Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.
  This paper presents an autonomous intelligent agent developed for monitoring Alzheimer patients' health care in execution time in geriatric residences. The AGALZ (Autonomous aGent for monitoring ALZheimer patients) is an autonomous deliberative case-based planner agent designed to plan the nurses' working time dynamically, to maintain the standard working reports about the nurses' activities, and to guarantee that the patients assigned to the nurses are given the right care. The agent operates in wireless devices and is integrated with complementary agents into a multi-agent system, named ALZ-MAS (ALZheimer Multi-Agent System), capable of interacting with the environment. AGALZ description, its relationship with the complementary agents, and preliminary results of the multi-agent system prototype in a real environment are presented. Multi-Issue Negotiation protocols have been studied very widely and represent a promising field since most of negotiation problems in the real-world are complex ones including multiple issues. In particular, in reality issues are constrained each other. This makes agents' utilities nonlinear. There have been a lot of work on multi-issue negotiations. However, there have been very few work that focus on nonlinear utility spaces. In this paper, we assume agents have nonlinear utility spaces. For the linear utility domain, agents can aggregate the utilities of the issue-values by simple linear summation. In the real world, such aggregations are unrealistic. For example, we cannot just add up the value of car's tires and the value of car's engine when engineers design a car. In this paper, we propose an auction-based multiple-issue negotiation protocol among nonlinear utility agents. Our negotiation protocol employs several techniques, i.e., adjusting sampling, auction-based maximization of social welfare. Our experimental results show that our method can outperform the existing simple methods in particular in the huge utility space that can be often found in the real-world. Further, theoretically, our negotiation protocol can guarantee the completeness if some conditions are satisfied. E-commerce has been one of the success stories of the last decade. Developments in wireless communications and mobile computing have heralded an era of mobile commerce (m-commerce). Though a number of successful applications and services have been deployed, these are almost invariably inherently static in nature. By augmenting m-commerce with intelligent and autonomous components, the significant benefits of convenience and added value may be realized for the average shopper as they wander their local shopping mall or high street. By a selective and judicious juxtaposition of Ambient Intelligent (AmI) concepts and e-commerce precepts, the foundations for truly ubiquitous commerce (u-commerce) may be constructed. In this paper, the synergy between AmI and e-commerce is explored and developed, resulting in the description of a prototypical application that demonstrates the viability of this approach. Ambient intelligence (AmI) is a new multidisciplinary paradigm rooted in the ideas of NormanAuthor of the Invisible Computer [32]. and Ubiquitous Computing. AmI fosters novel anthropomorphic human–machine models of interaction. In AmI, technologies are deployed to make computers disappear in the background, while the human user moves into the foreground in complete control of the augmented environment. AmI is a user-centric paradigm, it supports a variety of artificial intelligence methods and works pervasively, nonintrusively, and transparently to aid the user. AmI supports and promotes interdisciplinary research encompassing the technological, scientific and artistic fields creating a virtual support for embedded and distributed intelligence. This paper introduces the SHOMAS Multiagent System that provides guidance on leisure facilities and suggestions for shopping in malls. The multiagent architecture incorporates reactive and deliberative agents that take decisions automatically. The developed deliberative agent provides suggestions in execution time, with the help of case-based planners. This agent is described together with its guidance and suggestion mechanism. SHOMAS has been tested successfully, and the results obtained are presented in this paper.