In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.
"Such intelligent environments are becoming realized as a result of miniaturization of electronic devices, increase in connectivity , and decrease in cost. When a user is identified as an inhabitant of an environment, his/her preference model is transferred to and managed by the intelligence of the environment, in order to better suit the inhabitants through adjustable parameters available within the environment such as temperature, lighting, and background noise    . "
[Show abstract][Hide abstract] ABSTRACT: Ubiquitous computing technology can be effectively utilized in shared environments where groups of people are in close proximity. Shared environments are pervasive in the real world and hence the way of managing such environments will impact on not only quality of life but also business competitiveness. However, making decisions in an intelligent shared environment is never straightforward. The intelligence needs to be capable of choosing its parameters to satisfy all of its inhabitants, who have different preferences and are heterogeneous in their influences on decision. Till today, there has been no thorough research to scientifically investigate this type of decision making problems, though many systems have been already deployed. This research proposes a methodology for making decisions in such circumstances. The current and future works addressed in this paper are also conductive to any human-centric networks such as service systems, since the issues addressed here are also essential constitutes of such human-centric networks.
Information Sciences 09/2014; 278. DOI:10.1016/j.ins.2014.03.076 · 4.04 Impact Factor
"Rough approximations are used to model syntax, semantics, and operations of information granules. Information granules permeate almost all human endeavors , , –, , , , , , . No matter what problem is taken into consideration, we usually set it up in a certain conceptual framework composed of some generic and conceptually meaningful entities, i.e., information granules, which we regard to be of relevance to the problem formulation and problem solving. "
[Show abstract][Hide abstract] ABSTRACT: Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.
"This framework offers context-aware fuzzy services by applying learning and cooperation concepts from multi-agent systems in order to maximize the comfort of its user. The work presented in Acampora et al. (2010) describes how an inference engine helps decide which service has to be provided to the user. This decision is aided by taking into account the current context and a set of policies that act as a mapping function in terms of determining which service should be offered. "
[Show abstract][Hide abstract] ABSTRACT: This work introduces an alternative approach to designing ambient intelligent environments by using a multi-agent system consisting
of agents that represent inhabitants (humans, animals, plants, and objects) of the environment and physical devices (sensors
and actuators) that control and monitor the environment. Inhabitants are able to compromise their own needs for the betterment
of the environment as a whole. This synergy creates a balance where each inhabitant potentially receives sub-optimal environmental
conditions but the environment as a whole achieves a optimal level. This work addresses several issues involving multiple
parameter optimization and constraint satisfaction while maintaining the well being and physical structure of the inhabitants
of an environment as well as the comfort of multiple human inhabitants sharing the same environment and its resources.
Journal of Ambient Intelligence and Humanized Computing 09/2011; 2(3):185-200. DOI:10.1007/s12652-011-0056-0
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