Micro-Intelligence for the IoT: LP models and technologies

By Roberta Calegari,
Università di Bologna


Computing is moving towards 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, negotiating for services, acting on our behalf, and delivering services in an anywhere any time fashion.
An important next step for pervasive computing is the integration of intelligent agents that employ knowledge and reasoning to understand the local context and share this information in support of intelligent applications and interfaces. Such scenarios, characterised by “computation is everywhere around us”, require on the one hand software components with intelligent behaviour in terms of objectives and context, and on the other their integration so as to produce social intelligence.
Since its inception, Logic Programming (LP) has been recognised as a natural paradigm for addressing the needs of distributed intelligence. Yet, the development of novel architectures, in particular in the context Internet of Things (IoT), and the emergence of new domains and potential applications, are creating new research opportunities where LP could be exploited, when suitably coupled with agent technologies and methods so that it can fully develop its potential in the new context. In particular, the LP and its extensions can act as micro-intelligence sources for the IoT world, both at the individual and the social level, provided that they are reconsidered in a renewed architectural vision. Such micro-intelligence sources could deal with the local knowledge of the devices taking into account the domain specificity of each environment.
The goal of this thesis is to re-contextualise LP and its extensions in these new domains as a source of micro-intelligence for the IoT world, envisioning a large number of small computational units distributed and situated in the environment, thus promoting the local exploitation of symbolic languages with inference capabilities. The topic is explored in depth and the effectiveness of novel LP models and architectures –and of the corresponding technology– expressing the concept of micro intelligence is tested. In particular, two different, integrated models are presented, namely Logic Programming as a Service (LPaaS) and Labelled Variables in Logic Programming (LVLP) designed so as to act synergistically in order to support the distribution of intelligence in pervasive systems