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Titlebook: Case-Based Reasoning Research and Development; 29th International C Antonio A. Sánchez-Ruiz,Michael W. Floyd Conference proceedings 2021 Sp

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21#
發(fā)表于 2025-3-25 04:40:25 | 只看該作者
https://doi.org/10.1007/978-3-030-79382-1 on everyday tasks in open or semi-open domains, where exist a variety of situations that a planning (and execution) agent must deal with. This paper first introduces a new, generic structure for representing tasks and task plans. The paper, then, introduces a generic situation structure and a metho
22#
發(fā)表于 2025-3-25 09:30:16 | 只看該作者
23#
發(fā)表于 2025-3-25 12:00:05 | 只看該作者
https://doi.org/10.1007/978-3-030-86957-1artificial intelligence; case based reasoning; cognition; computer vision; correlation analysis; data min
24#
發(fā)表于 2025-3-25 15:55:41 | 只看該作者
978-3-030-86956-4Springer Nature Switzerland AG 2021
25#
發(fā)表于 2025-3-25 20:01:54 | 只看該作者
Case-Based Reasoning Research and Development978-3-030-86957-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
26#
發(fā)表于 2025-3-26 00:13:55 | 只看該作者
27#
發(fā)表于 2025-3-26 06:13:54 | 只看該作者
28#
發(fā)表于 2025-3-26 11:44:03 | 只看該作者
,Task and Situation Structures for?Case-Based Planning,first introduces a new, generic structure for representing tasks and task plans. The paper, then, introduces a generic situation structure and a methodology of situation handling. The proposed structures support encoding all domain knowledge in . while avoiding hard-coding domain rules.
29#
發(fā)表于 2025-3-26 15:51:25 | 只看該作者
0302-9743 th AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI)..978-3-030-86956-4978-3-030-86957-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
30#
發(fā)表于 2025-3-26 17:08:10 | 只看該作者
Sander J. J. Leemans,Artem Polyvyanyyed, and propose a Bayesian belief update as a possible way to infer both the parameters of the agent and the content of their case base. We illustrate our ideas with the simple application of an agent learning grammar rules throughout a sequence of observations.
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