2012 IEEE 24th International Conference on Tools with Artificial Intelligence最新文献_第6页

A New Semantics for Logic Programs Capturing and Extending the Stable Model Semantics 逻辑程序捕获和扩展稳定模型语义的一种新语义

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.167

B. Benhamou, P. Siegel

{"title":"A New Semantics for Logic Programs Capturing and Extending the Stable Model Semantics","authors":"B. Benhamou, P. Siegel","doi":"10.1109/ICTAI.2012.167","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.167","url":null,"abstract":"Many research works had been done in order to define a semantics for logic programs. Most of these semantics are iterated fixed point semantics. The main idea is the canonical model approach which is a declarative semantics for logic programs that can be defined by selecting for each program one of its canonical models. The notion of canonical models of a logic program is what it is called the stable models. The stable models of a logic program are the minimal Her brand models of its \"reduct\" programs. The work that we describe in this paper is theoretical, we introduce a new semantics for logic programs that is different from the known fixed point semantics. In our approach, logic programs are expressed as CNF formulas (sets of clauses) of a propositional logic for which we define a notion of extension. We prove in this semantics, that each consistent CNF formula admits at least an extension and for each given stable model of a logic program there exists an extension of its corresponding CNF formula which logically entails it. On the other hand, we show that some of the extensions do not entail any stable model, in this case, we define a simple condition called a discrimination condition which allows to recognize such extensions. These extensions could be very important, but are not captured by the stable models semantics. Our approach, extends the stable model semantics in this sense. Following the new semantics, we give a full characterization of the stable models of a logic program by means of the extensions of its CNF encoding verifying the simple discrimination condition, and provide a procedure which can be used to compute such extensions from which we deduce the stable models and eventually the extra-stable models of the given logic program.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133400207","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

An Argumentation Framework for Reasoning about Bounded Resources 有限资源推理的论证框架

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.79

P. Besnard, É. Grégoire, Badran Raddaoui

引用次数: 4

Directed Policy Search Using Relevance Vector Machines 基于相关向量机的定向策略搜索

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.13

Ioannis Rexakis, M. Lagoudakis

{"title":"Directed Policy Search Using Relevance Vector Machines","authors":"Ioannis Rexakis, M. Lagoudakis","doi":"10.1109/ICTAI.2012.13","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.13","url":null,"abstract":"Several recent learning approaches based on approximate policy iteration suggest the use of classifiers for representing policies compactly. The space of possible policies, even under such structured representations, is huge and must be searched carefully to avoid computationally expensive policy simulations (rollouts). In our recent work, we proposed a method for directed exploration of policy space using support vector classifiers, whereby rollouts are directed to states around the boundaries between different action choices indicated by the separating hyper planes in the represented policies. While effective, this method suffers from the growing number of support vectors in the underlying classifiers as the number of training examples increases. In this paper, we propose an alternative method for directed policy search based on relevance vector machines. Relevance vector machines are used both for classification (to represent a policy) and regression (to approximate the corresponding relative action advantage function). Exploiting the internal structure of the regress or, we guide the probing of the state space only to critical areas corresponding to changes of action dominance in the underlying policy. This directed focus on critical parts of the state space iteratively leads to refinement and improvement of the underlying policy and delivers excellent control policies in only a few iterations, while the small number of relevance vectors yields significant computational time savings. We demonstrate the proposed approach and compare it with our previous method on standard reinforcement learning domains.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799208","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

Mining Fuzzy Association Rules from Heterogeneous Probabilistic Datasets 从异构概率数据集中挖掘模糊关联规则

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.116

Bin Pei, Tingting Zhao, Suyun Zhao, Hong Chen

{"title":"Mining Fuzzy Association Rules from Heterogeneous Probabilistic Datasets","authors":"Bin Pei, Tingting Zhao, Suyun Zhao, Hong Chen","doi":"10.1109/ICTAI.2012.116","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.116","url":null,"abstract":"Association rule mining (ARM), as a useful method to discover relations between attributes of objects, has been widely studied. The previous methods focused on ARM either from a certain dataset with different type attributes, or from a probabilistic dataset with only Boolean attributes. However, little work on ARM from a probabilistic dataset with coexistence of different type attributes has been mentioned. Such dataset is named Heterogeneous Probabilistic Dataset (HPD), which is prevalent in the real-world applications. This paper develops a generic framework to discover association rules from a HPD. Considering the different type data in the dataset, we first convert a HPD to a probabilistic dataset with fuzzy sets by fuzzification. A novel Shannon-like Entropy is then introduced to measure the information of an item with coexistence of fuzzy uncertainty hidden in different type data and random uncertainty in the transformed dataset. Based on this Shannon-like Entropy, Support and Confidence degrees for such multi-uncertain dataset are defined. Finally, we design an Apriori-like algorithm to mine association rules from a HPD using the above measures. Experimental results show that the proposed algorithm for HPD is feasible and effective.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134538879","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

A Collaborative Approach to Solve a Nurse Scheduling Problem 一种解决护士调度问题的协作方法

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.54

Juan Pablo Cares, M. Riff

引用次数: 1

Mel-frequency Cepstral Coefficients for Eye Movement Identification 眼动识别的mel频倒谱系数

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.42

Cuong V Nguyen, Vu C. Dinh, L. Ho

引用次数: 46

A Parallel Radial Basis Probabilistic Neural Network for Scalable Data Mining in Distributed Memory Machines 基于并行径向基概率神经网络的分布式存储机可扩展数据挖掘

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.155

Y. Kokkinos, K. Margaritis

引用次数: 5

Adaptive Fuzzy Rule-Based Classification System Integrating Both Expert Knowledge and Data 集成专家知识和数据的自适应模糊规则分类系统

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.114

W. Tang, K. Mao, L. Mak, G. Ng

引用次数: 6

Advanced Block Detection and Quantification of Fibrotic Areas in Microscopy Images of Obstructive Nephropathy 梗阻性肾病显微镜图像中纤维化区域的高级块检测和定量

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.130

T. Goudas, Ilias Maglogiannis, A. Chatziioannou

引用次数: 0

Latent Beta Topographic Mapping 潜伏Beta地形测绘

2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.27

Kirthevasan Kandasamy

引用次数: 0