Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning

2013 ◽  
Vol 17 (2) ◽  
pp. 291-301 ◽  
Author(s):  
Lue-Feng Chen ◽  
◽  
Zhen-Tao Liu ◽  
Fang-Yan Dong ◽  
Yoichi Yamazaki ◽  
...  
Keyword(s):  
Production Rule ◽  
Independent Learning ◽  
Robot System ◽  
Adaptation Mechanism ◽  
Virtual Communication ◽  
Rule Based ◽  
Q Learning ◽  
The Social ◽  
Atmosphere Environment ◽  
Fuzzy Production Rule

A behavior adaptation mechanism in humans-robots interaction is proposed to adjust robots’ behavior to communication atmosphere, where fuzzy production rule based friend-Q learning (FPRFQ) is introduced. It aims to shorten the response time of robots and decrease the social distance between humans and robots to realize the smooth communication of robots and humans. Experiments on robots/humans interaction are performed in a virtual communication atmosphere environment. Results show that robots adapt well by saving 44 and 482 learning steps compared to that by friend-Q learning (FQ) and independent learning (IL), respectively; additionally, the distance between human-generated atmosphere and robot-generated atmosphere is 3 times and 10 times shorter than the FQ and the IL, respectively. The proposed behavior adaptation mechanism is also applied to robots’ eye movement in the developing humans-robots interaction system, calledmascot robot system, and basic experimental results are shown in home party scenario with five eye robots and four humans.

A Reasoning Method of Transformer Fault Causes Based on Fuzzy Petri Net

2014 ◽  
Vol 631-632 ◽  
pp. 537-542
Author(s):  
Wei Hua Zhang ◽  
Jin Sha Yuan ◽  
Ke Zhang ◽  
Zhong Li
Keyword(s):  
Petri Net ◽  
Production Rule ◽  
Correct Judgment ◽  
Rule Based ◽  
Automatic Reasoning ◽  
Fuzzy Petri Net ◽  
Matrix Iteration ◽  
The Rich ◽  
Transformer Fault

For a correct judgment on the fault cause of transformers with the rich knowledge in various criteria and guideline, a method of automatic reasoning for the fault cause through fuzzy Petri net has been put forward in this paper. In this method, the knowledge in criteria and guidelines is firstly presented in the form of IF-THEN structure through the production rule, based on which the fuzzy Petri net model of fault cause is established then; and lastly possibility of each fault cause can be worked out through matrix iteration, which means the automatic reasoning is completed. Based on fuzzy Petri net imaging, this method makes the reasoning clearer and the result be got faster. The example calculation verifies that the method is correct and feasible in practical projects.

A fuzzy production rule based expert system

1991 ◽  
Vol 44 (3) ◽  
pp. 391-403 ◽  
Author(s):  
Zhang Yue ◽  
Liang Fengchi ◽  
Su Fen ◽  
Bao Shounan ◽  
Peng Yunxiang
Keyword(s):  
Expert System ◽  
Production Rule ◽  
Rule Based ◽  
Fuzzy Production Rule

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

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