We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations ind...We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations indicate that, compared to the model with only one update rule, the the role of update dynamics should be paid more attention in cooperation behavior exhibits the richer phenomena, and the evolutionary game theory. Meanwhile, we also observe that the introduction of Moran rule, which needs to consider all neighbor＇s information, can markedly promote the aggregate cooperation level, that is, randomly selecting the neighbor proportional to its payoff to imitate will facilitate the cooperation among agents. Current results will contribute to further understand the cooperation dynamics and evolutionary behaviors within many biological, economic and social systems.展开更多
Inconsistencies or conflicts appearing in the integration of ontologies and general rules are handled by applying prioritizing and updating. First, a prioritized knowledge base is obtained by weighting information wei...Inconsistencies or conflicts appearing in the integration of ontologies and general rules are handled by applying prioritizing and updating. First, a prioritized knowledge base is obtained by weighting information weight. Then, based on the idea ＂abandoning the old for the new＂, the weight of each rule is greater than that of the information in ontologies. If ontologies conflict with general rules, then a new knowledge-base without any inconsistency or conflict is obtained by using rules with big weight updating information in ontologies with small weight. Thus, current logic programming solvers and description logic reasoners are employed to implement the reasoning services, such as querying etc. Updating based on prioritizing is more suitable for handling inconsistencies than other approaches to introducing non-standard semantics if knowledge bases are dynamically evolving. Moreover, a consistent knowledge base can be always maintained in the dynamical environment by updating outdated information with new information based on weighting. Finally, this approach to dealing with inconsistencies is feasibly exemplified.展开更多
Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledg...Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.展开更多
Incremental data mining is an attractive goal for many kinds of mining in large databases or data warehouses. A new incremental updating algorithm rule growing algorithm (RGA) is presented for efficient maintenance di...Incremental data mining is an attractive goal for many kinds of mining in large databases or data warehouses. A new incremental updating algorithm rule growing algorithm (RGA) is presented for efficient maintenance discovered association rules when new transaction data is added to a transaction database. The algorithm RGA makes use of previous association rules as seed rules. By RGA, the seed rules whether are strong or not can be confirmed without scanning all the transaction DB in most cases. If the distributing of item of transaction DB is not uniform, the inflexion of robustness curve comes very quickly, and RGA gets great efficiency, saving lots of time for I/O. Experiments validate the algorithm and the test results showed that this algorithm is efficient.展开更多
As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques w...Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.展开更多
挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tr...挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.60904063Tianjin Municipal Natural Science Foundation under Grant No.11JCYBJC06600+1 种基金the Development Fund of Science and Technology for the Higher Education in Tianjin under Grant No.20090813the 7th Overseas Training Project for the Young and Middle Teachers in Tianjin Municipal Universities
文摘We combine the Fermi and Moran update rules in the spatial prisoner＇s dilemma and snowdrift games to investigate the behavior of collective cooperation among agents on the regular lattice. Large-scale simulations indicate that, compared to the model with only one update rule, the the role of update dynamics should be paid more attention in cooperation behavior exhibits the richer phenomena, and the evolutionary game theory. Meanwhile, we also observe that the introduction of Moran rule, which needs to consider all neighbor＇s information, can markedly promote the aggregate cooperation level, that is, randomly selecting the neighbor proportional to its payoff to imitate will facilitate the cooperation among agents. Current results will contribute to further understand the cooperation dynamics and evolutionary behaviors within many biological, economic and social systems.
基金The National Natural Science Foundation of China (No60973003)
文摘Inconsistencies or conflicts appearing in the integration of ontologies and general rules are handled by applying prioritizing and updating. First, a prioritized knowledge base is obtained by weighting information weight. Then, based on the idea ＂abandoning the old for the new＂, the weight of each rule is greater than that of the information in ontologies. If ontologies conflict with general rules, then a new knowledge-base without any inconsistency or conflict is obtained by using rules with big weight updating information in ontologies with small weight. Thus, current logic programming solvers and description logic reasoners are employed to implement the reasoning services, such as querying etc. Updating based on prioritizing is more suitable for handling inconsistencies than other approaches to introducing non-standard semantics if knowledge bases are dynamically evolving. Moreover, a consistent knowledge base can be always maintained in the dynamical environment by updating outdated information with new information based on weighting. Finally, this approach to dealing with inconsistencies is feasibly exemplified.
基金Supported by the NSFC (No. 60772006, 60874105)the ZJNSF (Y1080422, R106745)Aviation Science Foundation (20070511001)
文摘Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.
基金The work is supported by Chongqing Municipal Education Commission Science Research Program( No.KJ060511)
文摘Incremental data mining is an attractive goal for many kinds of mining in large databases or data warehouses. A new incremental updating algorithm rule growing algorithm (RGA) is presented for efficient maintenance discovered association rules when new transaction data is added to a transaction database. The algorithm RGA makes use of previous association rules as seed rules. By RGA, the seed rules whether are strong or not can be confirmed without scanning all the transaction DB in most cases. If the distributing of item of transaction DB is not uniform, the inflexion of robustness curve comes very quickly, and RGA gets great efficiency, saving lots of time for I/O. Experiments validate the algorithm and the test results showed that this algorithm is efficient.
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
文摘Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.
文摘挖掘最大频繁项目集是多种数据挖掘应用中的关键问题,之前的很多研究都是采用Apriori类的候选项目集生成-检验方法.然而,候选项目集产生的代价是很高的,尤其是在存在大量强模式和/或长模式的时候.提出了一种快速的基于频繁模式树(FP-tree)的最大频繁项目集挖掘DMFIA(discover maximum frequent itemsets algorithm)及其更新算法UMFIA(update maximum frequent itemsets algorithm).算法UMFIA将充分利用以前的挖掘结果来减少在更新的数据库中发现新的最大频繁项目集的费用.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.70631003)国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2007AA04Z116)
