In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user req...In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.展开更多
Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the ...Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.展开更多
Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can...Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.展开更多
Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligen...Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligence decision support system (AOBDIDSS) structure model,applies generalized decision function (GDF) to the decomposition of decision task specifications, and determines decomposition criteria and properties of decision task specifications based on GDF. Because the task decomposition based on GDF is equivalent to the decomposition of Bayesian network, we present the representation and decomposition methods of decision tasks and properties based on Bayesian network. On these bases, the decision task decomposition problems can be entailed basically to construct a multi-sectioned Bayesian network and sub-Bayesian networks related to decision task specifications. The method is used to analyze the representation and decomposition of decision tasks in medical diagnosis. The results show that the model and method is not only feasible, but also effective and novel.展开更多
This paper introduces a Gray map from （Fp ＋ vFp）n to F2pn, and describes the relationship between codes over Fp ＋ vFp and their Gray images. The authors prove that every cyclic code of arbitrary length n over Fp ...This paper introduces a Gray map from （Fp ＋ vFp）n to F2pn, and describes the relationship between codes over Fp ＋ vFp and their Gray images. The authors prove that every cyclic code of arbitrary length n over Fp ＋ vFp is principal, and determine its generator polynomial as well as the number of cyclic codes. Moreover, the authors obtain many best-known p-ary quasic-cyclic codes in terms of their parameters via the Gray map.展开更多
基金National Natural Science Foundation of China (No. 70631003)
文摘In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.
基金The Key Project of NSFC(No.70631003)the Liberal Arts and Social Science Programming Project of Chinese Ministry of Education(No.07JA790109)
文摘Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.
文摘Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.
文摘Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligence decision support system (AOBDIDSS) structure model,applies generalized decision function (GDF) to the decomposition of decision task specifications, and determines decomposition criteria and properties of decision task specifications based on GDF. Because the task decomposition based on GDF is equivalent to the decomposition of Bayesian network, we present the representation and decomposition methods of decision tasks and properties based on Bayesian network. On these bases, the decision task decomposition problems can be entailed basically to construct a multi-sectioned Bayesian network and sub-Bayesian networks related to decision task specifications. The method is used to analyze the representation and decomposition of decision tasks in medical diagnosis. The results show that the model and method is not only feasible, but also effective and novel.
基金supported by NNSF of China under Grant Nos.11126174,60973125,71071045 and 71001032Talents youth Fund of Anhui Province Universities under Grant No.2012SQRL020ZD+1 种基金Youth Science Research Fund of Anhui University under Grant No.2009QN026Bthe 211 Project of Anhui University Grant No. KJTD002B
文摘This paper introduces a Gray map from （Fp ＋ vFp）n to F2pn, and describes the relationship between codes over Fp ＋ vFp and their Gray images. The authors prove that every cyclic code of arbitrary length n over Fp ＋ vFp is principal, and determine its generator polynomial as well as the number of cyclic codes. Moreover, the authors obtain many best-known p-ary quasic-cyclic codes in terms of their parameters via the Gray map.
