Juan A. Botía

Ambient Assisted Living system for in-home monitoring of healthy independent elders

In this work, the process followed for the development of a specific Ambient Assisted Living system is presented. The proposed systems has been designed to monitor elders which live alone and want to keep living independently. The process covers all the phases in intelligent system development: requirement analysis, conceptual model specification, architectural design and evaluation. One of the main contributions of the proposed work is an exhaustive evaluation methodology that is integrated in the development process. A relevant characteristic of the evaluation process is that, from initial phases, commercial presentations of the products functionalities is possible. Another important contribution is related with the capability of the system to adapt its behavior to that of the monitored elder. The presented system is called Necesity. It has become a commercial product which is already working and giving service to elders in the South-East of Spain.

A Fuzzy Logic-Based System for Indoor Localization Using WiFi in Ambient Intelligent Environments

Ambient intelligence is a new information paradigm, where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive, and responsive to their needs. Hence, one of the important requirements for ambient intelligent environments (AIEs) is the ability to localize the whereabouts of the user in the AIE to address her/his needs. In order to protect user privacy, the use of cameras is not desirable in AIEs, and hence, there is a need to rely on nonintrusive sensors. There are various localization means that are available for outdoor spaces such as those which rely on satellite signals triangulation. However, these outdoor localization means cannot be used in indoor environments. The majority of nonintrusive and noncamera-based indoor localization systems require the installation of extra hardware such as ultrasound emitters/antennas, radio-frequency identification (RFID) antennas, etc. In this paper, we propose a novel indoor localization system that is based on WiFi signals which are free to receive, and they are available in abundance in the majority of domestic spaces. However, free WiFi signals are noisy and uncertain, and their strengths and availability are continuously changing. Hence, we present a fuzzy logic-based system which employs free available WiFi signals to localize a given user in AIEs. The proposed system receives WiFi signals from a large number of existing WiFi access points (up to 170 access points), where no prior knowledge of the access points locations and the environment is required. The system employs an incremental lifelong learning approach to adjust its behavior to the varying and changing WiFi signals to provide a zero-cost localization system which can provide high accuracy in real-world living spaces. We have compared our system in both simulated and real environments with other relevant techniques in the literature, and we have found that our system outperforms the other systems in the offline learning process, whereas our system was the only system which is capable of performing online learning and adaptation. The proposed system was tested in real-world spaces from a living lab intelligent apartment (iSpace) to a town center apartment to a block of offices. In all these experiments, our system has been highly accurate in detecting the user in the given AIEs, and the system was able to adapt its behavior to changes in the AIE or the WiFi signals. We envisage that the proposed system will play an important role in AIEs, especially for privacy concerned situations like elderly care scenarios.

Testing and Debugging of MAS Interactions with INGENIAS

Testing and debugging activities are getting more relevance in multi-agent systems (MAS) as agents become part of real applications. Both activities are related, since failures to be debugged are frequently detected during the execution of tests. The support for these activities is not yet as complete as other activities of MAS development. However, agent oriented software engineering methodologies are incorporating new testing and debugging features. In this direction, the paper introduces advances made in the INGENIAS agent development framework towards a complete coverage of testing and debugging activities. The advances are compared with respect to a categorisation of related works in the agent literature. This categorisation will be useful for evaluating and planning issues for improvement in the context of INGENIAS.

Intelligent data analysis applied to debug complex software systems

The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well, this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules.

Validating ambient intelligence based ubiquitous computing systems by means of artificial societies

This paper introduces a new methodology based on the use of Multi-Agent Based Simulations (MABS) for testing and validation of Ambient Intelligence based Ubiquitous Computing (UbiCom) systems. An ambient intelligence based UbiCom is a pervasive system in which services have some intelligence in order to smoothly interact with users immersed in the environment. The motivation for this methodology is its application in UbiCom large-scale systems where large numbers of users are involved and in applications which deal with dangerous environments. In these cases, real tests are impractical and an artificial society is required. MABS allows building cheap and quick prototypes which can describe UbiCom systems. Analyzing these prototypes, if they are sufficiently descriptive, allows requisites violations in functionality of real UbiCom system designs to be discovered. MABSs and particularly the most descriptive ones can present very complex behaviors. Therefore, the MABS analysis obtained with the presented methodology is not trivial. Consequently, this paper also proposes two techniques for the analysis of general complex MABSs: forensic analysis and the use of simpler simulations. Moreover, the methodology proposes to inject elements of the actual UbiCom system in the simulated world to increase the confidence of the validation process. The proposal is illustrated with a detailed case study that considers a building on our campus and an AmI service for evacuation in case of fire.

Design and evaluation of an ambient assisted living system based on an argumentative multi-agent system

This paper focuses on ambient assisted living systems employed to monitor the ongoing situations of elderly people living independently. Such situations are represented here as contexts inferred by multiple software agents out of the data gathered from sensors within a home. Sensors can give an incomplete, sometimes ambiguous, picture of the world; hence, they often lead to inconsistent contexts and unreliability on the system as a whole. We report on a solution to this problem based on a multi-agent system where each agent is able to support its understanding of the context through arguments. These arguments can then be compared against each other to determine which agent provides the most reliable interpretation of the reality under observation.

On the application of clustering techniques to support debugging large-scale multi-agent systems

This work analyses the problematic of debugging a multi-agent system. It starts from the fact that MAS are a particular type of distributed systems in which the active entities are autonomous in the sense that behavior and knowledge of the whole system is distributed among agents. It situates the problem by firstly studying the classical approaches for conventional code debugging and also the techniques used in distributed systems in general. From this initial perspective, it tries to situate agent and multi-agent systems debugging. It finally proposes the use of conventional data mining tasks like clustering to, by summarising, help in debugging huge MAS.

Using cognitive agents in social simulations

Multi-Agent-Based Social Simulation (MABS) is a paradigm devoted to using agents as the modelling metaphor to simulate autonomous entities in a social world composed of a number of independent and interacting entities. Such models try to reproduce real environments and situations of interest within such environments. Most MABS platforms used today (e.g. MASON, Repast, NetLogo) see agents as very simple entities. However, there are situations in which a more intelligent kind of agent is needed. For example, when a society of persons with different roles and high-level behaviours must be modelled. In this paper, we address how to incorporate agents with cognitive skills into MABS.

A domain-specific language for context modeling in context-aware systems

Context-awareness refers to systems that can both sense and react based on their environment. One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper we present MLContext, a textual Domain-Specific Language (DSL) which is specially tailored for modeling context information. It has been implemented by applying Model-Driven Development (MDD) techniques to automatically generate software artifacts from context models. The MLContext abstract syntax has been defined as a metamodel, and model-to text transformations have been written to generate the desired software artifacts. The concrete syntax has been defined with the EMFText tool, which generates an editor and model injector. MLContext has been designed to provide a high-level abstraction, to be easy to learn, and to promote reuse of context models. A domain analysis has been applied to elicit the requirements and design choices to be taken into account in creating the DSL. As a proof of concept of the proposal, the generative approach has been applied to two different middleware platforms for context management.

Providing QoS through machine-learning-driven adaptive multimedia applications

We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.