Soft Concurrent Constraint Programming

Soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. While there are many soft constraint solving formalisms, even distributed ones, by now there seems to be no concurrent programming framework where soft constraints can be handled. In this paper we show how the classical concurrent constraint (cc) programming framework can work with soft constraints, and we also propose an extension of cc languages which can use soft constraints to prune and direct the search for a solution. We believe that this new programming paradigm, called soft cc (scc), can be also very useful in many web-related scenarios. In fact, the language level allows web agents to express their interaction and negotiation protocols, and also to post their requests in terms of preferences, and the underlying soft constraint solver can find an agreement among the agents even if their requests are incompatible.Comment: 25 pages, 4 figures, submitted to the ACM Transactions on Computational Logic (TOCL), zipped file

Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process

Evolving connection weights between sensors and actuators in robots

International Symposium on Industrial Electronics. Guimaraes, 7-11 July 1997.In this paper, an evolution strategy (ES) is introduced, to learn reactive behaviour in autonomous robots. An ES is used to learn high-performance reactive behaviour for navigation and collisions avoidance. The learned behaviour is able to solve the problem in a dynamic environment; so, the learning process has proven the ability to obtain generalised behaviours. The robot starts without information about the right associations between sensors and actuators, and, from this situation, the robot is able to learn, through experience, to reach the highest adaptability grade to the sensors information. No subjective information about “how to accomplish the task” is included in the fitness function. A mini-robot Khepera has been used to test the learned behaviour

Побудова нейро-нечітких моделей на основі неструктурованих даних

Розглянуто методи вирішення задачі синтезу нейро-нечітких моделей. Запропоновано метод побудови нейро-нечітких мереж, який заснований на застосуванні витягнутого із заданої транзакційної бази даних набору асоціативних правил, використовуваних для визначення структури нейромоделі, а також для обчислення значень параметрів функцій належності та вагових коефіцієнтів. Запропонований метод дозволяє будувати прості нейро-нечіткі моделі, зручні для подальшого застосування на практиці.Рассмотрены методы решения задачи синтеза нейро-нечетких моделей. Предложен метод построения нейро-нечетких сетей, основанный на применении извлеченного из заданной транзакционной базы данных набора ассоциативных правил, используемых для определения структуры нейромодели, а также для вычисления значений параметров функций принадлежностей и весовых коэффициентов. Предложенный метод позволяет строить простые нейро-нечеткие модели, удобные для дальнейшего применения на практике.The methods of solving the problem of synthesis of neuro-fuzzy models are considered. The method for constructing fuzzy neural networks based on application of extracted from a given transaction database set of association rules that are used to determine the structure of neuro-fuzzy network, as well as to calculate the values of functions and accessory weights is proposed. The proposed method allows to construct a simple neuro-fuzzy models suitable for further use in practice