Research Initiation: Induction of Casual Theories
研究启动:休闲理论归纳
基本信息
- 批准号:8908260
- 负责人:
- 金额:$ 6.92万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:1989
- 资助国家:美国
- 起止时间:1989-07-15 至 1992-01-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The researcher will investigate the acquisition of general theory of causality by observing examples of events and their consequences. A theory of causality is information about the principles that lead one to believe that an action has a necessary consequence. For example, people tend to select a cause which is close in space and time to an effect. The work builds on prior implementation of a computer model which uses a theory of causality as an explicit bias to facilitate the acquisition of a theory of causation (i.e., inference rules encode knowledge of the effect of actions, such as "glass objects break when struck with sufficient force."). The new work is an investigation of computational models for acquiring these general principles from examples. Input to the learning program will consists of a temporal sequence of propositions representing the states of the world. The input representation will be constructed by simulation (to allow systematic experimentation by varying parameters such as the amount of noise in the data) and by hand coding examples of causal changes from children's introductory science texts (to insure that the learning program will be able to account for a wide variety of common causal relationships).
研究者将调查一般理论的习得情况 通过观察事件的例子及其 后果 因果关系理论是关于 使人相信一个行为具有 必要的后果。 例如,人们倾向于选择 在空间和时间上与结果接近的原因。 工作 建立在计算机模型的先前实现之上,该计算机模型使用 因果关系理论作为一种明确的偏见,以促进 因果关系理论的获得(即,推理规则 对动作效果的知识进行编码,例如“玻璃物体 当被足够的力量击打时会破裂。"). 新作品是一个 调查的计算模型,以获得这些一般 从例子中的原则。 对学习计划的输入将 由一个时间序列的命题,代表 世界各国。 输入表示将是 通过模拟构建(以允许通过 变化的参数,例如数据中的噪声量),以及 通过手工编码的因果变化的例子,从儿童的 介绍性科学文本(以确保学习计划 将能够解释各种常见的因果关系, 关系)。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Michael Pazzani其他文献
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
- DOI:
10.1007/pl00011669 - 发表时间:
2001-08-01 - 期刊:
- 影响因子:3.100
- 作者:
Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra - 通讯作者:
Sharad Mehrotra
Expert-Informed, User-Centric Explanations for Machine Learning
由专家提供信息、以用户为中心的机器学习解释
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Michael Pazzani;Severine Soltani, Robert Kaufman - 通讯作者:
Severine Soltani, Robert Kaufman
A Computational Theory of Learning Causal Relationship^
学习因果关系的计算理论^
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Reiationships;Michael Pazzani - 通讯作者:
Michael Pazzani
Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrač, Sašo Džeroski
- DOI:
10.1023/a:1018098519027 - 发表时间:
1996-04-01 - 期刊:
- 影响因子:2.900
- 作者:
Michael Pazzani - 通讯作者:
Michael Pazzani
CTSHIV: a knowledge-based system for the management of HIV-infected patients
CTSHIV:用于管理艾滋病毒感染者的知识系统
- DOI:
- 发表时间:
1997 - 期刊:
- 影响因子:0
- 作者:
Michael Pazzani;Darryl See;Ranjit Iyer;Edison Schroeder;Jeremiah Tilles - 通讯作者:
Jeremiah Tilles
Michael Pazzani的其他文献
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{{ truncateString('Michael Pazzani', 18)}}的其他基金
RAPID: Explainable Machine Learning for Analysis of COVID-19 Chest CT
RAPID:用于分析 COVID-19 胸部 CT 的可解释机器学习
- 批准号:
2026809 - 财政年份:2020
- 资助金额:
$ 6.92万 - 项目类别:
Standard Grant
CC*IIE Networking Infrastructure: University of California Riverside's Science DMZ
CC*IIE 网络基础设施:加州大学河滨分校的 Science DMZ
- 批准号:
1440543 - 财政年份:2014
- 资助金额:
$ 6.92万 - 项目类别:
Standard Grant
From Computer Data to Human Knowledge: A Cognitive Approach to Knowlege Discovery and Data Mining
从计算机数据到人类知识:知识发现和数据挖掘的认知方法
- 批准号:
9731990 - 财政年份:1998
- 资助金额:
$ 6.92万 - 项目类别:
Continuing Grant
Learning Probabilistic Relational Concepts
学习概率关系概念
- 批准号:
9310413 - 财政年份:1994
- 资助金额:
$ 6.92万 - 项目类别:
Continuing Grant
Long and Medium-Term Research: Information-Based Approachesto Learning Relational Concepts
长期和中期研究:基于信息的关系概念学习方法
- 批准号:
9201842 - 财政年份:1992
- 资助金额:
$ 6.92万 - 项目类别:
Standard Grant
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