Causal knowledge and children’s possibility judgments

2021 ◽  
Author(s):  
Brandon W. Goulding ◽  
Emily Elizabeth Stonehouse ◽  
Ori Friedman
Keyword(s):  
Causal Knowledge

New Method of Knowledge Acquisition of Aid Decision-Making for Missile-Launching

2014 ◽  
Vol 1006-1007 ◽  
pp. 685-688
Author(s):  
Guo Bao Ding ◽  
Hao Xing ◽  
Lian Bing Wang ◽  
Dan Li
Keyword(s):  
Decision Making ◽  
Knowledge Acquisition ◽  
Power Supply System ◽  
Supply System ◽  
Pulse Power Supply ◽  
Causal Knowledge ◽  
Diagram Method ◽  
New Knowledge ◽  
Knowledge Acquisition Methods ◽  
Aid Decision

Acquiring causal knowledge of abnormity is essential to Missile-Launching reliably. There are lots of Knowledge Acquisition methods. But it is absence for usage and maintenance support process. So it is necessary to start the research on new knowledge acquisition technology of aid Decision-Making for Missile-Launching. Based on the Usage and Maintenance-Support Process, this thesis acquires knowledge with the ESD and CESD (Converse Event Sequence Diagram) method. First, this thesis gives the concept of CESD. Then, in order to adapt the CESD model of the complex systems more effectively, this paper expands the CESD framework and provides a software frame of computer aided ESD study. Finally, the operation of pulse power supply system is analyzed on the basis of the improved ESD and CESD. This sample shows the applicability of ESD and CESD methodology in knowledge acquisition technology of aid Decision-Making for Missile-Launching.

DECISION MAKING FOR NETWORK HEALTH ASSESSMENT IN AN INTELLIGENT INTRUSION DETECTION SYSTEM ARCHITECTURE

2004 ◽  
Vol 03 (02) ◽  
pp. 281-306 ◽  
Author(s):  
AMBAREEN SIRAJ ◽  
RAYFORD B. VAUGHN ◽  
SUSAN M. BRIDGES
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Health Assessment ◽  
Fuzzy Rule ◽  
Fuzzy Cognitive Maps ◽  
Causal Knowledge ◽  
Knowledge Reasoning ◽  
Decision Engine

This paper describes the use of artificial intelligence techniques in the creation of a network-based decision engine for decision support in an Intelligent Intrusion Detection System (IIDS). In order to assess overall network health, the decision engine fuses outputs from different intrusion detection sensors serving as "experts" and then analyzes the integrated information to present an overall security view of the system for the security administrator. This paper reports on the workings of a decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research, Mississippi State University. The decision engine uses Fuzzy Cognitive Maps (FCM)s and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process.

scholarly journals At udskyde det uundgåelige: Om brugen af kausal viden i forebyggende tiltag

2012 ◽  
Vol 9 (17) ◽  
Author(s):  
Morten Hulvej Rod ◽  
Tine Curtis
Keyword(s):  
Public Health ◽  
Social Interaction ◽  
Health Agency ◽  
Public Health Research ◽  
Causal Knowledge ◽  
Local Public Health ◽  
The Future ◽  
Prevention And Health Promotion ◽  
Local Public Health Agency ◽  
Public Health Practitioners

This paper discusses the heavy reliance upon a particular kind of causal knowledge in prevention and health promotion. Based on ethnographic fieldwork with prevention professionals working with interventions targeting teenage drinking in Denmark, the paper argues that, while attempting to provide predictions for the future, prevention creates certain problems for itself in the moments of social interaction where it is practiced. The paper suggests that prevention can be seen as an attempt at postponing the future and through empirical examples it is illustrated how this project causes a number of practical problems to prevention professionals. The paper begins by sketching the causal epistemology that dominates current public health research. Next, ethnographic descriptions of (i) an educational intervention in Danish schools and (ii) a meeting for parents arranged by a local public health agency provide the material for discussing the practical use of causal knowledge. It is shown that this knowledge becomes contradicted and undermined in the social interaction between public health practitioners and their target groups, and that – paradoxically – this knowledge tends to actualize the very phenomenon it seeks to prevent. The paper employs Bourdieu’s distinction between two modes of anticipatory intelligence, the project and the protention, and argues that, in the interaction between prevention professionals and target group, the widespread use of causal knowledge might inhibit and counteract the situational competencies of prevention professionals.

scholarly journals Reducing the Complexity of Casual Representation in Bayesian Belief Network

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1243
Author(s):  
Yit Yin Wee ◽  
Shing Chiang Tan ◽  
KuokKwee Wee
Keyword(s):  
Causal Model ◽  
Knowledge Engineering ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Joint Probability Distribution ◽  
Causal Knowledge ◽  
Belief Network ◽  
Track Record ◽  
Probability Table ◽  
Causal Framework

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool.   Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model.  Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN.   Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

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