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Big Data Cogn. Comput. 2018, 2(3), 23; https://doi.org/10.3390/bdcc2030023

LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning

1
Dipartimento di Informatica–Scienza e Ingegneria (DISI) Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy
2
Dipartimento di Informatica–Scienza e Ingegneria (DISI) Alma Mater Studiorum–Università di Bologna, 47521 Cesena, Italy
3
Dipartimento di Scienze e Metodi dell’Ingegneria (DSMI) Università degli Studi di Modena e Reggio Emilia, 42122 Modena, Italy
*
Author to whom correspondence should be addressed.
Received: 15 July 2018 / Revised: 27 July 2018 / Accepted: 31 July 2018 / Published: 3 August 2018
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Abstract

In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in order to achieve key properties such as observability, explainability, and accountability. In this paper we focus on logic programming (LP), and advocate its role as a provider of symbolic reasoning capabilities in IoT scenarios, suitably complementing non-symbolic ones. In particular, we show how its re-interpretation in terms of LPaaS (Logic Programming as a Service) can work as an enabling technology for distributed situated intelligence. A possible example of hybrid reasoning—where symbolic and non-symbolic techniques fruitfully combine to produce intelligent behaviour—is presented, demonstrating how LPaaS could work in a smart energy grid scenario. View Full-Text
Keywords: Logic Programming as a Service; IoT; symbolic reasoning Logic Programming as a Service; IoT; symbolic reasoning
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Calegari, R.; Ciatto, G.; Mariani, S.; Denti, E.; Omicini, A. LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning. Big Data Cogn. Comput. 2018, 2, 23.

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