Journal of Ambient Intelligence and Smart Environments

Over the past decade, the world of Ambient Intelligence and smart environments has brought us a wide variety of novel applications with potential for exhibiting sophisticated intelligent behavior. These applications are called smart because they can sense, anticipate and adapt themselves to the context, desires and intentions of their users. However, the unpredictability of human behavior, the unanticipated circumstances of execution and a growing heterogeneity of future operational environments impose significant development challenges if these innovative applications are to gain wide acceptance in our daily life. Therefore, applying well-established software engineering methodologies is a fundamental prerequisite for delivering high quality and robust smart software applications.

This survey provides a illustrative presentation of software engineering best practices in the area of smart environments and Ambient Intelligence. The aim of this survey is not to explore and compare every possible related work in detail, but to offer insights into the latest developments in this domain and to further the research into the successful design, development and evaluation of Ambient Intelligence frameworks and applications.

An Ambient Intelligence (AmI) system is a pervasive system in which services have some intelligence in order to smoothly interact with users immersed in the environment. Users are the main entity in AmI systems. Thus, the services are user-centred with an adaptive interaction that provides users with more personalized facilities in a non-intrusive way. The deployment of AmI services is a hard task, especially when the system includes complex users' behaviours or complex deployments in terms of the devices and software participating at the system. Since one of the intrinsic requirements in these services is smooth interaction with users, the user, or at least a model of the user, should be incorporated in the development process. This makes the process of verification and validation of such services quite difficult. This paper proposes the use of Agent Based Social Simulation for a quick and feasible validation of AmI systems. Simulation in the first stages enables easy validation of the services before real environment deployment. The main challenge in this approach is how to simulate humans with realistic behaviours. In this paper, Social Simulation (SS) is used to tackle this problem. Specifically, this paper presents a methodology for the validation of AmI systems by SS. The realistic modelling of users and its validation are key elements of this process and involve challenges that the techniques proposed here are able to deal with. A real application example which shows the level of reality reached in the users' models and the benefits of the methodology is presented in this paper.

The users of Ambient Intelligence systems expect an intelligent behavior from their environment, receiving adapted and easily accessible services and functionality. This can only be possible if the communication between the user and the system is carried out through an interface that is simple (i.e. which does not have a steep learning curve), fluid (i.e. the communication takes place rapidly and effectively), and robust (i.e. the system understands the user correctly). Natural language interfaces such as dialog systems combine the previous three requisites, as they are based on a spoken conversation between the user and the system that resembles human communication. The current industrial development of commercial dialog systems deploys robust interfaces in strictly defined application domains. However, commercial systems have not yet adopted the new perspective proposed in the academic settings, which would allow straightforward adaptation of these interfaces to various application domains. This would be highly beneficial for their use in AmI settings as the same interface could be used in varying environments. In this paper, we propose a new approach to bridge the gap between the academic and industrial perspectives in order to develop dialog systems using an academic paradigm while employing the industrial standards, which makes it possible to obtain new generation interfaces without the need for changing the already existing commercial infrastructures. Our proposal has been evaluated with the successful development of a real dialog system that follows our proposed approach to manage dialog and generates code compliant with the industry-wide standard VoiceXML.

Assistive software becomes more and more important part of our everyday life. As it is not straightforward to create such a system, the engineering of assistive systems is a topic of current research with different applications in healthcare, education and industry. In this paper we introduce three contributions to this field of research. Whereas most assistive systems use approaches for intention recognition based on training data applicable to specific environments and applications, we introduce a training-free approach. We do that by showing that it is possible to generate probabilistic inference systems from causal models for human behavior. Additionally, we collect a list of requirements for context aware assistive software and human behavior modeling for intention recognition and showed that our system satisfies them. We then introduce a software architecture for assistive systems that provides support for this kind of modeling. In addition to introducing the modeling approach and the architecture we show in an experimental way that our approach is suited for smart environments. The collected list of requirements could help a software engineer create a robust and easily adaptable to changes in the environment assistive software.

This article is concerned with the engineering of societal information systems where technical components of a system – software agents – support the social network around which the system is centered. We propose agent-oriented modeling as a suitable software engineering approach for developing open and adaptive societal information systems. The article first outlines the steps of the software engineering process of agent-oriented modeling and shows how the resulting models can be mapped to the simulation environment. It then describes two case studies where agent-oriented modeling has successfully been applied. The first case study addresses the development of an agent-based decision-making system for helping customers in grocery shopping. The second case study treats the engineering of a societal information system for helping patients in finding healthcare providers. The simulation results from both case studies are presented and discussed. We conclude the article by comparing related work and drawing conclusions.

People often behave in Smart Environments by relying on spatial metaphors that deserve to turn into architectural abstractions. The paper presents a set of space-aware communication primitives that support the seamless integration of application components in an open-ended Smart Environment. Environment spaces provide subjective views of the environment according to specific spatial models, be they physical or logical. Software components communicate in a publish/subscribe style by contextualizing information in the spaces they are aware of. Space mappings allow components to interact even if they rely on different spatial models. The paper introduces a formal definition of basic spatial models and describes the spaces-based architectural abstractions through a reference example. Then it presents SIS (Space Integration Services), a concrete framework that reifies the abstractions, together with some performance measurements. Finally, it shows a concrete implementation of the reference scenario and compares the proposed approach with related work.

On March 29, 2012 the author successfully defended his PhD thesis entitles Personalized persuasion in Ambient Intelligence. The PhD Degree was awarded with honors.

On March 20, 2012 the author successfully defended his PhD thesis entitled Behavior modeling in smart environments using camera networks.