Early playgrounds were more about fitness than fun--and children didn't enter the equation for decades. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Playgrounds have never been just fun and games. Breakthroughs, discoveries, and DIY tips sent six days a week. You can learn a lot about a society from the way they raise children.

We introduce a novel mechanism for knowledge transfer via concept formation to augment reinforcement learning agents operating in complex, uncertain domains. Based on their observations, agents form concepts and associate them with actions to generalize their decisions at higher levels of abstraction. Concepts serve as simple, portable, efficient packets of hierarchical information that can be learned in parallel. The use of conceptual knowledge simultaneously provides an interpretable, semantic explanation of an agent's decisions, making the techniques promising for human-interaction domains such as games, where human observers wish to inspect an agent's rationale. This technique extends previous work on probabilistic learning with Markov decision processes (MDPs) by introducing rich hierarchical feature structures that can be learned from experience, enabling more effective learning transfer to new, related tasks.