We survey and outline how an
agent-centered,
information-theoretic approach to meaningful
information extending classical Shannon
information
theory by means of utility
measures relevant for the goals of particular agents can be applied
to sensor evolution for real and constructed organisms.
Furthermore, we discuss the relationship of this approach to the
programme of freeing artificial life and robotic systems from
reactivity, by describing useful types of
information with
broader temporal horizon, for signaling, communication, affective
grounding, two-process learning, individual learning, imitation and
social learning, and episodic experiential information (memories,
narrative, and culturally transmitted information).