  This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" (both outlined in the article) may help to solve
computer-related problems in the design of autonomous robots, meaning robots
that do not depend on external intelligence or power supplies, are mobile, and
are designed to exhibit as much human-like intelligence as possible. The
article is about: how to increase the computational and energy efficiency of
computers and reduce their bulk; how to achieve human-like versatility in
intelligence; and likewise for human-like adaptability in intelligence. The SP
system has potential for substantial gains in computational and energy
efficiency and reductions in the bulkiness of computers: by reducing the size
of data to be processed; by exploiting statistical information that the system
gathers; and via an updated version of Donald Hebb's concept of a "cell
assembly". Towards human-like versatility in intelligence, the SP system has
strengths in unsupervised learning, natural language processing, pattern
recognition, information retrieval, several kinds of reasoning, planning,
problem solving, and more, with seamless integration amongst structures and
functions. The SP system's strengths in unsupervised learning and other aspects
of intelligence may help to achieve human-like adaptability in intelligence
via: the learning of natural language; learning to see; building 3D models of
objects and of a robot's surroundings; learning regularities in the workings of
a robot and in the robot's environment; exploration and play; learning major
skills; and secondary forms of learning. Also discussed are: how the SP system
may process parallel streams of information; generalisation of knowledge,
correction of over-generalisations, and learning from dirty data; how to cut
the cost of learning; and reinforcements, motivations, goals, and
demonstration.
