本の詳細
形式
ペーパーバック
ページ数
171
言語
英語
公開されました
Sep 1, 1993
出版社
Amer Assn for Artificial
ISBN-10
0929280482
ISBN-13
9780929280486
説明
In the realm of artificial intelligence, the 1993 workshop on Case-Based Reasoning brought together some of the brightest minds to explore a burgeoning area of research. This collection of papers delves into the cognitive processes behind case-based reasoning, showcasing innovative ideas and methodologies that emerged from the discussions and presentations. Each piece reflects a commitment to advancing the understanding of how past experiences can inform and enhance decision-making in future scenarios.
The contributions highlight various approaches to case retrieval, adaptation, and the implications of these strategies for intelligent systems. Readers will find a blend of theoretical insights and practical applications, providing a comprehensive overview of the landscape of case-based reasoning at the time. The workshop fostered an environment of collaboration and creativity, resulting in work that continues to influence the field.
As a historical reference, this compilation is essential for scholars and practitioners interested in the evolution of case-based reasoning. It captures the excitement and potential of a technology that aims to mimic human problem-solving capabilities by drawing lessons from previous experiences, making it a valuable resource for future research and applications in AI.
The contributions highlight various approaches to case retrieval, adaptation, and the implications of these strategies for intelligent systems. Readers will find a blend of theoretical insights and practical applications, providing a comprehensive overview of the landscape of case-based reasoning at the time. The workshop fostered an environment of collaboration and creativity, resulting in work that continues to influence the field.
As a historical reference, this compilation is essential for scholars and practitioners interested in the evolution of case-based reasoning. It captures the excitement and potential of a technology that aims to mimic human problem-solving capabilities by drawing lessons from previous experiences, making it a valuable resource for future research and applications in AI.