2010 22nd IEEE International Conference on Tools with Artificial Intelligence最新文献_第2页

Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving 用于动态规划、机器学习和约束求解的模式导向表

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.103

Neng-Fa Zhou, Yoshitaka Kameya, Taisuke Sato

{"title":"Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving","authors":"Neng-Fa Zhou, Yoshitaka Kameya, Taisuke Sato","doi":"10.1109/ICTAI.2010.103","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.103","url":null,"abstract":"Mode-directed tabling amounts to using table modes to control what arguments are used in variant checking of subgoals and how answers are tabled. A mode can be min, max, + (input), (output), or nt (non-tabled). While the traditional table-all approach to tabling is good for finding all answers, mode-directed tabling is well suited to dynamic programming problems that require selective answers. In this paper, we present three application examples of mode-directed tabling, namely, (1) hydraulic system planning, a dynamic programming problem, (2) the Viterbi algorithm in PRISM, a probabilistic logic reasoning and learning system, and (3) constraint checking in evaluating Answer Set Programs (ASP). For the Viterbi application, the feature of enabling a cardinality limit in a table mode declaration plays an important role. For a PRISM program and a set of data, the explanations may be too large to be completely stored and the cardinality limit allows for Viterbi inference based on a subset of explanations. The mode nt, which specifies an argument that can participate in the computation of a tabled predicate but is never tabled either in subgoal or answer tabling, is useful in constraint checking for the Hamilton cycle problem encoded as an ASP. These examples demonstrate the usefulness of modedirected tabling.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"1 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":"115741908","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: 27

Enhancing MAS Environments with Organizational Mechanisms 以组织机制强化MAS环境

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1142/S0218213011000395

Sergio Esparcia, E. Argente, Roberto Centeno, Ramón Hermoso

引用次数: 6

Bayesian 3D Human Motion Capture Using Factored Particle Filtering 使用因子粒子滤波的贝叶斯三维人体动作捕捉

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.131

Abdallah Dib, C. Rose, F. Charpillet

引用次数: 3

A Closer Look at MOMDPs 近距离观察momdp

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.101

Mauricio Araya-López, Vincent Thomas, O. Buffet, F. Charpillet

引用次数: 58

Intelligent Energy Data Warehouse: What Challenges? 智能能源数据仓库:面临哪些挑战?

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.120

L. Copin, Herve Rey, Xavier Vasques, Anne Laurent, M. Teisseire

引用次数: 5

A Multi-Objective Genetic Algorithm to Test Data Generation 测试数据生成的多目标遗传算法

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.26

Gustavo H. L. Pinto, S. Vergilio

引用次数: 37

An Adaptive Prediction-Regret Driven Strategy for Bilateral Bargaining 双边议价中的适应性预测-后悔驱动策略

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.77

Shu-juan Ji, Ho-fung Leung

引用次数: 2

Refactoring of Ontologies: Improving the Design of Ontological Models with Concept Analysis 本体重构:用概念分析改进本体模型的设计

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.97

M. Hacene, Schahrazed Fennouh, R. Nkambou, Petko Valtchev

{"title":"Refactoring of Ontologies: Improving the Design of Ontological Models with Concept Analysis","authors":"M. Hacene, Schahrazed Fennouh, R. Nkambou, Petko Valtchev","doi":"10.1109/ICTAI.2010.97","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.97","url":null,"abstract":"It is now widely accepted that in order to optimize both their usage and their design and maintenance ontologies should comply to design quality criteria, e.g., absence of redundancies and appropriate level of abstraction. Yet given the variety and scope of activities comprised in the life-cycle of an ontological model (OM), such as adapting, splitting, populating, this quality is easily compromised, especially with ontologies of larger size and/or resulting from the merge of smaller ones. Conversely, restoring it through refactoring, i.e., restructuring of the ontology to improve defects, is knowingly a challenging task as relocating an ontology element can adversely affect its neighbors. We investigate here a holistic refactoring approach that, given an ontology, amounts to presenting its designer with a list of the most plausible abstract entities missing in it. The core of the approach is a recently devised concept analysis method, called 'relational', that allows deeper refactoring by feeding into the process various ontological relations, e.g., concept-to-property incidences. The focus here is put on the NLP-aspects of the refactoring, while we also provide some preliminary results from a series of validating experiments.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"85 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114010498","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

PKOPT: Faster k-Optimal Solution for DCOP by Improving Group Selection Strategy PKOPT:基于改进群体选择策略的DCOP快速k-最优解

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.70

Elnaz Bigdeli, M. Rahmaninia, M. Afsharchi

{"title":"PKOPT: Faster k-Optimal Solution for DCOP by Improving Group Selection Strategy","authors":"Elnaz Bigdeli, M. Rahmaninia, M. Afsharchi","doi":"10.1109/ICTAI.2010.70","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.70","url":null,"abstract":"A significant body of work in multiagent systems over more than two decades has focused on multi-agent coordination. Many challenges in multi-agent coordination can be modeled as Distributed Constraint Optimizations (DCOPs). Many complete and incomplete algorithms have been introduced for DCOPs but complete algorithms are often impractical for large-scale and dynamic environments which lead to study incomplete algorithms. Some incomplete algorithms produce k-optimal solutions; a k-optimal solution is the one that cannot be improved by any deviation by k or fewer agents. In this paper we focus on the only k-optimal algorithm which works for arbitrary k, entitled as KOPT. In both complete and incomplete algorithms, computational complexity is the major concern. Different approaches are introduced to solve this problem and improve existing algorithms. The main contribution of this paper is to decrease computational complexity of KOPT algorithm by introducing a new method for selecting leaders which should assign new values to a group of agents. This new approach is called Partial KOPT (PKOPT). PKOPT is an effective method to reduce computational load and power consumption in implementation. This paper under various assumptions presents an analysis of sequential and stochastic PKOPT algorithms.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"47 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":"122457648","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

Strategy and Fairness in Repeated Two-agent Interaction 重复双智能体交互中的策略与公平性

2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.75

Jianye Hao, Ho-fung Leung

引用次数: 5