2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)最新文献

Uncertain Reasoning in Multi-agent Ontology Mapping on the Semantic Web 语义Web上多智能体本体映射中的不确定推理

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.11

M. Nagy, M. Vargas-Vera, E. Motta

引用次数: 1

Genetic Evolution of a Neural Network for the Autonomous Control of a Four-Wheeled Robot 四轮机器人自主控制神经网络的遗传进化

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.13

W. Elmenreich, G. Klingler

引用次数: 24

Jason Smiles: Incremental BDI MAS Learning Jason Smiles:增量BDI MAS学习

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.16

A. Guerra-Hernández, G. Ortiz-Hernández, W. A. Luna-Ramírez

引用次数: 6

A Multi-threads Architecture for the Motion Coordination of a Heterogeneous Multi-robot System 异构多机器人系统运动协调的多线程体系结构

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.44

F. Marchese

引用次数: 1

Shake – Regicide: A New Heuristic for the Diversity Control of Evolutionary Algorithms 摇-弑君:进化算法多样性控制的新启发式

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.9

J. Ramírez, M. Rivera, A. Hernandez-Aguirre

引用次数: 0

Using Translation Paraphrases from Trilingual Corpora to Improve Phrase-Based Statistical Machine Translation: A Preliminary Report 利用三语语料库的翻译释义改进基于短语的统计机器翻译:初步报告

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.34

F. Herrera, L. Luna

引用次数: 3

Algorithm for Affective Pattern Recognition by Means of Use of First Initial Momentum 基于第一初始动量的情感模式识别算法

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.21

R. Romero-Herrera, F. Funes, J. Y. Montiel-Pérez

引用次数: 0

Adaptive Hierarchical Fuzzy CMAC Controller with Stable Learning Algorithm for Unknown Nonlinear Systems 未知非线性系统的稳定学习自适应层次模糊CMAC控制器

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.26

F. Ortiz, Wen Yu, M. Moreno-Armendáriz

{"title":"Adaptive Hierarchical Fuzzy CMAC Controller with Stable Learning Algorithm for Unknown Nonlinear Systems","authors":"F. Ortiz, Wen Yu, M. Moreno-Armendáriz","doi":"10.1109/MICAI.2007.26","DOIUrl":"https://doi.org/10.1109/MICAI.2007.26","url":null,"abstract":"In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control architecture that merges hierarchical structure, CMAC neural network and fuzzy logic. The input space dimension in CMAC is a time-consuming task especially when the number of inputs is huge this would be overload the memory and make the neuro-fuzzy system very hard to implement. This is can be simplified using a number of low-dimensional fuzzy CMAC in a hierarchical form. A new adaptation law is obtained for the method proposed, the overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results for its applications to one example is presented to demonstrate the performance of the proposed methodology.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"34 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132835754","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: 0

Incremental Refinement of Solutions for Dynamic Multi Objective Optimization Problems 动态多目标优化问题解的增量细化

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.47

C. E. Mariano-Romero, M.E.F. Morales

{"title":"Incremental Refinement of Solutions for Dynamic Multi Objective Optimization Problems","authors":"C. E. Mariano-Romero, M.E.F. Morales","doi":"10.1109/MICAI.2007.47","DOIUrl":"https://doi.org/10.1109/MICAI.2007.47","url":null,"abstract":"MDQL is an algorithm, based on reinforcement learning, for solving multiple objective optimization problems, that has been tested on several applications with promising results. MDQL discretizes the decision variables into a set of states, each associated with actions to move agents to contiguous states. A group of agents explore this state space and are able to find Pareto sets applying a distributed reinforcement learning algorithm. The precision of the Pareto solutions depends on the chosen granularity of the states. A finer granularity on the states creates more precise solutions but at the expense of a larger search space, and consequently the need for more computational resources. In this paper, a very important improvement is introduced into the original MDQL algorithm to incrementally refined the Pareto solutions. The new algorithm, called IMDQL, starts with a coarse granularity to find an initial Pareto set. A vicinity for each of the Pareto solutions in refined and a new Pareto set is founded in this refined state space. This process continues until there is no more improvement within a small threshold value. It is shown that IMDQL not only improves the solutions found by MDQL, but also converges faster. MDQL has also been tested on the solutions of dynamic optimization problems. In this paper, it is also shown that the adaptation capabilities observed in MDQL can be improved with IMDQL. IMDQL was tested on the benchmark problems proposed by Jin. Performance evaluation was made using the Collective Mean Fitness metric proposed by Morrison. IMDQL was compared against an standard evolution strategy with the covariance matrix adaptation (CMA-ES) with very promising results.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"88 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307109","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: 2

Modelling Intelligent Agents through Causality Theory 基于因果关系理论的智能体建模

2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI) Pub Date : 2007-11-04 DOI: 10.1109/MICAI.2007.25

H. Ceballos, F. Cantú

引用次数: 7