causal reasoning 中文意思是什麼
causal reasoning
解釋
因果推理-
The relation between counterfactual thinking and causal reasoning
反事實思維與因果推理的關系 -
The development of children ' s social information reasoning with causal conditionals
推理方向與規則維度對兒童因果推理的影響 -
The influence of beliefs and covariation in causal reasoning
16歲兒童因果判斷過程中經驗信息與共變信息的作用 -
Causal knowledge representation and nonmonotonic reasoning models in law consultant systems
法律知識的因果表達和非單調推理模型 -
It also captures nonmonotonicity in causal reasoning. ( 4 ) specific causal reasoning systems are constructed
（ 4 ）以默認規則表達方法為基礎，建立了具體領域的因果推理應用模型。 -
This paper proposes a knowledge map model for representing and reasoning causal knowledge as an overlay in the knowledge grid
模糊認知圖是一種具有語義的基於計算的融合因果知識表示與推理的一種圖模型。 -
Training studies on rule - based causal reasoning in children aged 3 to 4 years
4歲兒童規則因果推理能力的訓練研究 -
More specifically, the research provides an appropriate framework of entities among which causal relations are to hold ; it also develops a theoretical framework of event causation, under which the structures and elements of causal relations holding among these ontological entities can be described ; it gives a general representation tool for event causation supported by the ontological and theoretical frameworks, under which causal relations can be formalized as causal rules for practical reasoning, e. g., predictive reasoning, in which nonmonotonicity, as well as the other general properties and the nature of elements involved, can be captured ; it constructs computational frameworks for abstract causal reasoning models, such as causal prediction, causal explanation, and causal diagnosis ; and it finally extends and utilizes these abstract reasoning models to formalize causal knowledge in specific domains to develop practical causal reasoning systems for ai research, e. g., story understanding and legal reasoning. the research i s original from several aspects as follows : ( 1 ) the analysis of the internal structure of events provides a fundamental ontology for causal relations
具體地說，此項研究在以下幾個方面做了工作：它對因果關系存在的實體給出了一個合適的框架；它建立了一個基於事件的因果關系的理論框架，在這個框架下因果關系的結構與因素能夠被合理描述；它提供了一個得到本體論與因果理論支持的基於事件的因果關系的一般表達方式，使因果關系能夠被形式化為在實際推理（例如預測推理）中應用的因果規則，並使因果關系的非單調性以及其它的一般性質得到體現；它構造了基於事件的因果關系的抽象推理模型，特別是因果預測、因果解釋和因果診斷；最後它把因果推理模型推廣應用到具體的領域以建立實際的ai系統，例如在故事理解和法律推理中的應用。 -
Bayesian networks for causal reasoning in situation assessment
用於態勢評估中因果推理的貝葉斯網路 -
Effect of rule dimensions and reasoning direction on children ' s causal reasoning
文本閱讀過程中目標焦點的預期推理 -
And in the computer - aided law agency, causal reasoning of legal rules is the core of the system and results can help lawyers have more efficient law documentation. the use of causation is one of basic intelligence for human beings
在法律推理應用中，把法律條文表示成因果規則，按照所建立的因果模型進行有效的推理，作為計算機輔助法律事務所的核心系統。 -
A analytical theory is established by putting causal elements into partial states and actions, which deepens our understanding of event causation at the level of partial states and actions. ( 3 ) a causal rule representation is mapped into default logic formalism, based on the examination of general properties of causation. the default rule representation provides a concise syntactic and semantic formalism for potential causal relations to be used in causal reasoning models such as predicting, explaining and diagnosing
（ 2 ）通過對因果關系的可能類型的全面分析，給出了因果關系的結構與組成元素，特別是區分了潛在的因果關系內的原因、結果和因果場中的激活條件，並且把它們同半狀態與動作對應起來，建立了關于因果關系的分析理論。 -
The specification of a satisfactory general analysis of causal relations has long proved difficult. the research described in this thesis is an attempt to develop a computational theory of causal relations between temporally ordered events, and more precisely causal relations among partial states and actions, from the causal reasoning point of view
本論文中所描述的研究工作是試圖建立一個因果關系的可計算性理論，而且從因果推理的觀點來看，這里的因果關系被限制在基於時間上有序事件之間，更精確地說是存在於部分狀態與動作之間的。 -
This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning
本課程探索關于機率法則形成的歷史和爭論，機率理論如何運用於具體的認知過程，它與人類對因果的理解有著怎樣的關系，以及新的關于因果模型計算方法怎樣為理解人類的機率推理過程提供架構。 -
Dynamic causality diagram was first proposed by professor zhang qin in 1994, it is a mathematics tool combined with probability and graph theory, just like the belief network, its characteristic is to provide the method of uncertain knowledge representation and agility reasoning, it adopts nodes to represent random variables in the domain and directional edges between nodes to represent causal relationship between variables, linkage intensity to represent the strength of the link between these variables, it supports the forms of reasoning from cause to effect and from effect to cause and together
動態因果圖由張勤教授1994年提出，它與信度網類似，是概率論與圖論結合的一種數學工具，其特點是提供不確定知識的表達和靈活的推理方法:用節點表示事件或變量，有向邊表示因果關系，並用連接強度來表示因果關系的強度，支持由原因到結果的正向推理方式和由結果到原因的反向推理方式以及正反向混合推理方式。
分享友人