Analysis and design of multiagent systems using MAS-CommonKADS

This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network

Automated Purchase Negotiations in a Dynamic Electronic Marketplace

Nowadays, there is a surge of B2C and B2B e-commerce operated\ud on the Internet. However, many of these systems are often nothing\ud more than electronic product or service catalogues. Against this background,\ud it is argued that new generation systems based on automatic\ud negotiation will emerge. This paper covers a particular kind of automatic\ud negotiation systems, where a number of participants in a mobile\ud dynamic electronic marketplace automatically negotiate the purchase of\ud products or services, by means of multiple automated one-to-one bargainings.\ud In a dynamic e-marketplace, the number of buyers and sellers\ud and their preferences may change over time. By mobile we mean that\ud buyers in a commercial area may initiate simultaneous negotiations with\ud several sellers using portable devices like cell phones, laptops or personal\ud digital assistants, so these negotiations do not require participants to be\ud colocated in space. We will show how an expressive approach to fuzzy\ud constraint based agent purchase negotiations in competitive trading environments,\ud is ideally suited to work on these kind of e-marketplaces. An\ud example of mobile e-marketplace, and a comparison between an expressive\ud and an inexpressive approach will be presented to show the efficiency\ud of the proposed solution

Un enfoque práctico para la localización de usuarios mediante Bluetooth en entornos domóticos

Para que un sistema domótico pueda adaptarse adecuadamente\ud a las preferencias de sus diferentes usuarios, debe ser capaz de determinar\ud en cada momento en qué habitación se encuentran éstos dentro\ud de la vivienda. Este artículo presenta un sistema de localización especialmente\ud orientado a su utilización dentro del hogar inteligente. Cada\ud usuario del sistema lleva consigo un dispositivo personal Bluetooth, a\ud partir del cual el sistema puede identi carle y localizarle dentro de la\ud vivienda. El sistema se ha desarrollado dentro de una arquitectura multiagente\ud específicamente diseñada para ser utilizada en un hogar digital\ud capaz de ofrecer servicios a sus habitantes en función de su ubicación

A contextual ontology to provide location-aware services and interfaces in smart environments

Context-aware computing is about gathering user information and their environment such as user location and\ud preferences, service status, and nearby devices. Such context information is used to adjust enviroment settings to suit user\ud needs and preferences. As environments can change rapidly, applications must be aware of it and adapt their behaviour in\ud real time. We describe an application that introduces intelligent agents in smart homes to provide location-aware services\ud and interfaces. This application must keep an eye on some context information to carry out user preferences. Our\ud approach is based on a contextual ontology that is a key component to enable context sharing and representation

A fully-distributed, multiagent approach to negotiation in mobile ad-hoc networks

This paper presents an interaction protocol intended to be used in distributed negotiation problems using software agents,\ud which could be applied to multi-agent systems deployed over Personal Digital Assistants (PDAs) connected via wireless\ud networks. We are especially interested in semi-competitive scenarios, where each agent in the system acts on behalf of a\ud user, trying to maximize its user preferences while pursuing a common agreement. In these conditions, and especially if\ud we are dealing with open and dynamic environments like mobile ad-hoc networks, the goals and attitudes of software\ud agents cannot be guaranteed. Taking this into account we propose a protocol where interaction among agents is done in a\ud fully-distributed manner, so that no user can have negotiation privileges over the others

Infraestructura para servicios e interfaces sensibles a la localicación en hogares inteligentes

Some not widespread environments like sophisticated vehicles, adjust controlled elements,\ud like the seat and the rearview, in order to match the preferences of their users. In this context\ud computer systems are fully capable of providing customized interfaces for users. However, this kind\ud of service customization has not yet reached the home environment. Inside real home environments,\ud we can find new services based on automatizing traditional ones, which make our lives easier and\ud more comfortable. However, this services are provided independently, the degree of personalization is\ud still very low, and the results are insufficient. The smart home must release the user from performing\ud routine and tedious tasks to achieve comfort, security, and effective energy management. To achieve\ud this goal, designed systems must use all posible components at home, providing a high quality service.\ud In this paper we extend our previous work on using multiagent systems to build a smart home\ud environment. We describe its funcionality and introduce a new ontology in order to make easy agent\ud communication and knowledge sharing

Modelo de negociación automática bilateral para entornos abiertos de comercio electrónico

Este artículo presenta un modelo para la negociación automática bilateral y multiparámetrica en entornos abiertos de\ud comercio electrónico. El modelo se describe en el ámbito de un entorno competitivo de agentes software, en el que uno\ud de los agentes del sistema actúa como comprador y otro de los agentes como vendedor. En un sistema abierto, la\ud incertidumbre acerca del comportamiento de otro agente es clave, correspondiéndose con la realidad presente en medios\ud convencionales de negociación o en comercio electrónico e Internet. Aún así, la actitud de un agente en un entorno\ud competitivo frente a otro agente, puede diferir en cuanto al nivel de confianza recíproca existente. De esta manera, el\ud modelo integra diferentes estrategias de interacción que son función de este nivel de confianza. Estas estrategias abarcan\ud desde un posicionamiento púramente competitivo, con criterios estrictos de mínima revelación de información, hasta una\ud estrategia cooperativa, en la que este criterio de privacidad se relaja. El modelo es capaz de encontrar una solución o\ud acuerdo justo si dicha solución existe, de manera que en el proceso de negociación ningún usuario tenga privilegios. Se\ud emplean restricciones difusas con prioridades (PFCSPs) con valoraciones y graduación de privacidad para representar\ud propuestas, ya sean específicas o de espacios de soluciones, y para definir el grado de satisfacción de estas posibles\ud soluciones. Dicho grado de satisfacción es función del cumplimiento de las restricciones impuestas al proceso de\ud negociación, del perfil del usuario, y de mecanismos de argumentación

Filtrando atributos para mejorar procesos de aprendizaje

IX Conferencia de la Asociación Española para la Inteligencia Artificial. Gijón, EspañaLos sistemas de aprendizaje automático han sido tradicionalmente usados para extraer conocimiento a partir de conjuntos de ejemplos descritos mediante atributos. Cuando la información de partida representa un problema real no se sabe, generalmente, qué atributos influyen en su resolución. En esos casos, la única opción a priori es utilizar toda la información disponible. Para evitar los problemas que esto conlleva se puede emplear un filtrado de atributos, previo al aprendizaje, que nos permita quedarnos sólo con los atributos más relevantes, aquellos que encierran la solución del problema. En este artículo se describe un método que realiza esta selección. Como se mostrará, está técnica mejora los procesos posteriores de aprendizaj