習周圍的連續現象。Forbus 主張質性表徵——將連續現象切割成有意義單位的符號表徵——是人類認知的核心。質性表徵為常識推理提供了基礎,因為它們使得在非常少的數據下進行實用推理成為可能;這使得質性表徵成為自然語言語義的一個有用組成部分。質性表徵還為科學和工程中的專家推理提供了基礎,通過明確化可能發生的廣泛類別,並使因果模型得以建立,這有助於在需要時指導更定量知識的應用。質性表徵對於創造更具人類特徵的人工智慧系統也很重要,這些系統具備空間推理、視覺、問題回答和理解自然語言的能力。
Forbus 討論了包括知識表徵和推理的基本概念、質性過程理論、質性模擬和變化推理、組合建模、質性空間推理以及學習和概念變化等主題。他的論點值得注意,因為他提出了一種質性推理的方法,其中類比推理和學習扮演著關鍵角色,並且他整合了各種證據,包括人工智慧系統的表現。認知科學家會發現 Forbus 對質性表徵的闡述頗具啟發性;人工智慧科學家則會重視 Forbus 對質性表徵的新方法及其提供的概述。