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Is abortion a pseudo-problem? 堕胎是一个伪问题吗?
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871218
A. Browne
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引用次数: 1
Fame as a Value Concept 名望作为一种价值观念
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871214
D. Lackey
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引用次数: 1
The Social Nature of Self and Morality for Husserl, Schutz, Marx, and Mead 胡塞尔、舒茨、马克思、米德的自我与道德的社会本质
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/1987125
W. O'Meara
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引用次数: 2
Impartiality and Practical Reason 公正与实践理性
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871219
L. B. Kelley
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引用次数: 2
Notes on Bergmann's New Ontology and Account of Relations 伯格曼的新本体论和关系论注释
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871227
Lorenzo Peña
Recent work of Gustav Bergmann develops an ontological framework within which an account of relations has been sketched out. The approach is a kind of new logical atomism which has some of the features of an Aristotelian hylomorphism (of sorts). It recognizes a number of categories and groups of a hylomorphic kind, chiefly “determinates” and “subdeterminates” — the latter only indirectly or implicitly. Winsome though it is, the approach is flawed by certain difficulties it gives rise to, among them inability to speak of subdeterminates and failure of a relation to be had by a referent towards a relatum. Instead of having a sense, a relation is conceived of as a determinate which enters an arrangement whose existence and nature are not properly accounted for. Finally, Bergmann’s Ideal Language is assayed and shown not to be as useful philosophically in itself as he takes it to be.
古斯塔夫·伯格曼(Gustav Bergmann)最近的工作发展了一个本体论框架,其中对关系的描述已经勾画出来。这种方法是一种新的逻辑原子论,具有亚里士多德同形论的一些特征。它承认许多同质性的范畴和组,主要是"限定词"和"次限定词",后者只是间接地或含蓄地。虽然这种方法很迷人,但它也有缺陷,因为它引起了一些困难,其中包括不能说到从属决定因素,以及指称物对相对人的关系的失败。一种关系没有意义,而是被认作一种规定,这种规定进入一种安排,而这种安排的存在和性质都没有得到适当的说明。最后,对伯格曼的理想语言进行了分析,并证明它本身并不像他所认为的那样在哲学上有用。
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引用次数: 1
Aristotle’s Great Clock: Necessity, Possibility and the Motion of the Cosmos in De Caelo I.12 亚里士多德的大钟:宇宙的必然性、可能性和运动,《论卡洛》I.12
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/1987127
J. Bogen, J. Mcguire
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引用次数: 11
No-Fault Theories of Regulative Justification 规制正当性的无过错理论
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871211
A. Graybosch
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引用次数: 0
The Logic of Learning 学习的逻辑
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/1987123
William E. Murnion
. I use the term logical and relational learning (LRL) to re-fer to the sub- eld of machine learning and data mining that is concerned with learning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining and constitutes a general class of techniques and methodology for learning from structured data (such as graphs, networks, relational databases) and background knowledge. During the course of its existence, logical and relational learning has changed dramatically. Whereas early work was mainly concerned with logical issues (and even program synthesis from examples), in the 90s its focus was on the discovery of new and interpretable knowledge from structured data, often in the form of rules or patterns. Since then the range of tasks to which log- ical and relational learning has been applied has signicantly broadened and now covers almost all machine learning problems and settings. Today, there ex- ist logical and relational learning methods for reinforcement learning, statistical learning, distance-and kernel-based learning in addition to traditional symbolic machine learning approaches. At the same time, logical and relational learning problems are appearing everywhere. Advances in intelligent systems are enabling the generation of high-level symbolic and structured data in a wide variety of domains, including the semantic web, robotics, vision, social networks, and the life sciences, which in turn raises new challenges and opportunities for logical and relational learning. In this talk, I will start by providing a gentle introduction to the field of logical and relational learning and then continue with an overview of the logical foundations for learning, which are concerned with various settings for learning (such as learning from entailment, from interpreta-tions and from proofs), with the logical inference rules and generality relationships used, and the effects they have on the properties of the search-space. More details can be found in
。我使用术语“逻辑和关系学习”(LRL)来指代机器学习和数据挖掘的子领域,它与表达逻辑或关系表示的学习有关。它是归纳逻辑编程、(统计)关系学习和多关系数据挖掘的结合,构成了从结构化数据(如图、网络、关系数据库)和背景知识中学习的一般技术和方法。在其存在的过程中,逻辑和关系学习发生了巨大的变化。早期的工作主要关注逻辑问题(甚至从例子中合成程序),而在90年代,它的重点是从结构化数据中发现新的和可解释的知识,通常以规则或模式的形式出现。从那时起,应用逻辑和关系学习的任务范围已经大大扩大,现在几乎涵盖了所有机器学习问题和设置。今天,除了传统的符号机器学习方法外,还有用于强化学习、统计学习、基于距离和核的学习的逻辑和关系学习方法。与此同时,逻辑和关系学习问题无处不在。智能系统的进步使得在各种领域生成高级符号和结构化数据成为可能,包括语义网、机器人、视觉、社交网络和生命科学,这反过来又为逻辑和关系学习提出了新的挑战和机遇。在这次演讲中,我将首先对逻辑和关系学习领域进行一个温和的介绍,然后继续概述学习的逻辑基础,这些基础涉及各种学习设置(例如从蕴涵中学习,从解释中学习和从证明中学习),使用逻辑推理规则和一般关系,以及它们对搜索空间属性的影响。更多细节可在
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引用次数: 1
Causes, Ends, and the Units of Selection 原因、目的和选择的单位
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/19871213
H. R. Holcomb
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引用次数: 0
The Concept of Intentionality: Husserl’s Development from the Brentano Period to the Logical Investigations 意向性概念:胡塞尔从布伦塔诺时期到逻辑研究的发展
Pub Date : 1986-04-01 DOI: 10.5840/PRA1986/1987124
H. Philipse
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引用次数: 8
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