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.
Ambient Intelligence looks to offer personalized services and provide users with easier and more efficient ways to communicate and interact with other people and systems @cite_14 @cite_24 . Since several users may coexist in AmI environments, it is quite probable that their preferences conflict and thus mechanisms are needed to allow users to cooperate. For instance, imagine a ubiquitous shopping mall @cite_32 @cite_0 where buying agents have to help users to buy the products, and vendor agents have to maximize their users' profits. Automated negotiation provides mechanisms that solve this particularly interesting problem. Some authors have already claimed that in most real world negotiations such as e-commerce @cite_35 @cite_18 @cite_7 , issues present interdependence relationships that make agents' utility functions complex. Therefore, the problem of complex utility functions in automated negotiation is also interesting for AmI applications.
@cite_35 @cite_18 @cite_14 @cite_7 @cite_32 @cite_24 @cite_0
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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. Ambient Intelligence (AmI) is an IT concept by which mobile users shall be seamlessly supported in their everyday activities. This includes interactions with remote resources as well as with their current physical environment. We have developed the so-called Ad hoc Service Grid (ASG) infrastructure that supports the latter form of interactions. It allows operators to cover arbitrary locations with ambient services in a drop-and-deploy fashion. An ambient service may autonomously distribute (replicate and migrate) within an ASG network to optimize its availability, response times, and network usage. In this article, we propose a fully decentralized, dynamic, and adaptive service placement algorithm for AmI environments like the ASG. This algorithm achieves a coordinated global placement pattern that minimizes the communication costs without any central controller. It does not even require additional communication among the replicas. Moreover, placement patterns stabilize if no changes occur in the environment while replicas still retain their ability to adapt. Mechanisms for self-organized placement of services are very important for AmI environments in general since they allow for autonomous adaptations to dynamic changes and, thus, remove the need for manual (re)configuration of a running system. We present a detailed evaluation of the algorithm's performance and compare it with three other algorithms to show its competitiveness. Furthermore, we discuss how the desired self-organizing behavior emerges from the interactions of a few simple, local rules that govern the individual placement decisions. In order to do so, we give an in-depth analysis of a series of emergent effects that are not directly encoded into the placement algorithm but stem from its collective dynamics. 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. Artificial Intelligence has become one of the most fundamental areas of computer science research. One line of research in artificial intelligence is associated with coordination of intelligent agents. Coordination takes place in multi-agent environments where distributed agents have limited knowledge about their surrounding environment; therefore, they continuously ask other agents to obtain required information. In general, the growing popularity of agents requires systematic coordination management and reputation system that enable agents to decide about their interacting partner and overall acting attitude. Using the reputation system, agents choose reliable agents to interact with. Here, the question arises that how the reputation mechanism helps agents to make the most prudent decisions that yield the best outcome. More specifically, agents need to use a decision-theoretic reasoning algorithm to optimize their decisions under uncertainty. In multi-agent systems, the reputation value is highly competitive because it is used as the beliefs of agents about one another. Thus, a carefully designed mechanism is required to maintain accuracy of this parameter.