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17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)最新文献

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Good learning and implicit model enumeration 良好的学习和隐式模型枚举
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.69
A. Morgado, Joao Marques-Silva
{"title":"Good learning and implicit model enumeration","authors":"A. Morgado, Joao Marques-Silva","doi":"10.1109/ICTAI.2005.69","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.69","url":null,"abstract":"A large number of practical applications rely on effective algorithms for propositional model enumeration and counting. Examples include knowledge compilation, model checking and hybrid solvers. Besides practical applications, the problem of counting propositional models is of key relevancy in computational complexity. In recent years a number of algorithms have been proposed for propositional model enumeration. This paper surveys algorithms for model enumeration, and proposes optimizations to existing algorithms, namely through the learning and simplification of goods. Moreover, the paper also addresses open topics in model counting related with good learning. Experimental results indicate that the proposed techniques are effective for model enumeration","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching 基于软切换的机器人多模型自适应神经网络滑模控制
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.25
N. Sadati, R. Ghadami, M. Bagherpour
{"title":"Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching","authors":"N. Sadati, R. Ghadami, M. Bagherpour","doi":"10.1109/ICTAI.2005.25","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.25","url":null,"abstract":"In this paper, an adaptive neural network multiple models sliding mode controller for robotic manipulators is presented. The proposed approach remedies the previous problems met in practical implementation of classical sliding mode controllers. Adaptive single-input single-output (SISO) RBF neural networks are used to calculate each element of the control gain vector; discontinuous part of control signal, in a classical sliding mode controller. By using the multiple models technique the nominal part of the control signal is constructed according to the most appropriate model at different environments. The key feature of this scheme is that prior knowledge of the system uncertainties is not required to guarantee the stability. Also the chattering phenomenon is completely eliminated. Moreover, a theoretical proof of the stability and convergence of the proposed scheme using Lyapunov method is presented. To demonstrate the effectiveness of the proposed approach, a practical situation in robot control is simulated","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
An ANFIS based method of agent behavior in simulated soccer agents 基于ANFIS的足球模拟agent行为分析方法
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.32
R. Zafarani, M. Yazdchi, S. Salehi
{"title":"An ANFIS based method of agent behavior in simulated soccer agents","authors":"R. Zafarani, M. Yazdchi, S. Salehi","doi":"10.1109/ICTAI.2005.32","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.32","url":null,"abstract":"Multi-agent systems has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. One of the most important aspects of agent design in AI is the way agent acts or responds to the environment that the agent is acting upon. An effective action selection and behavioral method gives a powerful advantage in overall agent performance. We define a new method of action selection based on probability/priority models in RoboCup Soccer Simulation League(H. Kitano, 1997) and for simulated soccer agents, we thereby introduce an efficient way to determine probabilities and a new priority based system which maps the human knowledge to action selection method. Furthermore, a behavior model is introduced to make the model more flexible","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Ultimately periodic qualitative constraint networks for spatial and temporal reasoning 最终，空间和时间推理的周期性定性约束网络
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.124
Jean-François Condotta, G. Ligozat, S. Tripakis
{"title":"Ultimately periodic qualitative constraint networks for spatial and temporal reasoning","authors":"Jean-François Condotta, G. Ligozat, S. Tripakis","doi":"10.1109/ICTAI.2005.124","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.124","url":null,"abstract":"We consider qualitative temporal or spatial constraint networks whose constraints evolve over time in an ultimately periodic fashion: after an initial stretch of time, a fixed pattern of constraints (over an interval) is reproduced indefinitely. We propose a local propagation algorithm which is polynomial, and we show that it decides the consistency problem in some particular cases. We also show that the general problem of consistency for such networks is in PSPACE","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128276466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Development of system for crossarm reuse judgment on the basis of classification of rust images using support vector machine 基于支持向量机对铁锈图像分类的横臂复用判断系统的开发
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.58
Michiko Yamana, H. Murata, T. Onoda, Tohru Ohashi, Seiji Kato
{"title":"Development of system for crossarm reuse judgment on the basis of classification of rust images using support vector machine","authors":"Michiko Yamana, H. Murata, T. Onoda, Tohru Ohashi, Seiji Kato","doi":"10.1109/ICTAI.2005.58","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.58","url":null,"abstract":"We attempt to develop a crossarm reuse judgment system based on rust images that uses machine learning techniques. The system consists of a digital camera and a standard note book personal computer (PC). We estimate the degree of accuracy of the judgment of various pattern classification methods without special image processing techniques such as the extraction of features. The results show that a support vector machine is the most suitable instrument for this judgment system. We obtain the high degree of accuracy by compressing the image data in order to decrease the number of features","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
A robust approach to sequence classification 一种鲁棒序列分类方法
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.16
Ming Li, R. Sleep
{"title":"A robust approach to sequence classification","authors":"Ming Li, R. Sleep","doi":"10.1109/ICTAI.2005.16","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.16","url":null,"abstract":"We report results for classification of representations of music, spoken words, and text documents. Experimental comparisons with other state-of-the-art algorithms yield improved results for all three examples. We use a support vector machine (SVM) as our classifier in all experiments. This is driven by a kernel matrix of similarity measures between the sequences. Our similarity measure is based on n-grams of varying length (multi-grams), weighted to reflect discrimination ability. To alleviate the problem of the exponential growth of feature size with n, we use a modified LZ78 algorithm (Z. Jacob and L. Abraham, 1978) to guide feature selection. Our method exhibits good performance over the three widely distinct tasks reported here, and is very computationally efficient and may therefore be useful in real time applications","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
Prediction of the Stock Exchange of Thailand using adaptive evolution strategies 运用自适应进化策略预测泰国证券交易所
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.99
S. Rimcharoen, D. Sutivong, P. Chongstitvatana
{"title":"Prediction of the Stock Exchange of Thailand using adaptive evolution strategies","authors":"S. Rimcharoen, D. Sutivong, P. Chongstitvatana","doi":"10.1109/ICTAI.2005.99","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.99","url":null,"abstract":"In this paper we present a prediction process of the Stock Exchange of Thailand index using adaptive evolution strategies. The prediction process does not require the knowledge of the functional form a priori. In each recursion step, genetic algorithm is used to evolve the structure of the prediction function, whereas the coefficient is evolved by evolution strategies. The proposed method has been shown to successfully predict the Stock Exchange of Thailand and returns an error less than 3%. This methodology is also a tool for knowledge discovery in a specific application. We have found that the SET index can be reasonably forecasted with only two factors: the Hang Seng index and minimum loan rate. The proposed method also achieves a lower prediction error when compared with multiple regression method","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123096140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 39
Performance evaluation of hybrid genetic algorithm for assembly line scheduling 混合遗传算法在装配线调度中的性能评价
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.94
Song Hui
{"title":"Performance evaluation of hybrid genetic algorithm for assembly line scheduling","authors":"Song Hui","doi":"10.1109/ICTAI.2005.94","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.94","url":null,"abstract":"In this paper, we present a new approach to tackle scheduling problems in manufacturers' assembly line. Former solutions also provide results, yet they turn out to be ineffective or time-consuming. Our approach involves several new schemes in crossover and mutation, which reduce its processing time. In order to avoid premature convergences of the chromosomes, we choose a self-adaptive mutation rate and a clone-replacement approach. We then try an alternative called derivative tree crossover. Finally, the paper examines this algorithm's efficiency, which outperforms the previous methods","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126466884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Image segmentation using spectral clustering 基于光谱聚类的图像分割
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.74
Wang Chongjun, Liao Jun, Ding Lin, Tian Juan, C. Shifu
{"title":"Image segmentation using spectral clustering","authors":"Wang Chongjun, Liao Jun, Ding Lin, Tian Juan, C. Shifu","doi":"10.1109/ICTAI.2005.74","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.74","url":null,"abstract":"This paper focuses on how to automatically determine the suitable clustering number in image segmentation and designs an algorithm of CANA using spectral clustering. Experiment results indicate that ACNA can provide superior performance. An application sample in image punching is introduced","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124906715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Sub-ontology evolution for service composition with application to distributed e-learning 面向服务组合的子本体演化及其在分布式电子学习中的应用
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI: 10.1109/ICTAI.2005.117
Yuxin Mao, W. K. Cheung, Zhaohui Wu, Jiming Liu
{"title":"Sub-ontology evolution for service composition with application to distributed e-learning","authors":"Yuxin Mao, W. K. Cheung, Zhaohui Wu, Jiming Liu","doi":"10.1109/ICTAI.2005.117","DOIUrl":"https://doi.org/10.1109/ICTAI.2005.117","url":null,"abstract":"In order to meet the on-demand requirement of service composition at a large scale, one should go beyond the use of static domain ontologies but allow different focused aspects of the ontologies to be distributed as sub-ontologies. To demonstrate the feasibility of the sub-ontology idea, we suggest a possible implementation using the semantic Web technology and apply it to distributed e-learning","PeriodicalId":294694,"journal":{"name":"17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128010220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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