14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.最新文献_第3页

Machine learning for software engineering: case studies in software reuse 软件工程中的机器学习:软件重用中的案例研究

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180811

Justin S. Di Stefano, T. Menzies

引用次数: 22

An agent-based approach to inference prevention in distributed database systems 分布式数据库系统中基于agent的推理预防方法

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180833

J. Tracy, LiWu Chang, I. S. Moskowitz

引用次数: 4

Context query in information retrieval 信息检索中的上下文查询

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180793

Chi-Hung Chi, Chen Ding, Kwok-Yan Lam

引用次数: 4

A trace-scaling agent for parallel application tracing 用于并行应用程序跟踪的跟踪缩放代理

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180844

Felix Freitag, Jordi Caubet, Jesús Labarta

引用次数: 12

Selection process of intelligent decision support tool for real-time monitoring system 实时监控系统智能决策支持工具的选择过程

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180802

H. Vafaie, M. Shaughnessy, T. Bethem, J. Burton

引用次数: 2

TimeSleuth: a tool for discovering causal and temporal rules TimeSleuth:一个发现因果和时间规则的工具

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180827

K. Karimi, Howard J. Hamilton

{"title":"TimeSleuth: a tool for discovering causal and temporal rules","authors":"K. Karimi, Howard J. Hamilton","doi":"10.1109/TAI.2002.1180827","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180827","url":null,"abstract":"Discovering causal and temporal relations in a system is essential to understanding how it works, and to learning to control the behaviour of the system. TimeSleuth is a causality miner that uses association relations as the basis for the discovery of causal and temporal relations. It does so by introducing time into the observed data. TimeSleuth uses C4.5 as its association discoverer, and by using a series of preprocessing and post-processing techniques to enable the user to try different scenarios for mining causality. The data to be mined should originate sequentially from a single system. TimeSleuth's use of a standard decision tree builder such as C4.5 puts it outside the current mainstream method of discovering causality, which is based on conditional independencies and causal Bayesian networks. This paper introduces TimeSleuth as a tool, and describes its functionality. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the attributes. There is also a mechanism to distinguish between causality and acausal relations. The user is thus encouraged to perform experiments and discover the nature of relationships among the data.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842541","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: 22

Object-based representation and classification of spatial structures and relations 基于对象的空间结构和关系表示与分类

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180814

F. Ber, A. Napoli

引用次数: 4

Diagnosis of component failures in the Space Shuttle main engines using Bayesian belief networks: a feasibility study 用贝叶斯信念网络诊断航天飞机主发动机部件故障的可行性研究

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180803

E. Liu, Du Zhang

引用次数: 20

Adjusted network flow for the shelf-space allocation problem 针对货架空间分配问题调整了网络流量

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180808

A. Lim, B. Rodrigues, Fei Xiao, Xingwen Zhang

引用次数: 9

Calculus of variations in discrete space for constrained nonlinear dynamic optimization 约束非线性动态优化的离散空间变分法

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. Pub Date : 2002-11-04 DOI: 10.1109/TAI.2002.1180789

Yixin Chen, B. Wah

{"title":"Calculus of variations in discrete space for constrained nonlinear dynamic optimization","authors":"Yixin Chen, B. Wah","doi":"10.1109/TAI.2002.1180789","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180789","url":null,"abstract":"We propose new dominance relations that can speed up significantly the solution process of nonlinear constrained dynamic optimization problems in discrete time and space. We first show that path dominance in dynamic programming cannot be applied when there are general constraints that span across multiple stages, and that node dominance, in the form of Euler-Lagrange conditions developed in optimal control theory in continuous space, cannot be extended to that in discrete space. This paper is the first to propose efficient dominance relations, in the form of local saddle-point conditions in each stage of a problem, for pruning states that will not lead to locally optimal paths. By utilizing these dominance relations, we develop efficient search algorithms whose complexity, despite exponential, has a much smaller base as compared to that without using the relations. Finally, we demonstrate the performance of our algorithms on some spacecraft planning and scheduling benchmarks and show significant improvements in CPU time and solution quality as compared to those obtained by the existing ASPEN planner.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834044","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