A Reasoning Hardware Platform for Real-Time Common-Sense Inference

Abstract

Enabling Ambient Intelligence systems to understand the activities that are taking place in a supervised context is a rather complicated task. Moreover, this task cannot be successfully addressed while overlooking the mechanisms (common-sense knowledge and reasoning) that entitle us, as humans beings, to successfully undertake it. This work is based on the premise that Ambient Intelligence systems will be able to understand and react to context events if common-sense capabilities are embodied in them. However, there are some difficulties that need to be resolved before common-sense capabilities can be fully deployed to Ambient Intelligence. This work presents a hardware accelerated implementation of a common-sense knowledge-base system intended to improve response time and efficiency.

Semantic Framework for Social Robot Self-Configuration

“…For the same reason, the restrictions of this type of inference still apply. For instance, other types of reasoning [11] may not be applied and it would be interesting to take into account the context uncertainty and vagueness [12] so as to better model the environment.…”