Reasoning with user's preferences in ambient assisted living environments

Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet users' expectations. Preferences are fundamental in decision making, so it is an essential element in developing systems that guides the choices of the users. These choices can be decided through argument(s) which are known to have various strengths, as one argument can rely on more certain or vital information than the other. The analysis of survey conducted on preferences handling techniques in Artificial Intelligence (AmI), indicates that most of existing techniques lack the ability to handle ambiguity and/or the evolution of preferences over time. Further investigation identified argumentation technique as a feasible solution to complement existing work. Argumentation provides a means to deal with inconsistent knowledge and we explored its potentials to handle conflicting users preferences by applying to it several real world scenarios. The exploration demonstrates the usefulness of argumentation in handling conflicting preferences and inconsistencies, and provides effective ways to manage, reason and represents user's preferences. Using argumentation technique, this research provide a practical implementation of a system to manage conflicting situations, along with a simple interface that aids the flow of preferences from users to the system, so as to provide services that are better aligned with the users' behaviour. This thesis also describes the functionalities of the implemented system, and illustrates the functions by solving some of the complexities in users' preferences in a real smart home. The system detects potential conflict(s), and solves them using a redefined precedence order among some preference criteria. The research further show how the implemented Hybrid System is capable of interacting with external source's data. The system was used to access and filter live data (groceries products) of a UK supermarket chain store, through their application programming interface (API), and advise users on their eating habits, based on their set preference(s)

Context-aware systems testing and validation

Newer systems are still tested and validated following techniques which have been developed decades ago when systems were of a different nature. We report on an attempt to define a new method which is practical and focus on the concept of ‘context’ as a system aspect which have become more relevant in the development of the subsystem category called Intelligent Environments

Using argumentation to manage users' preferences

Argumentation has provided a means to deal with inconsistent knowledge. We explore the potential of argumentation to handle conflicting user preferences. Classical preference handling methods in Artificial Intelligence (AI) lack the ability to handle ambiguity and the evolution of preferences over time. Previous experiments conducted by the authors indicate the usefulness of argumentation systems to handle Ambient Intelligence (AmI) examples with the aforementioned characteristics. This paper explores a generalized framework that can be applied to handle user preferences in AmI. The paper provides an overall preference handling architecture which can be used to extend current argumentation systems. We show how the proposed system can handle multiple users with the introduction of personalised preference functions. We illustrate how user preferences can be handled in realistic ways in AmI environments (such as smart homes), by showing how the system can make decisions based on inhabitants’ preferences on lighting, healthy eating and leisure

An interface for managing users' preferences in AmI

One important concept of Ambient Intelligence (AmI) is meeting the user needs unobtrusively. To meet such needs, it is crucial to provide way(s) that will allow users to indicate their preferences to the system and allow users update them easily. As this will be used by the system to reason about their preferences and better align services with users behaviour. The paper aim to identify an effective way to manage, represent and reason with users preferences. A simple interface for managing users’ preferences was provided to help manage and resolve some of the complexities in users’ preferences. The interface also relied on textual menus for simplicity. Videos demonstration have been provided to show how the interface effectively works with a reasoning system in a smart house, providing different results when there is a change in users’ preference(s)

Using argumentation to solve conflicting situations in users' preferences in ambient assisted living

Preferences are fundamental in decision making, so understanding preference management is key in developing systems that guide the choices of the users. These choices can be decided through argument(s) which are known to have various strengths, as one argument can rely on more certain or vital information than the other. We explored argumentation technique from a previous study, and validated its potentials by applying to it several real life scenarios. The exploration demonstrates the usefulness of argumentation in handling conflicting preferences and inconsistencies, and provides effective ways to manage, reason and represents users' preferences. Using argumentation, we provide a practical implementation of a system to manage conflicting situations, and a simple interface that aids the flow of preferences from users to the system. We illustrated using the interface, how the changes in users' preferences can effect system output in a smart home. This article describes the functionalities of the implemented system, and illustrates the functions by solving some of the complexities in users' preferences in a real smart home. The system detects potential conflicts, and tries solve them using a redefined precedence order among some preference criteria. We also show how our system is capable of interacting with external sources data. The system was used to access and use live data of a UK supermarket chain store, through their application programming interface (API) and provide users suggestions on their eating habits, based on their set preference(s). The system was used to filter specific products from the live data, and check the product description, before advising the user accordingly

A survey on managing users' preferences in ambient intelligence

Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet users' expectations. This survey provides an account of the various ways that preferences have been handled in Artificial Intelligence. Our analysis indicates that most of those techniques lack the ability to handle ambiguity and the evolution of preferences over time. Further exploration shows that argumentation can provide a feasible solution to complement existing work. We illustrate our claim by using an intelligent environment case study

Context-aware systems architecture (CaSA)

Context-aware systems are becoming increasingly mainstream as more and more technology allows real-time collection of daily life data and it is more and more affordable to provide useful services to citizens in various situations of need. However, developers in this field are not well supported. Naturally we have inherited a number of methods and tools from past software engineering efforts to create previous computing systems. However the most recent generation of systems dominated by sensing supported context-awareness integrating a variety of data sources and with a higher expectation of personalized services delivered at the right time, place and in the right form, are not well supported. Developers need more guidance and support to pinpoint those valuable contexts and to work out ways of detecting them and activating the right services associated with these contexts. Our community has reported on various systems they created however not much is emerging in a way of a methodology, a standard, a transferable body of advice and guidance which can help teams next time they need to develop a new system. In this article we explain a couple of complementary methodologies which we have tried and tested through development of different context-aware projects. We argue these are of practical usefulness and provide an initial valid point of discussion for our community to create evolved versions of these which can be tested more widely to identify good practice in the area

Achieving multi-user capabilities through an indoor positioning system based on BLE beacons

The multiple user challenge is one of the issues that need to be addressed in order to facilitate the adoption of intelligent environments in everyday activities. The development of multi-user capabilities in smart homes is closely related to the creation of effective indoor positioning systems. This research work reports on the development and evaluation of an indoor positioning system that allows multi-user management in a smart home environment. The design of the BLE based system is presented, as well as its implementation and evaluation in the Smart Spaces Lab at Middlesex University. The validation of the system is shown as a case study in which it is used to develop multi-user capabilities in two context-aware systems of the laboratory. Video demonstrations are provided to illustrate the multi-user capabilities that were developed in the validation

The SEArch smart environments architecture

we report on a Smart Environment Architecture (SEArch) which has been developed to support innovative Ambient Assisted Living services. We explain SEArch at a conceptual level and also how it has been linked to a sensing environment. We compare SEArch to other similar systems reported in the technical literature. We illustrate how the system works using a practical automation scenario

A smart environments architecture (Search)

We report on a system architecture, SEArch, and its associated methods and tools we have been developing, testing and extending for several years through a number of innovation processes in the field of Smart Environments. We have developed these infrastructure in a bottom up fashion directed by the needs of the different projects as opposed to an ideal one which projects have to conform to. In this sense is practical and although necessarily incomplete, it has significant versatility and reasonable efficiency. Projects developed using this architecture have been funded by different companies and funding bodies in Europe. The different components of the architecture are explained through the software supporting those aspects of the system and through the functionality they exhibit in different practical scenarios, extracted from some of the projects implemented with SEArch