Ushered by competition and technological change, a growing number of human activities are situated in environments requiring situation recognition and dynamic response to unpredicted events. This chapter presents the vision and structure of a novel computational model, called Bidirectional Responsive IoT Collaboration and Knowledge System (BRICKS), meant to deal with adaptable and open IoT systems to recognize contingencies and autonomously reconfigure their processes to achieve collaborative goals. A key innovation of BRICKS is the harmonization of two traditionally antithetic views of modern IoT systems: the top-down, process-driven view, in which user-defined processes promote the composition of preexisting blocks, facilitating well-engineered, top-down design and control at the expense of openness; and the bottom-up, self-organizing view, in which components autonomously learn new capabilities and causal relations, leading to their integration at the expense of control. These approaches coexist in BRICKS and lead to augmented processes.
Pianini, D., Re, B., Rossi, L., Zambonelli, F. (2024). Envisioning Unpredictability in Smart Environments. - : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-62146-8_9].
Envisioning Unpredictability in Smart Environments
Pianini D.;Zambonelli F.
2024
Abstract
Ushered by competition and technological change, a growing number of human activities are situated in environments requiring situation recognition and dynamic response to unpredicted events. This chapter presents the vision and structure of a novel computational model, called Bidirectional Responsive IoT Collaboration and Knowledge System (BRICKS), meant to deal with adaptable and open IoT systems to recognize contingencies and autonomously reconfigure their processes to achieve collaborative goals. A key innovation of BRICKS is the harmonization of two traditionally antithetic views of modern IoT systems: the top-down, process-driven view, in which user-defined processes promote the composition of preexisting blocks, facilitating well-engineered, top-down design and control at the expense of openness; and the bottom-up, self-organizing view, in which components autonomously learn new capabilities and causal relations, leading to their integration at the expense of control. These approaches coexist in BRICKS and lead to augmented processes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.