Artificial Attention at Scale

Human-machine systems have expanded in terms of their sensing, communication, and computational capabilities. These capabilities have led to developments of a variety of sensor systems, like robotic platforms. There are benefits to these new sensor systems, however, these benefits have been offset by new difficulties; dynamic data overload, keeping pace with changing tempo, and managing data flows from multiple sensors feeds. One approach to manage data overload from multiple sensor feeds are computational models of attention. These models also address an important aspect of human-machine symbiosis, the need for machines agents to understand attention, manage interaction based on the flow of attention, and anticipate the flow of attention in the future. Unfortunately, existing computational models of attention use assumptions that limit their applicability to human-machine systems. The Artificial Attention Architecture is introduced and demonstrates how computational models of attention can be extended to handle multi-agent, multi-sensor systems. The Artificial Attention Architecture addresses important properties of human-machine systems like the need to build symbiosis between people searching for meaning in extensive data flows and the computational algorithms processing complex and dynamic data flows.