Attention has represented a core scienti?c topic in the design of AI-enabled systems in the
last few decades. Today in the ongoing debate design and c-
putationalmodelingofarti?cialcognitivesystems attentionhasgainedacentral position as a focus of
research. For instance attentional methods are considered in investigating the interfacing of
sensory and cognitive information processing for the organization of behaviors and for the
understanding of individual and social cognition in infant development.
Whilevisualcognitionplaysacentralroleinhumanperception ?ndingsfrom neuroscience and
experimental psychology have provided strong evidence about the perception-action nature of
cognition. The embodied nature of senso- motor intelligence requires a continuous and focused
interplay between the c- trolofmotoractivitiesandtheinterpretationoffeedbackfromperceptualmod-
ities. Decision making about the selection of information from the incoming sensory stream - in
tune with contextual processing on a current task and an agent's global objectives - becomes a
further challenging issue in attentional control. Attention must operate at interfaces between
a bottom-up-driven world interpretationandtop-down-driveninformationselection
thusactingatthecore of arti?cial cognitive systems. These insights have already induced changes
in AI-related disciplines such as the design of behavior-based robot control and the
computational modeling of animats. Today the development of enabling technologiessuch as
autonomous robotic systems miniaturizedmobile-evenwearable-sensors andambientintelligence
systems involves the real-time analysis of enormous quantities of data. These data have to be
processed in an intelligent way to provide on time delivery of the required relevant
information. Knowledge has to be applied about what needs to be attended to and when and what
to do in a meaningful sequence in correspondence with visual feedback.
