Нечеткие модели мультиагентных систем в распределенной среде

Рассмотрены архитектурные нечеткие модели мультиагентных систем в распределенной среде, обладающие преимуществами как реактивных, так и мотивированных делиберативных агентов. Предложены средства спецификации поведения нечетких агентов на основе трансформаций нечетких графов с использованием нечеткого координатора и механизма адаптации нечетких правил. Формализована процедура согласования поведения нечетких агентов в мультиагентных системах относительно нечетких атрибутов, задающих свойства объектов. Предложена функция полезности, позволяющая получить оптимальное поведение нечетких агентов в процессе многоатрибутного итеративного согласования в распределенной среде.Architectural fuzzy models of multiagent systems in distributed environment that possesses advantages of both reactive and motivated deliberative agents, are considered. Means of behavior specification of fuzzy agents are offered on the basis of fuzzy graphs transformations using fuzzy coordinator and mechanism for adaptation of fuzzy rules. A negotiation procedure of fuzzy agents’ behavior in multiagent systems is formalized in relation to fuzzy attributes that define properties of objects. A fitness function that allows to achieve an optimum behavior of fuzzy agents in the process of multiattribute iterative concordance in distributed environment, is offered

Принципы построения нечетких мультиагентных систем в распределенной среде

Рассматриваются средства спецификации поведения нечетких агентов на основе моделе-ориентированного подхода. Предложена процедура согласования поведения нечетких агентов в мультиагентных системах относительно нечетких атрибутов для объектов поставки и потребления. Разработан способ взаимодействия нечетких агентов в распределенной среде, основанный на значениях получаемого преимущества и коэффициентах согласия по всем раундам согласования.Розглянуто засоби специфікації поведінки нечітких агентів на основі моделе-орієнтованого підходу. Запропонована процедура узгодження поведінки нечітких агентів у мультиагентних системах відносно нечітких атрибутів для об'єктів постачання і споживання. Розроблений спосіб взаємодії нечітких агентів у розподіленому середовищі, що ґрунтується на значеннях отримуваної переваги і коефіцієнтах згоди за всіма раундами узгодження.Means of behavior specification of fuzzy agents on the basis of model-oriented approach are examined. A negotiation procedure of fuzzy agents’ behavior in multiagent systems is proposed in relation to fuzzy attributes for the objects of delivery and consumption. A method of cooperation of fuzzy agents in distributed environment is developed, that is based on a values of gain and coefficients of consensus by all rounds of negotiation

Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods

Agent-Based Product Configuration: towards Generalized Consensus Seeking

This paper will present an evolution of a fuzzy agent based platform which performed products configuration. As a first step, we used the notion of consensus to establish robust results at the end of the configuration process. We implemented the concept of generalized consensus which implied the consideration of consensuses from the beginning, in this way robust data are treated during the entire process and the final result enables the designer to distinguish the robust components and flexible ones in a set of configurations.Comment: 8 pages, 8 figures, 5 table

НЕЧЕТКИЕ МОДЕЛИ МУЛЬТИАГЕНТНЫХ СИСТЕМ \ud В РАСПРЕДЕЛЕННОЙ СРЕДЕ\ud

Рассмотрены архитектурные нечеткие модели мультиагентных систем в распределенной среде, обладающие преимуществами как реактивных, так и мотивированных делиберативных агентов. Предложены средства спецификации поведения нечетких агентов на основе трансформаций нечетких графов с использованием нечеткого координатора и механизма адаптации нечетких правил. Формализована процедура согласования поведения нечетких агентов в мультиагентных системах относительно нечетких атрибутов, задающих свойства объектов. Предложена функция полезности, позволяющая получить оптимальное поведение нечетких агентов в процессе многоатрибутного итеративного согласования в распределенной среде.\ud Architectural fuzzy models of multiagent systems in distributed environment that possesses advantages of both reactive and motivated deliberative agents, are considered. Means of behavior specification of fuzzy agents are offered on the basis of fuzzy graphs transformations using fuzzy coordinator and mechanism for adaptation of fuzzy rules. A negotiation procedure of fuzzy agents’ behavior in multiagent systems is formalized in relation to fuzzy attributes that define properties of objects. A fitness function that allows to achieve an optimum behavior of fuzzy agents in the process of multiattribute iterative concordance in distributed environment, is offered.\u

Cost-allocation problems for fuzzy agents in a fixed-tree network

Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such costs. We work only with tree networks and we assume that a minimum cost spanning tree network has already been constructed and now we are interested in the maintenance costs. The classic problem supposes that each agent stays for the entire time in the same node of the network. This paper introduces cost-allocation problems in a fixed-tree network with a set of agents whose activity over the nodes is fuzzy. Agent’s needs to pay for each period of time may differ. Moreover, the agents do not always remain in the same node for each period. We propose the extension of a very well-known solution for these problems: Bird’s rule.Ministerio de Economía y Competitividad MTM2017-83455-PJunta de Andalucía FQM23

An Intelligence Representation in Agent Systems: An Extended π-Calculus

Intelligent mobile agent technology is one of the most promising of the newer software paradigms for providing solutions to complex, distributed computing problems. Agent properties of autonomy, intelligence and mobility provide a powerful platform for implementations that can utilize techniques involving collaborative problem solving and adaptive behavior. Although the technological tools and capabilities have advanced to this point, research into formal models and extensions to support representations of this new computing paradigm has not been kept pace. Specifically, we find that current formal processing models are lacking in representation abilities for: (1) intelligence capabilities, (2) team-based problem-solving approaches, and (3) mobility. In this paper, we present an extension of π-calculus that addresses the first of these deficiencies, the representation of intelligence