How Intelligent Are Ambient Intelligence Systems?

Journal of Ambient Intelligence and Smart Environments, 2019

Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore's law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

Journal of Universal Computer Science, 2010

Ambient Intelligence (AmI) has emerged in the past 10 years as a multidisciplinary field within ubiquitous computing, attracting considerable research, funding and public attention and leading to many research groups, and conferences specifically focused on Ambient Intelligence topics. From its conception, AmI has always been a field strongly driven by a particular vision of how ICT technologies would shape our future. This has given the AmI vision, essentially as proposed by ISTAG, an excessively central role in shaping the field and setting its research agenda. We argue that this inspiring vision should no longer be the main driver for AmI research and that we should now re-interpret its role in the background of 10 years of research. In this paper, we reflect on what it means for AmI to move behind its foundational vision and we identify a number of emerging trends around some of its core concepts, more specifically the notion of intelligence, the system view and the requirements process. The main motivation is to search for alternative research directions that may be more effective in delivering today the essence of the AmI vision, even if they mean abandoning some of the currently prevailing approaches and assumptions. Overall, these trends provide a more holistic view of AmI and may represent important contributions for bringing this field closer to realisation, delivery and real social impact.

2004

We investigate the application of a logical model of agency, known as the KGP model, to develop agents for ambient intelligence applications. Using a concrete scenario, we illustrate how the logical formalism employed by a KGP agent allows a person to access the surrounding ambient through the agent in a transparent manner. We evaluate our claims by implementing the resulting interactions in PROSOCS, a prototype multi-agent systems platform that allows KGP agents to be deployed as components of ambient intelligence applications.

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.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2000

The imperfect nature of context in ambient intelligence environments, and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents, and provide methods for handling the imperfect and distributed nature of context. We propose a solution based on the multi-context systems (MCS) formalism, in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules, associating concepts used by different contexts. To handle the imperfect nature of context, we extend MCS with non-monotonic features-local defeasible theories, defeasible mappings, and a preference ordering on the system contexts. In this paper, we present the novel representation model, called contextual defeasible logic, describe how its elements are used to derive distributed conclusions through a proof theory, and propose an algorithm for distributed query evaluation that implements the proof theory of contextual defeasible logic. The application of the proposed approach in a scenario from the ambient intelligence domain demonstrates how its distinct features overcome the challenges imposed by the special characteristics of ambient intelligence environments. Index Terms-Ambient intelligence, contextual reasoning, defeasible reasoning, multi-context systems (MCS). I. INTRODUCTION C OMPUTING is moving toward pervasive ubiquitous environments in which devices, software agents, and services are all expected to seamlessly integrate and cooperate in support of human objectives, anticipating needs, and delivering services in an anywhere, anytime and for-all fashion [1]. Pervasive computing and ambient intelligence are considered to be key issues in the further development and use of information and communication technologies, as evidenced, for example, by the IST Advisory Group [2]. Ambient intelligence is a user-centric paradigm, it supports a variety of artificial intelligence methods Manuscript

IEEE Expert / IEEE Intelligent Systems, 2008

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. These environments should be aware of people's needs, customizing requirements and forecasting behaviors. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. Artificial intelligence research aims to include more intelligence in AmI environments, allowing better support for humans and access to the essential knowledge for making better decisions when interacting with these environments. This article, which introduces a special issue on AmI, views the area from an artificial intelligence perspective.

Modeling and Using Context, 2007

Computer Science and Information Systems, 2007

Ambient Intelligence aims to enhance the way people interact with their environment to promote safety and to enrich their lives. A Smart Home is one such system but the idea extends to hospitals, public transport, factories and other environments. The achievement of Ambient Intelligence largely depends on the technology deployed (sensors and devices interconnected through networks) as well as on the intelligence of the software used for decision-making. The aims of this article are to describe the characteristics of systems with Ambient Intelligence, to provide examples of their applications and to highlight the challenges that lie ahead, especially for the Software Engineering and Knowledge Engineering communities. In particular we address system specification and verification for the former and knowledge acquisition from the vast amount of data collected for the latter.