A hybrid reasoning system with diagrams and sentences

2002 ◽  
Author(s):  
H. Sawamura ◽  
K. Kiyozuka
Keyword(s):  
Reasoning System ◽  
Hybrid Reasoning

scholarly journals A hybrid reasoning system for supporting estuary modelling

2005 ◽  
Vol 7 (3) ◽  
pp. 185-198
Author(s):  
Sara Passone ◽  
Vahid Nassehi ◽  
Paul W. H. Chung
Keyword(s):  
Genetic Algorithm ◽  
Physical Phenomenon ◽  
Computing Methods ◽  
Case Based Reasoning ◽  
Illustrative Case ◽  
Detailed Knowledge ◽  
Reasoning System ◽  
Hybrid Reasoning ◽  
Illustrative Case Study ◽  
Case Based

In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated software effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme, a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system's library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find the optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Using these modules the steps that yield the best solution for a problem from the available hydrographic data under a set of specified conditions are explained. This is further elucidated by an illustrative case study which shows the applicability of the present CBEM system under realistic conditions. This case deals with the simulation of salinity distribution in the Tay estuary (Scotland, UK).

Electric Vehicles Online Monitoring System Design Based on the Hybrid Reasoning

2013 ◽  
Vol 278-280 ◽  
pp. 878-883
Author(s):  
Wei Guo Zhang ◽  
Yi Chu ◽  
Ming Yu Zhao ◽  
Fu Bin Hong
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Electric Vehicles ◽  
Online Monitoring ◽  
Base Layer ◽  
Reasoning System ◽  
Hybrid Reasoning ◽  
Base Management ◽  
Machine Interface ◽  
Online Monitoring System

This paper designs a set of electric vehicles online monitoring system with SOA framework, which includes acquisition layer, network layer, support platform, knowledge base layer and presentation layer, and knowledge layer includes a knowledge base management system, reasoning system and man-machine interface system. Also, this paper constructs a knowledge expert system with hybrid rule-based reasoning method of CBR and RBR, finally, electric automobile remote intelligent fault diagnosis is illustrated with the expert systems.

Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic
Keyword(s):  
Temporal Reasoning ◽  
Higher Order ◽  
Important Case ◽  
Human Cognition ◽  
Relational Reasoning ◽  
Dual Systems ◽  
First Order ◽  
Reasoning System ◽  
Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

DECISION-MAKING ON INVESTMENT OF IT-INNOVATION BASED ON FUZZY EXPERT INFORMATION

2018 ◽  
pp. 103-111
Author(s):  
Olga Olegovna Eremenko ◽  
Lyubov Borisovna Aminul ◽  
Elena Vitalievna Chertina
Keyword(s):  
Decision Making ◽  
Individual Characteristics ◽  
Production Model ◽  
Investment Management ◽  
It Projects ◽  
Product Model ◽  
Project Cost ◽  
Reasoning System ◽  
Managerial Decisions ◽  
The Individual

The subject of the research is the process of making managerial decisions for innovative IT projects investing. The paper focuses on the new approach to decision making on investing innovative IT projects using expert survey in a fuzzy reasoning system. As input information, expert estimates of projects have been aggregated into six indicators having a linguistic description of the individual characteristics of the project type "high", "medium", and "low". The task of decision making investing has been formalized and the term-set of the output variable Des has been defined: to invest 50-75% of the project cost; to invest 20-50% of the project cost; to invest 10-20% of the project cost; to send the project for revision; to turn down investing project. The fuzzy product model of making investment management decisions has been developed; it adequately describes the process of investment management. The expediency of using constructed production model on a practical example is shown.

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