Bounded Recursive Self-Improvement
  We have designed a machine that becomes increasingly better at behaving in
underspecified circumstances, in a goal-directed way, on the job, by modeling
itself and its environment as experience accumulates. Based on principles of
autocatalysis, endogeny, and reflectivity, the work provides an architectural
blueprint for constructing systems with high levels of operational autonomy in
underspecified circumstances, starting from a small seed. Through value-driven
dynamic priority scheduling controlling the parallel execution of a vast number
of reasoning threads, the system achieves recursive self-improvement after it
leaves the lab, within the boundaries imposed by its designers. A prototype
system has been implemented and demonstrated to learn a complex real-world
task, real-time multimodal dialogue with humans, by on-line observation. Our
work presents solutions to several challenges that must be solved for achieving
artificial general intelligence.
