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2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)最新文献

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Belief Update in Bayesian Networks Using Uncertain Evidence 基于不确定证据的贝叶斯网络信念更新
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.39
Rong Pan, Yun Peng, Zhongli Ding
{"title":"Belief Update in Bayesian Networks Using Uncertain Evidence","authors":"Rong Pan, Yun Peng, Zhongli Ding","doi":"10.1109/ICTAI.2006.39","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.39","url":null,"abstract":"This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey's rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This in-depth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130256609","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: 52
Heuristic Policy Analysis and Efficiency Assessment in Constraint Satisfaction Search 约束满足搜索的启发式策略分析与效率评价
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.62
R. Wallace
{"title":"Heuristic Policy Analysis and Efficiency Assessment in Constraint Satisfaction Search","authors":"R. Wallace","doi":"10.1109/ICTAI.2006.62","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.62","url":null,"abstract":"This paper argues that characterizing heuristic performance in terms of adherence to optimal policies can elucidate many differences in search effort associated with different variable ordering heuristics. This framework involves two policies that search must adhere to to be successful: the promise policy is in force when search is on a solution path; the fail-first policy holds when search is in an insoluble subtree. After discussing how adherence to these policies can be measured, the paper shows that many complex patterns of performance can be elucidated by measuring the adherence to each policy. For example, some strategies designed to maximize adherence to one policy are shown to be either unsuccessful in this regard or to affect adherence to the other policy adversely, thus impairing search. In contrast, strategies used by superior heuristics often balance features such as branching and local connectivity so as to enhance adherence to both policies simultaneously. Differences related to problem structure can also be elucidated","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126601046","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
Interval Data Clustering with Applications 应用程序的区间数据聚类
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.71
Wei Peng, Tao Li
{"title":"Interval Data Clustering with Applications","authors":"Wei Peng, Tao Li","doi":"10.1109/ICTAI.2006.71","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.71","url":null,"abstract":"Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"18 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116735167","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: 18
Constructing a Simple Visually-Guided Robotic Part-Grasping System with Off-the-Shelf Components 构建一种简单的视觉引导机器人零件抓取系统
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.48
S. V. Delden
{"title":"Constructing a Simple Visually-Guided Robotic Part-Grasping System with Off-the-Shelf Components","authors":"S. V. Delden","doi":"10.1109/ICTAI.2006.48","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.48","url":null,"abstract":"A novel approach to grasping and recognizing parts in an industrial robotic work cell is presented in this paper. The centroid, orientation, and length of elongated parts lying on a flat work area are estimated by a sequence of simple algorithms. Off-the-shelf components and freely downloadable software APIs make this system inexpensive and easily implemented. The approach has been implemented and tested with a Staubli RX60 manipulator. Results and future research are presented","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981494","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
iAIML: a Mechanism to Treat Intentionality in AIML Chatterbots 一种处理AIML聊天机器人意向性的机制
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.64
André M. M. Neves, Flávia De Almeida Barros, Resumo, Ver Simon Laven
{"title":"iAIML: a Mechanism to Treat Intentionality in AIML Chatterbots","authors":"André M. M. Neves, Flávia De Almeida Barros, Resumo, Ver Simon Laven","doi":"10.1109/ICTAI.2006.64","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.64","url":null,"abstract":"This paper presents iAIML, a mechanism to treat intentional information based on AIML, a state-of-the-art technology in chatterbot development. Our main goal was to improve dialogues with AIML chatterbots. iAIML adds structure to AIML bases, incorporating intentions and rules used in sentence interpretation and generation. We adopted as linguistic base the conversational analysis theory (CAT), which considers intentionality in adjacent pairs in dialogue, facilitating the establishment of consistent dialogues between chatterbots and users. Tests with the implemented solution showed feasibility of the proposed approach. This is an original work with several contributions, such as the innovative and effective use of CAT, and a consistent modular structure of the iAIML base, favoring reuse and maintenance","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134422602","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: 37
An Algorithm for a Constraint Optimization Problem in Mobile Ad-hoc Networks 移动自组网中约束优化问题的一种算法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.29
A. Idrissi, Chu Min Li, J. Myoupo
{"title":"An Algorithm for a Constraint Optimization Problem in Mobile Ad-hoc Networks","authors":"A. Idrissi, Chu Min Li, J. Myoupo","doi":"10.1109/ICTAI.2006.29","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.29","url":null,"abstract":"A mobile ad-hoc network is considered as a dynamic autonomous system composed of mobile devices interconnected by links without wire, without the use of a fixed infrastructure and without centralized administration. The absence of a centralized infrastructure forces each device to work in a peer to peer distributed environment, and to act as a router to relay communications, or to generate its own data. The management of the network thus is strongly distributed on all elements of the network. In this paper, we present a modelling of the mobile ad-hoc network (MANET) problem in form of a constraint satisfaction/optimization problem called CSPADhoc. Then, to minimize the consumption of batteries for devices, we describe an approach based on an adaptation of the A star algorithm to the MANET problem called (MANET-Astar). Finally, we present some experimental results using our approach","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115661091","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: 17
Incremental Filtering Algorithms for Precedence and Dependency Constraints 优先和依赖约束的增量过滤算法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1142/S0218213008003856
R. Barták, O. Cepek
{"title":"Incremental Filtering Algorithms for Precedence and Dependency Constraints","authors":"R. Barták, O. Cepek","doi":"10.1142/S0218213008003856","DOIUrl":"https://doi.org/10.1142/S0218213008003856","url":null,"abstract":"Precedence constraints play a crucial role in planning and scheduling problems. Many real-life problems also include dependency constraints expressing logical relations between the activities - for example, an activity requires presence of another activity in the plan. For such problems a typical objective is a maximization of the number of activities satisfying the precedence and dependency constraints. In the paper we propose new incremental filtering rules integrating propagation through both precedence and dependency constraints. We also propose a new filtering rule using the information about the requested number of activities in the plan. We demonstrate efficiency of the proposed rules on the log-based reconciliation problems and min-cutset problems","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658398","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: 9
Modeling Workflow Systems with Genetic Planner and Scheduler 用遗传规划器和调度器建模工作流系统
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.86
Fabiano S. R. Alves, K. F. Guimaraes, M. A. Fernandes
{"title":"Modeling Workflow Systems with Genetic Planner and Scheduler","authors":"Fabiano S. R. Alves, K. F. Guimaraes, M. A. Fernandes","doi":"10.1109/ICTAI.2006.86","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.86","url":null,"abstract":"Workflow systems have been widely employed by organizations in general; however, the generation of process models is still an area to be explored. Some works are concentrated in the use of planning techniques to solve problems; however, one of the difficulties in applying such techniques in workflow problems is the size of the search space required for the real world problems. An alternative is the use of evolutionary computing techniques, particularly genetic algorithms that in general are more suitable for these problems. In this context, we will present an architecture based on the use of a genetic planner in order to allow the automatic generation of process modeling. A simulation environment is also proposed by using scheduling techniques based on the use of genetic algorithms to identify the most suitable process model","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114817107","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: 9
Using Boosting Techniques to Improve Software Reliability Models Based on Genetic Programming 基于遗传规划的增强技术改进软件可靠性模型
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.117
E. O. Costa, A. Pozo, S. Vergilio
{"title":"Using Boosting Techniques to Improve Software Reliability Models Based on Genetic Programming","authors":"E. O. Costa, A. Pozo, S. Vergilio","doi":"10.1109/ICTAI.2006.117","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.117","url":null,"abstract":"Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored genetic programming (GP) to derive reliability models. However, nowadays boosting techniques (BT) have been successfully applied with other machine learning techniques, including GP. BT merge several hypotheses of the training set to get better results. With the goal of improving the GP software reliability models, this work explores the combination GP and BT. The results show advantages in the use of the proposed approach","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122980087","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: 18
MI-Winnow: A New Multiple-Instance Learning Algorithm MI-Winnow:一种新的多实例学习算法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.82
Sharath R. Cholleti, S. Goldman, R. Rahmani
{"title":"MI-Winnow: A New Multiple-Instance Learning Algorithm","authors":"Sharath R. Cholleti, S. Goldman, R. Rahmani","doi":"10.1109/ICTAI.2006.82","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.82","url":null,"abstract":"We present Mi-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a collection (or bag) of d-dimensional points where each dimension corresponds to a feature. A label is provided for the bag, but not for the individual points within the bag. Mi-Winnow is different from existing multiple-instance learning algorithms in several key ways. First, Mi-Winnow allows each image to be converted into a bag in multiple ways to create training (and test) data that varies in both the number of dimensions per point, and in the kind of features used. Second, instead of learning a concept defined by a single point-and-scaling hypothesis, Mi-Winnow allows the underlying concept to be described by combining a set of separators learned by Winnow. For content-based image retrieval applications, such a generalized hypothesis is important since there may be different ways to recognize which images are of interest","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904156","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: 6
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