Real-Time GA-Based Probabilistic Programming in Application to Robot Control

Abstract

Abstract. Possibility to solve the problem of planning and plan recovery for robots using probabilistic programming with optimization queries, which is being developed as a framework for AGI and cognitive architectures, is considered. Planning can be done directly by introducing a generative model for plans and optimizing an objective function calculated via plan simulation. Plan recovery is achieved almost without modifying optimization queries. These queries are simply executed in parallel with plan execution by a robot meaning that they continuously optimize dynamically varying objective functions tracking their optima. Experiments with the NAO robot showed that replanning can be naturally done within this approach without developing special plan recovery methods.

The Foundations of Deep Learning with a Path Towards General Intelligence

“…These reasonable and desirable properties of a complete AI system lead naturally to a top-down design sometimes called an AGI Unification Architecture among practitioners, if built around the floor plan of a universal induction system such as AIXI. An example of such an approach to designing a cognitive architecture may be seen in [36]. However, this is not necessarily the only kind of solution.…”