Ambient Intelligence — the Next Step for Artificial Intelligence

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. These environments should be aware of people's needs, customizing requirements and forecasting behaviors. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. Artificial intelligence research aims to include more intelligence in AmI environments, allowing better support for humans and access to the essential knowledge for making better decisions when interacting with these environments. This article, which introduces a special issue on AmI, views the area from an artificial intelligence perspective.info:eu-repo/semantics/publishedVersio

Swarm intelligence via the internet of things and the phenomenological turn

Considering the current advancements in biometric sensors and other related technologies, as well as the use of bio-inspired models for AI improvements, we can infer that the swarm intelligence paradigm can be implemented in human daily spheres through the connectivity between user gadgets connected to the Internet of Things. This is a first step towards a real Ambient Intelligence, but also of a Global Intelligence. This unconscious (by the user) connectivity may alter the way by which we feel the world. Besides, with the arrival of new augmented ways of capturing and providing information or radical new ways of expanding our bodies (through synthetic biology or artificial prosthesis like brain-computer connections), we can be very close to a change which may radically affect our experience of ourselves and of the feeling of collectivity. We call it the techno-phenomenological turn. We show social implications, present challenges, and and open questions for the new kind of swarm intelligence-enhanced society, and provide the taxonomy of the field of study. We will also explore the possible roadmaps of this next possible situation

MODERN APPROACES IN THE CONTEXT OF AMBIENT INTELLIGENCE

Ambient Intelligence (AmI), as a new vision and concept of the tomorrow, gathers a few features regarding both the integration of technology in the environment and the capacity technology has to recognize the user and its context, the system capacity to iAmbient Intelligence (AmI), ubiquitous computing, scenario, artificial intelligence (AI)

Flow for Meta Control

The psychological state of flow has been linked to optimizing human performance. A key condition of flow emergence is a match between the human abilities and complexity of the task. We propose a simple computational model of flow for Artificial Intelligence (AI) agents. The model factors the standard agent-environment state into a self-reflective set of the agent's abilities and a socially learned set of the environmental complexity. Maximizing the flow serves as a meta control for the agent. We show how to apply the meta-control policy to a broad class of AI control policies and illustrate our approach with a specific implementation. Results in a synthetic testbed are promising and open interesting directions for future work

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules