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2008 Seventh Mexican International Conference on Artificial Intelligence最新文献

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Performance of Inductive Method of Model Self-Organization with Incomplete Model and Noisy Data 不完全模型和噪声数据下模型自组织的归纳方法性能
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.72
N. Ponomareva, M. Alexandrov, Alexander Gelbukh
{"title":"Performance of Inductive Method of Model Self-Organization with Incomplete Model and Noisy Data","authors":"N. Ponomareva, M. Alexandrov, Alexander Gelbukh","doi":"10.1109/MICAI.2008.72","DOIUrl":"https://doi.org/10.1109/MICAI.2008.72","url":null,"abstract":"Inductive method of model self-organization (IMMSO) developed in 80s by A. Ivakhnenko is an evolutionary machine learning algorithm, which allows selecting a model of optimal complexity that describes or explains a limited number of observation data when any a priori information is absent or is highly insufficient. In this paper, we study the performance of IMMSO to reveal a model in a given class with different volumes of data, contributions of unaccounted components, and levels of noise. As a simple case study, we consider artificial observation data: the sum of a quadratic parabola and cosine; model class under consideration is a polynomial series. The results are interpreted in the terms of signal-noise ratio.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124698097","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
Measuring Lexical Cohesion in a Document 测量文档中的词汇衔接
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.69
Kamakhya Gupta, Mohamed Sadiq
{"title":"Measuring Lexical Cohesion in a Document","authors":"Kamakhya Gupta, Mohamed Sadiq","doi":"10.1109/MICAI.2008.69","DOIUrl":"https://doi.org/10.1109/MICAI.2008.69","url":null,"abstract":"Lexical Cohesion is one of the important features in text processing for analyzing document structure and improving the accuracy of text processing. We present a hierarchial graph based model to measure cohesion by grouping lexically cohesive units together in a text. Latent semantic analysis is used to construct a relational graph to uncover the hidden semantics from the text by discovering new relations and their weights. Coherence score is derived from the learned weights that are assigned to edges in the graph and number of disjoint partitions in the graph.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125047154","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
An Improvement to Flock Traffic Navigation Algorithm Using Linear Regression Techniques 基于线性回归技术的群体交通导航算法改进
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.39
L. Martinez-Elizalde, C. Astengo-Noguez
{"title":"An Improvement to Flock Traffic Navigation Algorithm Using Linear Regression Techniques","authors":"L. Martinez-Elizalde, C. Astengo-Noguez","doi":"10.1109/MICAI.2008.39","DOIUrl":"https://doi.org/10.1109/MICAI.2008.39","url":null,"abstract":"Flock traffic navigation (FTN) is a recently approach to solve vehicular traffic congestions in cities using multiagent technology. These agents are instructed to cooperate with others in order to form flocks, which gain a speed bonus, therefore improving individual travel time. Identifying partners is a critical issue, since it is a time consuming process. In this paper we present an improvement to the negotiation algorithm using linear regression techniques in order to decide which subset of agents could be the best possible partners. Finally, we also present early experimental data that support these ideas.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125107122","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
Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem 基于信念的多智能体ERA模型求解多港口集装箱积载问题
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.10
Liu Yanbin, Wang Kangping, Guo Dongwei, Pang Wei, Zhou Chunguang
{"title":"Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem","authors":"Liu Yanbin, Wang Kangping, Guo Dongwei, Pang Wei, Zhou Chunguang","doi":"10.1109/MICAI.2008.10","DOIUrl":"https://doi.org/10.1109/MICAI.2008.10","url":null,"abstract":"ERA is the acronym of environment, reactive rules and agent; the model is a new effective multi-agent cooperation framework, which has been successfully applied in a wide range of areas. Multi-port Container Stowage (MPCS) is a more practical problem. However, multi-port makes the container stowage problem more difficult. This paper presents a MPCS model based on time series and a multi-agent ERA model based on belief to solve the multi-port container stowage problem. The algorithm introduces the belief concept into agents and creates a multi-agent ERA model. The searching direction and strength of the agents are determined by the learning machine using belief parameter. Agent has the ability to evaluate its searching path. At last, the feasibility and efficiency of the model are validated by the experimental result.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130233156","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
Compression Strength Prediction of Mixtures Concrete with Scrap Tire with Neural Network Approach 用神经网络方法预测废轮胎混合混凝土抗压强度
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.43
J. Acevedo-Dávila, L.M. Torres-Trevio
{"title":"Compression Strength Prediction of Mixtures Concrete with Scrap Tire with Neural Network Approach","authors":"J. Acevedo-Dávila, L.M. Torres-Trevio","doi":"10.1109/MICAI.2008.43","DOIUrl":"https://doi.org/10.1109/MICAI.2008.43","url":null,"abstract":"The compressive strength of mixtures made with scrap tire id presented. In this study, neural network modeling was applied for predicting compressive strength of mixtures containing variable size of tire scrap. This modeling allows avoiding a large number of trial mixtures tests and provides a new alternative for designing new constructive components at lower costs. Results shown that neural model showed an excellent performance and accurate and highly reproducible predictions.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129536497","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
Analysis of Time Series with Artificial Neural Networks 用人工神经网络分析时间序列
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.55
R. A. González-Grimaldo, J. Cuevas-Tello
{"title":"Analysis of Time Series with Artificial Neural Networks","authors":"R. A. González-Grimaldo, J. Cuevas-Tello","doi":"10.1109/MICAI.2008.55","DOIUrl":"https://doi.org/10.1109/MICAI.2008.55","url":null,"abstract":"This paper presents the study of time series in gravitational lensing to solve the time delay problem in astrophysics. The time series are irregularly sampled and noisy. There are several methods to estimate the time delay between this kind of time series, and this paper proposes a new method based on artificial neural networks, in particular, General Regression Neural Networks (GRNN), which is based on Radial Basis Function (RBF) networks. We also compare other typical artificial neural network architectures, where the learning time of GRNN is better. We analyze artificial data used in the literature to compare the performance of the new method against state-of-the-art methods. Some statistics are presented to study the significance of results.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121116519","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: 5
Use of Intelligent Emotional Agents in the Animation of Autonomous Virtual Creatures 智能情感代理在自主虚拟生物动画中的应用
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.15
H.R.O. Aguirre, F. Corchado, L.A.R. Ruvalcaba, J.A.Z. Rios, D. Thalmann
{"title":"Use of Intelligent Emotional Agents in the Animation of Autonomous Virtual Creatures","authors":"H.R.O. Aguirre, F. Corchado, L.A.R. Ruvalcaba, J.A.Z. Rios, D. Thalmann","doi":"10.1109/MICAI.2008.15","DOIUrl":"https://doi.org/10.1109/MICAI.2008.15","url":null,"abstract":"Recently several psychologists have begun the exploration and study of emotions to explain better their functioning. The obtained results give evidence that emotions have an important impact on thinking, judgment, reasoning, memory and decision making of human being. In this work we describe a new faculty called artificial emotional intelligence (AEI), and we propose a model based on emotional intelligence (EI) to develop a new approach to the problem of mood and emotion control in the animation of avatars or virtual creatures (VC). We use intelligent emotional agents to control the behavior of the avatars in 3D virtual environments. Our proposal about AEI relates cognitive processes of avatar involving emotional information. These processes include mainly: appraising and expressing emotions and regulating emotion in the self and others, and using emotions in adaptive ways.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121285871","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
Using Linguistic Information to Classify Portuguese Text Documents 利用语言信息对葡萄牙语文本文档进行分类
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.17
Teresa Gonçalves, P. Quaresma
{"title":"Using Linguistic Information to Classify Portuguese Text Documents","authors":"Teresa Gonçalves, P. Quaresma","doi":"10.1109/MICAI.2008.17","DOIUrl":"https://doi.org/10.1109/MICAI.2008.17","url":null,"abstract":"This paper examines the role of various linguistic structures on text classification applying the study to the Portuguese language. Besides using a bag-of-words representation where we evaluate different measures and use linguistic knowledge for term selection, we do several experiments using syntactic information representing documents as strings of words and strings of syntactic parse trees. To build the classifier we use the support vector machine (SVM) algorithm which is known to produce good results on text classification tasks and apply the study to a dataset of articles from the Publico newspaper. The results show that sentences' syntactic structure is not useful for text classification (as initially expected), but part-of-speech information can be used as a term selection technique to construct the bag-of-words representation of documents.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114275178","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: 3
Fault Prediction Using Artificial Neural Network and Fuzzy Logic 基于人工神经网络和模糊逻辑的故障预测
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.38
S.M. Virk, A. Muhammad, A. MARTÍNEZ-ENRIQUEZ
{"title":"Fault Prediction Using Artificial Neural Network and Fuzzy Logic","authors":"S.M. Virk, A. Muhammad, A. MARTÍNEZ-ENRIQUEZ","doi":"10.1109/MICAI.2008.38","DOIUrl":"https://doi.org/10.1109/MICAI.2008.38","url":null,"abstract":"This paper studies different vehicle fault prediction techniques, using artificial neural network and fuzzy logic based model. With increasing demands for efficiency and product quality as well as progressing integration of automatic control systems in high-cost mechatronics and safety-critical processes, monitoring is necessary to detect and diagnose faults using symptoms and related data. However, beyond protective maintenance services, it is viable to integrate fault prediction services. Thus, we studied different parameters to model a fault prediction service. This service not only helps to predict faults but is also useful to take precautionary measures to avoid tangible and intangible losses.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115411144","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: 20
Multi-leak Diagnosis in Pipelines A Comparison of Approaches 管道多泄漏诊断方法的比较
2008 Seventh Mexican International Conference on Artificial Intelligence Pub Date : 2008-10-27 DOI: 10.1109/MICAI.2008.33
C. Verde, R. Morales-Menéndez, L. Garza, A. Vargas, P. Velasquez-Roug, C. Rea, C.T. Aparicio, J.O. De la Fuente
{"title":"Multi-leak Diagnosis in Pipelines A Comparison of Approaches","authors":"C. Verde, R. Morales-Menéndez, L. Garza, A. Vargas, P. Velasquez-Roug, C. Rea, C.T. Aparicio, J.O. De la Fuente","doi":"10.1109/MICAI.2008.33","DOIUrl":"https://doi.org/10.1109/MICAI.2008.33","url":null,"abstract":"Leaks on pipelines can cause strong economic losses and environmental problems if these are not detected on time. The problem of detecting leaks is even more complicated when the pipelines are too large, difficult to reach by maintenance personnel, and equipped with minimum instrumentation. A comparison of four fault diagnosis approaches based on Output Observers, Artificial Neural Networks, Particle Filtering and Principal Components Analysis are presented. Simulated results of multi-leaks in pipelines showed that Particle Filtering techniques outperform the other approaches. However, a combined solution is proposed.","PeriodicalId":301028,"journal":{"name":"2008 Seventh Mexican International Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132081681","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
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