Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
An ambient intelligent environment is definitely a prerequisite for anticipating the needs and catching the attention of systems. But how to endow such an environment with natural anticipatory and attentive features is still a hardly ever properly addressed question. Before providing a roadmap towards such an ambient intelligent environment we first give cognitive-ergonomic accounts for how natural anticipation and selection of attention (NASA) emerge in living organisms. In particular, we describe why, when and how exploratory and goal-directed acts by living organisms are controlled while optimizing their changing and limited structural and functional capabilities of multimodal sensor, cognitive and actuator systems. Next, we describe how NASA can be embedded and embodied in sustainable intelligent multimodal systems (SIMS). Such systems allow an ambient intelligent environment to (self-) interact taking its contexts into account. In addition, collective intelligent agents (CIA) distribute, store, extend, maintain, optimize, diversify and sustain the NASA embedded and embodied in the ambient intelligent environment. Finally, we present the basic ingredients of a mathematical-physical framework for empirically modeling and sustaining NASA within SIMS by CIA in an ambient intelligent environment. An environment which is modeled this way, robustly and reliably over time aligns multi-sensor detection and fusion; multimodal fusion, dialogue planning and fission; multi actuator fission, rendering and presentation schemes. NASA residing in such an environment are then active within every phase of perception-decision-action cycles, and are gauged and renormalized to its physics. After determining and assessing across several evolutionary dynamic scales appropriate fitness, utility and measures, NASA can be realized by reinforcement learning and self-organization.
