Ontology selection ranking model for knowledge reuse

2011 ◽  
Vol 38 (5) ◽  
pp. 5133-5144 ◽  
Author(s):  
Jinsoo Park ◽  
Sunjoo Oh ◽  
Joongho Ahn
Keyword(s):  
Knowledge Reuse ◽  
Ranking Model ◽  
Ontology Selection

Study on a Knowledge-Reuse-Based Rapid Product Design Process

2008 ◽  
Vol 392-394 ◽  
pp. 543-550 ◽  
Author(s):  
Hun Guo ◽  
Guo Xing Tang ◽  
Dun Wen Zuo ◽  
T.J. Liu ◽  
W.D. Jin
Keyword(s):  
Product Design ◽  
Design Process ◽  
Iterative Process ◽  
Process Model ◽  
Design Reuse ◽  
Knowledge Reuse ◽  
Construction Machinery ◽  
Current Design ◽  
Product Design Process ◽  
Three Factor

Design reuse is the application of past designs knowledge and successful experience to current design process and it is a significant method for rapid design. A knowledge-reuse-based rapid product design model is proposed and a three-factor product design iterative process model is studied. Finally, it is applied successfully in the rapid product design of construction machinery combining with the requirement of the construct machinery product design.

scholarly journals Collaborative Case-Based Reasoning for Knowledge Discovery of Elders Health Assessment System

2014 ◽  
Vol 8 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Ping Hu ◽  
Dong-xiao Gu ◽  
Yu Zhu
Keyword(s):  
Single Case ◽  
Health Assessment ◽  
Assessment System ◽  
Gray Relational Analysis ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Knowledge Reuse ◽  
Case Retrieval ◽  
Case Library ◽  
Case Representation

The existing Elders Health Assessment (EHA) system based on single-case-library reasoning has low intelligence level, poor coordination, and limited capabilities of assessment decision support. To effectively support knowledge reuse of EHA system, this paper proposes collaborative case reasoning and applies it to the whole knowledge reuse process of EHA system. It proposes a multi-case library reasoning application framework of EHA knowledge reuse system, and studies key techniques such as case representation, case retrieval algorithm, case optimization and correction, and reuse etc.. In the aspect of case representation, XML-based multi-case representation for case organization and storage is applied to facilitate case retrieval and management. In the aspect of retrieval method, Knowledge-Guided Approach with Nearest-Neighbor is proposed. Given the complexity of EHA, Gray Relational Analysis with weighted Euclidean Distance is used to measure the similarity so as to improve case retrieval accuracy.

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

Assessing user simulation for dialog systems using human judges and automatic evaluation measures

2011 ◽  
Vol 17 (4) ◽  
pp. 511-540 ◽  
Author(s):  
HUA AI ◽  
DIANE LITMAN
Keyword(s):  
Gold Standard ◽  
System Development ◽  
Automatic Evaluation ◽  
Dialog Systems ◽  
Evaluation Measures ◽  
Assessment Study ◽  
User Simulation ◽  
Dialog System ◽  
Ranking Model

AbstractWhile different user simulations are built to assist dialog system development, there is an increasing need to quickly assess the quality of the user simulations reliably. Previous studies have proposed several automatic evaluation measures for this purpose. However, the validity of these evaluation measures has not been fully proven. We present an assessment study in which human judgments are collected on user simulation qualities as the gold standard to validate automatic evaluation measures. We show that a ranking model can be built using the automatic measures to predict the rankings of the simulations in the same order as the human judgments. We further show that the ranking model can be improved by using a simple feature that utilizes time-series analysis.

Sign in / Sign up

Export Citation Format

Share Document