An experiment of subconscious intelligent social computing on household appliances

Subconscious Social Intelligence refers to the design of social services oriented towards user problem solving, providing an underlying innovation layer is able to generate new solutions to yet unknown problems. The innovation layer is achieved by Computational Intelligence techniques, encompassing machine learning to build models of user satisfaction over solution quality, and stochastic search as the means for innovation generation. The SandS project provides an instance of such paradigm, where household appliances are the subject of the social service. This paper proposes a specific architecture, reporting results on a synthetic database build according to SandS project current designs. Database synthesis for system tuning and validation is a critical issue, hence the paper details the considerations guiding its design and generation, as well as the validation procedure ensuring the ecological validity of the innovation process simulation. The architecture is composed of a Support Vector Regression (SVR) module for user satisfaction modeling, and an Evolution Strategy (ES) achieving recipe innovation. The paper reports some computational experiments that may guide the real life implementation. The reported results are methodologically sound as far as they are independent of the generation process.