Decentralized Reasoning in Ambient Intelligence

Abstract

In Ambient Intelligence (AmI), reasoning is fundamental for identifying specific situations that may be meaningful and relevant to some applications. As in such systems usually not all context data is readily available to all reasoners within a system, these reasoning operations may need to evaluate context data collected from distributed sources and stored on different devices. This work proposes a middleware service for performing decentralized rule-based reasoning about context data targeting AmI systems in which we assume that there are two main interacting parties in the reasoning process, each having access to different context information.

Collaborative Explanation and Response in Assisted Living Environments Enhanced with Humanoid Robots

“…knowledge is distributed, but reasoning is local and agents do not exchange context information (Román et al, 2002); or are limited in their reasoning capabilities. For example, reasoning in (Viterbo and Endler, 2012) is distributed in that different computational nodes cooperate to infer a global context state; the reasoning process is however limited to ontology-based inference, and is not resilient to inaccurate or conflicting information. Finally, all existing knowledge-based reasoning approaches assume a single direction information flow: from sensor input, through to data analysis and decision-making, and then to reaction and control, irrespective of the extent to which each of these phases are decentralised (Snchez-Garzn et al, 2012;Valero et al, 2013).…”