发布求助
文献互助 智能选刊 最新文献

2010 22nd IEEE International Conference on Tools with Artificial Intelligence最新文献

筛选
英文 中文
Integrating Time and Resources into Planning 将时间和资源整合到计划中
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.86
Filip Dvorak, R. Barták
{"title":"Integrating Time and Resources into Planning","authors":"Filip Dvorak, R. Barták","doi":"10.1109/ICTAI.2010.86","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.86","url":null,"abstract":"AI Planning typically deals with the causal relations between the actions while the role of explicit time and limited resources is suppressed. The recent trends show that integrating time and resource reasoning into planning significantly improves direct applicability of planning technology in real-life problems. In this paper we propose a suboptimal domain-independent planning system Filuta that focuses on planning, where explicit time plays a major role and resources are constrained. We benchmark Filuta on the planning problems from the International Planning Competition (IPC) 2008 and compare our results with the competition participants.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129493608","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 Parallel Solving Algorithm for Quantified Constraints Problems 一种量化约束问题的并行求解算法
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.46
Jérémie Vautard, Arnaud Lallouet, Y. Hamadi
{"title":"A Parallel Solving Algorithm for Quantified Constraints Problems","authors":"Jérémie Vautard, Arnaud Lallouet, Y. Hamadi","doi":"10.1109/ICTAI.2010.46","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.46","url":null,"abstract":"Quantified constraint satisfaction problems have been the topic of an increasing number of studies for a few years. However, only sequential resolution algorithms have been proposed so far. This paper presents a parallel QCSP+ solving algorithm based on a problem-partition approach. It then discuss about work distribution policies and presents several experimental results comparing several parameters.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404895","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
An Environment for the Joint Management of Written Policies and Business Rules 共同管理书面政策和业务规则的环境
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.95
F. Lévy, Abdoulaye Guissé, A. Nazarenko, Nouha Omrane, Sylvie Szulman
{"title":"An Environment for the Joint Management of Written Policies and Business Rules","authors":"F. Lévy, Abdoulaye Guissé, A. Nazarenko, Nouha Omrane, Sylvie Szulman","doi":"10.1109/ICTAI.2010.95","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.95","url":null,"abstract":"The contemporary world produces huge bodies of policies and regulations, while the underlying procedures tend to be automated in decision systems, which are designed to define, deploy, execute, monitor and maintain the various rules to which an organization or enterprise has to comply. It is important that the written documentation is integrated into such decision systems in order to refer to the texts to explain decisions, to update the systems when the policy evolves or, conversely, to amend the source documents if some of the rules happen to be inconsistent. The problem is that the complexity of information to be searched for is not reachable by an automated processing, but that their volume prohibits a manual one. Arguing that the integration of policies in decision systems can be better achieved through a semantic annotation than by the full parsing of the source documentation, this paper presents a technical environment that enables the building and exploitation of such semantic annotations.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128845908","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
Fault Localization in Constraint Programs 约束程序中的故障定位
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.18
Nadjib Lazaar, A. Gotlieb, Yahia Lebbah
{"title":"Fault Localization in Constraint Programs","authors":"Nadjib Lazaar, A. Gotlieb, Yahia Lebbah","doi":"10.1109/ICTAI.2010.18","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.18","url":null,"abstract":"Constraint programs such as those written in high level modeling languages (e.g., OPL , ZINC , or COMET ) must be thoroughly verified before being used in applications. Detecting and localizing faults is therefore of great importance to lower the cost of the development of these constraint programs. In a previous work, we introduced a testing framework called CPTEST enabling automated test case generation for detecting non-conformities. In this paper, we enhance this framework to introduce automatic fault localization in constraint programs. Our approach is based on constraint relaxation to identify the constraint that is responsible of a given fault. CPTEST is henceforth able to automatically localize faults in optimized OPL programs. We provide empirical evidence of the effectiveness of this approach on classical benchmark problems, namely Golomb rulers, n-queens, social golfer and car sequencing.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122766184","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
Multiobjective Approach for Feature Selection in Maximum Entropy Based Named Entity Recognition 基于最大熵的命名实体识别特征选择多目标方法
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.54
Asif Ekbal, S. Saha, Mohammed Hasanuzzaman
{"title":"Multiobjective Approach for Feature Selection in Maximum Entropy Based Named Entity Recognition","authors":"Asif Ekbal, S. Saha, Mohammed Hasanuzzaman","doi":"10.1109/ICTAI.2010.54","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.54","url":null,"abstract":"In this paper, we present the problem of appropriate feature selection for constructing a Maximum Entropy (ME) based Named Entity Recognition (NER) system under the multiobjective optimization (MOO) framework. Two conflicting objective functions are simultaneously optimized using the search capability of MOO. These objectives are (i). the dimensionality of features, which is tried to be minimized, and (ii). the corresponding F-measure value of the classifier, trained using the features present, is maximized. The features are encoded in the chromosomes. Thereafter, a multiobjective evolutionary algorithm in the steps of a popular MOO technique, NSGA-II, is developed to determine the appropriate feature subset. The proposed technique is evaluated to determine the suitable feature combinations for NER in a resource-constrained language, namely Bengali. Evaluation results yield the recall, precision and F-measure values of 72.45%, 82.39% and 77.11%, respectively.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132078858","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
TyCo: Towards Typicality-based Collaborative Filtering Recommendation 泰科:迈向基于典型性的协同过滤推荐
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.89
Y. Cai, Ho-fung Leung, Qing Li, Jie Tang, Juan-Zi Li
{"title":"TyCo: Towards Typicality-based Collaborative Filtering Recommendation","authors":"Y. Cai, Ho-fung Leung, Qing Li, Jie Tang, Juan-Zi Li","doi":"10.1109/ICTAI.2010.89","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.89","url":null,"abstract":"Collaborative filtering (CF) is an important and popular technology for recommendation systems. However, current collaborative filtering methods suffer from some problems such as sparsity problem, inaccurate recommendation and producing big-error predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds ‘neighbors’ of users based on user typicality degrees in user groups (instead of the co-rated items of users or common users of items in traditional CF). To the best of our knowledge, there is no work on investigating collaborative filtering recommendation by combining object typicality. We conduct experiments to validate TyCo and compare it with previous methods.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123606291","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
Particle Swarm Classification for High Dimensional Data Sets 高维数据集的粒子群分类
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.21
Nabila Nouaouria, M. Boukadoum
{"title":"Particle Swarm Classification for High Dimensional Data Sets","authors":"Nabila Nouaouria, M. Boukadoum","doi":"10.1109/ICTAI.2010.21","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.21","url":null,"abstract":"This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets. Our results are that, depending on the mechanisms controlling confinement and dispersion in the PSO algorithm, the classification accuracy varied with the dimensionality of the data and the cardinality of the output space.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124921968","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: 13
On the Role of Preferences in Argumentation Frameworks 论偏好在论证框架中的作用
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.38
Leila Amgoud, Srdjan Vesic
{"title":"On the Role of Preferences in Argumentation Frameworks","authors":"Leila Amgoud, Srdjan Vesic","doi":"10.1109/ICTAI.2010.38","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.38","url":null,"abstract":"The aim of this paper is to study how preferences, which are used to model intrinsic strengths of arguments, can be used in argumentation. We show that they play two roles: i) to repair the attack relation between arguments, and ii) to refine the evaluation of arguments. Then, we point out that the existing approaches for preference-based argumentation model only the first role. They may also return non conflict-free extensions. We propose a general framework that overcomes those limitations.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"49 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114057633","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
Creating Possible Worlds Using Sims Tables for the Imperfect Information Card Game Schnapsen 《不完全信息纸牌游戏Schnapsen》中使用模拟人生表格创造可能的世界
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.76
F. Wisser
{"title":"Creating Possible Worlds Using Sims Tables for the Imperfect Information Card Game Schnapsen","authors":"F. Wisser","doi":"10.1109/ICTAI.2010.76","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.76","url":null,"abstract":"We discuss a new algorithm for traversing possible worlds in games of imperfect information based on the enumerablity of permutations by means of Sims tables. The resulting anytime capabilities of the algorithm are demonstrated by applying it to the card game Schnapsen, popular in Central Europe.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121122044","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
Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization 基于最小裁剪二乘矩阵分解的鲁棒协同推荐
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.90
Zunping Cheng, N. Hurley
{"title":"Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization","authors":"Zunping Cheng, N. Hurley","doi":"10.1109/ICTAI.2010.90","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.90","url":null,"abstract":"Collaborative filtering (CF) recommender systems help people discover what they really need in a large set of alternatives by analyzing the preferences of other related users. Recent research has shown that the accuracy of recommendations can be improved significantly by using matrix factorization (MF) models. In particular, a mixed MF model was used by BellKor's Pragmatic Chaos to win the Netflix Prize. On the other hand, system designers must also be concerned about system robustness - the ability of the system to provide good recommendations when the system database is contaminated with some portion of noisy or erroneous data, perhaps maliciously entered by `profile injection' attackers intent on distorting system recommendations. In this paper, we focus on the robustness of MF based CF algorithms (MFCF), which usually transform the prediction of user preferences on items into a least squares problem, solved by gradient descent. As least squares is known to be sensitive to outliers, it is not surprising that MF algorithms are vulnerable to attack. Nevertheless a number of `robust statistics' have been proposed since the 1960's that provide alternative data fitting strategies that are less sensitive to outliers. In particular, in this paper, we propose a least trimmed squares based MF (LTSMF) to help improve the robustness of the least squares based MF (LSMF) models. Least trimmed squares is shown to be more robust than least squares and another popular robust method - M-estimator. Experiments also show that LTSMF outperforms previous robust CF models on both accuracy and robustness.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116081241","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: 43
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信