Hybrid system to analyze user's behaviour

2010

Research in context-aware systems has been moving towards reusable and adaptable architectures for managing more advanced human-computer interfaces. Ambient. Intelligence (AmI) investigates computer-based services, which are ubiquitous and based on a variety of objects and devices. Their intelligent and intuitive interfaces act as mediators through which people can interact with the ambient environment. In this paper we present an agent-based architecture which supports the execution of agents in AmI environments. Two case studies are also presented, an airport information system and a railway information system, which uses spoken conversational agents to respond to the user’s requests using the contextual information that includes the location information of the user.

2009

Intelligent Environments are supposed to act proactively anticipating user's needs and preferences in order to support people in their daily lives. Therefore, the capability of an Intelligent Environment to learn user's habits and common behaviours becomes an important step towards allowing an environment to provide such personalized services. In this chapter, we use two scenarios, we highlight the opportunities given by learned patterns in order to interpret and understand the habits of the user which allows to automate services in an environment for the benefit of its user. We propose a system which learns user's patterns of behaviour and an interaction system, based on speech recognition, which facilitates the use of such patterns in real applications.

IEEE Expert / IEEE Intelligent Systems, 2004

The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.

The goal of this project is to create a context aware home that utilizes multiagent system to monitor and execute appropriate actions based on the current state of the house. There are two main intelligent features introduced in this project architecture to facilitate the multi-agent system in a smart home environment. The first feature learns and adapts the movements and actions of the occupants. The second feature predicts events engaged by the occupants. The effectiveness of the integration on smart home is documented and presented using a simulation application of the technology in a working smart home environment. The proposed architecture expands from the conversion of passive sensors into smart devices by adding processing and analytical capabilities from the software integration.

2010

Abstract The significance that ambient intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. One of the most important aspects in AmI is the use of context-aware technologies in order to perceive stimuli from both the users and the environment.

Lecture Notes in Computer Science, 2003

An important problem in intelligent environments is how the system can identify and model users' activities. This paper describes a new technique for identifying correlations between sensors and activities in an intelligent environment. Intelligent systems can then use these correlations to recognize the activities in a space. The proposed approach is motivated by the need for distinguishing the critical set of sensors that identifies a specific activity from others that do not. We compare several correlation techniques and show that logistic regression is a suitable solution. Finally, we describe our approach and report preliminary results.

Developing Ambient Intelligence, 2008

This paper presents an Ambient Intelligence based distributed architecture that uses intelligent agents with reasoning and planning mechanisms. The agents have the ability to obtain automatic and real-time information about the context using a set of technologies, such as radio frequency identification, wireless networks and wireless control devices. The architecture presented can be implemented on a wide diversity of dynamic environments to manage tasks and services.

Lecture Notes in Computer Science

Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, music devices, etc. In the aims of Ambient Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments. This paper can be seen as a State of the Art of Ambient Intelligence, according to an Artificial Intelligence (AI) perspective. We will define Ambient Intelligence; refer some of their prototype and systems; and to analyze how the main Artificial Intelligence areas can be applied.

In this paper we present a Multi-Agent System (MAS) paradigm and discuss how it can be used to design intelligent and distributed systems. The main features of this MAS, such as natural distribution of the system, inherent intelligence of its agents, and their mobility help address a large scope of distributed applications including the domain of ambient intelligence. Other features of the MAS, like multiagent planning, context-awareness and adaptation are also very useful since they bring added value, by allowing to implement intelligent and collective behavior. The paper also presents a scenario of ambient intelligence and shows how it could be designed using the MAS paradigm.

Lecture Notes in Computer Science, 2012

In this paper we present mobile Multi-Agent Systems (MAS) as a specific paradigm to design intelligent and distributed applications in the context of Ambient Intelligence (AmI). We discuss how mobility, coupled with MAS, can be useful to meet the requirements of AmI. Indeed, the main features of mobile MAS, such as natural distribution of the system, inherent intelligence of the agents, and their mobility help to address a large scope of distributed applications in the domain of AmI. Other features of MAS, like multi-agent planning, context-awareness and self-adaptation are also very useful to bring an added value to AmI applications. They allow the implementation of both intelligent and collaborative agent behavior. This paper presents the Ao Dai project, that employs the mobile MAS paradigm, and serves as a prototype AmI environment. We also illustrate the functioning of the application through a scenario of user guidance in a smart environment.