• Education of Primary School Mathematics
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• v.9 no.2 s.18
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• pp.75-87
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• 2005

For Probabilistic Reasoning Ability is useful to predict uncertain fact from information, it's getting more important. But when we consider the actual condition of teaching Probabilistic Reasoning Ability, it doesn't correspond with its importance. So the purpose of this study is, by developing Basic Contents of Probabilistic Reasoning Teaching; by developing and applying Probabilistic Reasoning Teaching Program, to study how the application of it effects the progress of the student's Probabilistic Reasoning Ability.

• Journal of Science Education
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• v.33 no.1
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• pp.69-76
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• 2009

Probabilistic reasoning and combinational reasoning are essential to build a logical thinking and a process of thinking dealing with everyday life as well as scientific knowledge. This research aims at finding the optimal period to teach reasoning to the students who haven't developed probabilistic reasoning and combinational reasoning. The treatment program was performed for 20 students from each grade who couldn't develop two parts of reasoning. The treatment program using baduk stones and cards was performed repeatedly, focusing on the specific activities. After four weeks of treatment program, the test to check the development of probabilistic reasoning and combinational reasoning was performed again and the changes of reasoning development were identified. After giving treatment program for reasoning development, 15.0%, 25.0% and 40.0% of improvement in the 4th, the 5th, the 6th graders respectively were shown. With regard to the combinational reasoning, the results showed the improvement of 20.0% in the 4th grades, 25.0% in the 5th graders and 63.2% in the 6th graders. As a result of research in the above, students, who were not formed probabilistic reasoning and combinational reasoning, could be known to be enhanced through learning, but to fail to be formed the qualitative change like the cognitive development. It is expected that this research can contribute to the improvement of students' cognitive level and there would be more active researches in different fields to improve the cognitive level of the 6th graders who are in their optimal periods to learn two parts of reasoning.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3124-3134
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• 1993

Diagnosis is composed of two different but interrelated steps ; retrieving the sensory responses f the system and reasoning the state of the system through the given sensor data. This paper explains the probabilistic nature of reasoning involved in the diagnosis when the uncertainties are inevitably included in experts' diagnostic decision making. Uncertainties in decision making are experts' personal experiences, preferences, and system's coherent characteristics. In order to ensure a consistent decision based on the same responses from the system, expert system technology is adopted with the Bayesian reasoning scheme.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.61-69
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• 2001

We present a probabilistic reasoning method for inferring knowledge about mathematical truth before an automated theorem prover completes a proof. We use a Bayesian analysis to update beleif in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• Journal of Information Management
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• v.21 no.1
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• pp.34-52
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• 1990

The probabilistic reasoning method using Bayesian theory is studied to use uncertain or incomplete knowledge in ways that take the uncertainty into account. This method presented for knowledge acquisition process of expert system to evaluate certainty degree of input knowledge.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.266-267
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• 2008

We argue that agents may benefit from strategic ignorance in argumentation-based negotiation (ABN). We assume our agents are selfish, myopic, and residing in open systems. Some analytical results that can be used for designing agent reasoning on strategic ignorance are provided.

• Journal of the Korean Society for information Management
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• v.7 no.1
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• pp.68-78
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• 1990

This paper shows the fuzzy reasoning process that is specifically designed to deal wit the inexactness or fuzziness in the expert systems. The impact of overall fuzzy reasoning reviewed when knowledge with certainty is provided. Also, the example of fuzzy reazoning used at probabilistic inference is presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.357-366
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• 1999

Inheritance reasoning has been widely used in the area of common sense reasoning in artificial intelligence. Although many inheritance reasoners have been proposed in artificial intelligence literature, most previous reasoning systems are lack of clear semantics, thus sometimes provide anomalous conclusions. In this paper, we describe a set-oriented inheritance reasoner and propose a method of resolving conflicts with clear semantics of defeasible rules. The semantics of default rule is provided by statistical analysis of $\chi$ method, and likelihood of rule is computed based on the evidence in the past. Two basic rules, specificity and generality, are defined to resolve conflicts effectively in the process of reasoning. We show that the mutual tradeoff between specificity and generality 추 prevent many anomalous results from occurring in traditional inheritance reasoners. An algorithm is provided. and some typical examples are given to show how the specificity/generality rules resolve conflicts effectively in inheritance reasoning.