LPaaS as Micro-intelligence: Enhancing IoT with Symbolic Reasoning
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, still symbolic approaches to machine intelligence 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.
@article{lpaas-bdcc2,
Articleno = 23,
Author = {Calegari, Roberta and Ciatto, Giovanni and Mariani, Stefano and Denti, Enrico and Omicini, Andrea},
Doi = {10.3390/bdcc2030023},
IrisId = {11585/640012},
Issn-Online = {2504-2289},
Journal = {Big Data and Cognitive Computing},
Keywords = {Logic Programming as a Service, IoT, symbolic reasoning},
Number = 3,
Numpages = 26,
Publisher = {MDPI},
Title = {{LPaaS} as Micro-intelligence: Enhancing {IoT} with Symbolic Reasoning},
Url = {http://www.mdpi.com/2504-2289/2/3/23},
Url-Pdf = {http://www.mdpi.com/2504-2289/2/3/23/pdf},
Volume = 2,
Year = 2018}