A Machine Consciousness architecture based on Deep Learning and Gaussian Processes

Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achieved complex tasks such as generating art pictures or literature automatically. On the other hand, symbolic resources have also been developed further and behave well in problems such as the ones proposed by common sense reasoning. Machine Consciousness is a field that has been deeply studied and several theories based in the functionalism philosophical theory like the global workspace theory or information integration have been proposed that try to explain the ariseness of consciousness in machines. In this work, we propose an architecture that may arise consciousness in a machine based in the global workspace theory and in the assumption that consciousness appear in machines that has cognitive processes and exhibit conscious behaviour. This architecture is based in processes that use the recent developments in artificial intelligence models which output are these correlated activities. For every one of the modules of this architecture, we provide detailed explanations of the models involved and how they communicate with each other to create the cognitive architecture.

Survey of Bayesian Networks Applications on Unmanned Intelligent Autonomous Vehicles

This article review the applications of Bayesian networks on Unmanned Intelligent Autonomous Vehicles (UIAV) from the decision making point of view, which represents the final step for fully autonomous unmanned vehicles (currently under discussion). Until now when it comes to make high level decisions for unmanned autonomous vehicles (UAV) the humans have the last word. Based on the works exposed in this article and current analysis, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian networks as a promising computer model for decision making. Remains for the future to test Bayesian networks models in real situations. Besides the applications, Bayesian networks fundaments are introduced as elements to consider when we try to develop (UIAVs) with the potential of achieving high level judgements.