A Reasoning Framework for Ambient Intelligence

The special characteristics and requirements of intelligent environments impose several challenges to the reasoning processes of Ambient Intelligence systems. Such systems must enable heterogeneous entities operating in open and dynamic environments to collectively reason with imperfect context information. Previously we introduced Contextual Defeasible Logic (CDL) as a contextual reasoning model that addresses most of these challenges using the concepts of context, mappings and contextual preferences. In this paper, we present a platform integrating CDL with Kevoree, a component-based software framework for Dynamically Adaptive Systems. We explain how the capabilities of Kevoree are exploited to overcome several technical issues, such as communication, information exchange and detection, and explain how the reasoning methods may be further extended. We illustrate our approach with a running example from Ambient Assisted Living.

Engineering Applications of Artificial Intelligence, 2011

Event ma nagement and response generation are two essential aspects of systems for ambient intelligence. This work proposes handling these issues through the use of a semantic model for ambient intelligence which, under the umbrella of a philosophical and common-sense optic, describes what actions and events are, how they are connected, and how computational systems should think about their meaning. This model entails an approach with which to both reason about and model context events and generate behavioral responses to those events, when necessary. The model supports this ad hoc response generation by automatically composing services when those which are available do not meet the expected functionality (without requesting user intervention). An evaluation methodology is presented and illustrated with a case scenario, in which synthetic data has been generated to emulate events and analyze the system response. The evaluation of the system response is carried out on the basis of a vector of attributes.

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

Knowledge and Information Systems - KAIS

Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges in the study of Distributed Artiflcial Intelligence. This paper proposes a solution based on the Multi-Context Sys- tems paradigm, according to which local knowledge of ambient agents is encoded in rule theories (contexts), and information ∞ow between agents is achieved through mapping rules that associate concepts used by difierent contexts. To resolve potential inconsis- tencies that may arise from the interaction of contexts through their mappings (global con∞icts), we use a preference ordering on the system contexts, which may express the confldence that an agent has in the knowledge imported by other agents. On top of this model, we have developed four alternative strategies for glo...

2009

Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges in the eld of Distributed Arti cial Intelligence, which have not yet been successfully addressed by current Ambient Intelligence systems. This thesis proposes a solution based on the Multi-Context Systems paradigm, in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by di erent contexts. To handle imperfect context, we extend Multi-Context Systems with non-monotonic features, such as local defeasible theories, defeasible mapping rules, and a preference ordering over the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through their mappings. We also provide an operational model in the form of a distributed algorithm for query evaluation, which is sound and complete with respect to the argumentation framework, as well as three alternative versions of the algorithm, each of which implements a diff erent strategy for conflict resolution. The four strategies, which mainly diff er in the type and extent of context and preference information that is used to resolve potential conflicts, have been evaluated in a simulated peer-to-peer system and implemented in Logic Programming in four diff erent logic metaprograms.