Resumen de Intelligent techniques for context-aware systems
Ambient Intelligence and Smart Environments (SmE) emphasize on greater user friendliness, more efficient services support, user-empowerment, and support for human interactions. In this vision, people will be surrounded by intelligent and intuitive interfaces embedded in everyday objects around us and an environment recognising and responding to the presence of individuals in an invisible way.
This thesis is mainly concerned with the open problem in context-aware systems: the evaluation of these systems in Ambient Intelligence (AmI) environments.
With regard to this issue, we argue that due to highly dynamic properties of the AmI environments, it should exist a methodology for evaluating these systems taking into account the type of scenarios. However in order to support with a solid ground for that discussion, some elements are to be discussed as well. In particular, we:
- use a commercial platform that allows us to design and manage the contextual information of context- aware systems by means of a context manager included in the architecture; - analyze the formal representation of this contextual information by means of a knowledge based system (KBS); - discuss the possible methodologies to be used for modelling knowledge in KBS and our approach; - give reasons why intelligent agents is a valid technique to be applied to systems in AmI environments; - propose a generic multi-agent system (MAS) architecture that can be applied to a large class of envisaged AmI applications; - propose a multimodal user interface and its integration with our MAS; - propose an evaluation methodology for context-aware systems in AmI scenarios.
The formulation of the above mentioned elements became necessary as this thesis was developed. The lack of an evaluation methodology for context-aware systems in AmI environments, where so many issues to be covered, took us to the main objective of this thesis. In this regard we: - provide an updated and exhaustive state-of-the-art of this matter; - examine the properties and characteristics of AmI scenarios; - put forward an evaluation methodology; - experimentally test our methodology in AmI scenarios.
