Temporal reasoning for intuitive specification of context-awareness

One of the most important challenges of the creation of intelligent environments is the specifications of what intelligent behaviours the system will exhibit. The processing of these situations can be computationally demanding. We report on the advances of the specification of a rule-based language which allows for the natural expression of situations of interest as those which occur on Intelligent Environments. The language focuses on quasi real-time situations and includes new temporal operators which allow a natural reference to time instants and to intervals. We explained how the system is implemented and how the system was validated within a Smart Office scenario

A survey on the evolution of the notion of context-awareness

The notion of Context has been considered for a long time in different areas of Computer Science. This article considers the use of context-based reasoning from the earlier perspective of AI as well as the newer developments in Ubiquitous Computing. Both communities have been somehow interested in the potential of context-reasoning to support real-time meaningful reactions from systems. We explain how the concept evolved in each of these different approaches. We found initially each of them considered this topic quite independently and separated from each other, however latest developments have started to show signs of cross-fertilization amongst these areas. The aim of our survey is to provide an understanding on the way context and context-reasoning were approached, to show that work in each area is complementary, and to highlight there are positive synergies arising amongst them. The overarching goal of this article is to encourage further and longer-term synergies between those interested in further understanding and using context-based reasoning

Discovering frequent user-environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user's needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user's most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this paper we present a system that takes information collected by sensors as a starting point, and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction which provides the user the option to fine tune their preferences in a natural way, just by speaking to the system

Sensorization and intelligent systems in energetic sustainable environments

Sustainability is an important topic of discussion in our world. However, measuring sustainability and assessing behaviors is not always easy. Indeed, and in order to fulfill this goal, in this work it will be proposed a multi-agent based architecture to measure and assess sustainable indicators taken from a given environment. These evaluations will be based on past and present behaviors of the users and the particularities of the setting, leading to the evaluation of workable indicators such as gas emissions, energetic consumption and the users fitting with respect to the milieu. Special attention is given to user interaction and user attributes to calculate sustainable indicators for each type of structure, i.e., the aim of this scheme is to promote sustainability awareness and sustainable actions through the use of sustainable markers calculated in terms of the information gathered from the environment.The research presented is partially supported by a portuguese doctoral grant, SFRH/BD/78713/2011, issued by the Fundação da Ciência e Tecnologia (FCT) in Portugal

Ubiquitous sensorization for multimodal assessment of driving patterns

Sustainability issues and sustainable behaviours are becoming concerns of increasing signi cance in our society. In the case of transportation systems, it would be important to know the impact of a given driving behaviour over sustainability factors. This paper describes a system that integrates ubiquitous mobile sensors available on devices such as smartphones, intelligent wristbands and smartwatches, in order to determine and classify driving patterns and to assess driving e ficiency and driver's moods. It first identi fies the main attributes for contextual information, with relevance to driving analysis. Next, it describes how to obtain that information from ubiquitous mobile sensors, usually carried by drivers. Finally, it addresses the multimodal assessment process which produces the analysis of driving patterns and the classi cation of driving moods, promoting the identifi cation of either regular or aggressive driving patterns, and the classi fication of mood types between aggressive and relaxed. Such an approach enables ubiquitous sensing of personal driving patterns across diff erent vehicles, which can be used in sustainability frameworks, driving alerts and recommendation systems.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012). It is also supported by a doctoral grant, SFRH/BD/78713/2011, issued by FCT in Portugal

Harnessing content and context for enhanced decision making

In a time in which a significant amount of interpersonal interactions take place online, one must enquire to which extent are these milieus suitable for supporting the complexity of our communication. This is especially important in more sensitive domains, such as the one of Online Dispute Resolution, in which inefficient communication environments may result in misunderstandings, poor decisions or the escalation of the conflict. The conflict manager, in particular, may find his skills severely diminished, namely in what concerns the accurate perception of the state of the parties. In this paper the development of a rich communication framework is detailed that conveys contextual information about their users, harnessed from the transparent analysis of their behaviour while communicating. Using it, the conflict manager may not only better perceive the conflict and how it affects each party but also take better contextualized decisions, closer to the ones taken in face-to-face settings.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT { Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst- OE/EEI/UI0752/2014

Is Context-aware Reasoning = Case-based Reasoning?

The purpose of this paper is to explore the similarities and differences and then argue for the potential synergies between two methodologies, namely Context-aware Reasoning and Case-based Reasoning, that are amongst the tools which can be used for intelligent environment (IE) system development. Through a case study supported by a review of the literature, we argue that context awareness and case based reasoning are not equal and are complementary methodologies to solve a domain specific problem, rather, the IE development paradigm must build a cooperation between these two approaches to overcome the individual drawbacks and to maximise the success of the IE systems

Context-aware discovery of human frequent behaviours through sensor information interpretation

The ability of discovering frequent behaviours of the users allows an environment to act intelligently, for example automating some devices’ activation. Moreover, such frequent behaviours could be used to understand and detect bad or unhealthy habits. Such a discovering process must be as unobtrusive and transparent as possible. In that sense, the ability of inferring interesting information from sensors installed in the environment plays an essential role in order to provide the discovering process with meaningful data. The importance of this system is clear due to the fact the process of discovering frequent behaviours will totally depend upon the actions/activities identified by such a system. This development reinforces the link between context-awareness and human behaviour understanding as it can perceive a current situation, compare it to typical behaviour, and differentiate between the two