Mathematical reasoning is one of the crucial abilities general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus interpretable) in terms what has been learned applied process. In this paper, we propose a Learning by Applying (LeAp) framework enhance models (backbones) principl...

There is evidence that sleep supports the enhancement of implicit as well as explicit memories (i.e., two memory systems that during learning normally appear to act together). Here, employing a serial reaction time task (SRTT) paradigm, we examined the question whether sleep can provide explicit knowledge on an implicitly acquired skill. At learning, young healthy subjects (n = 20) were first t...

The development of our team for RoboCup '99 is mainly oriented towards a transparent way of transferring explicit knowledge into the agent control and its combination with learning algorithms capable of ne-tuning the acquired skills. The explicit knowledge is formulated in terms of rules, the non-explicit knowledge is to be realized as a set of parameters adapted by hierarchical reinforcement l...

Deliberative experimental learning is an approach for learning explicit game strategies in a small number of trials by posting and experimentally satisfying learning goals. Learning explicit strategies is important for producing knowledge that can easily be transferred via analogy to new games, as well as for rapid learning. In our approach, experiments, or plans for learning, serve to drive bo...