Pure scientists do not only invent new methods to solve given problems. They also invent new problems. The recent POWERPLAY framework formalizes this type of curiosity and creativity in a new, general, yet practical way. To acquire problem solving prowess through playing, POWERPLAY-based artificial explorers by design continually come up with the fastest to find, initially novel, but eventually solvable problems. They also continually simplify or speed up solutions to previous problems. We report on results of first experiments with POWERPLAY. A self-delimiting recurrent neural network (SLIM RNN) is used as a general computational architecture to implement the system's solver. Its weights can encode arbitrary, self-delimiting, halting or non-halting programs affecting both environment (through effectors) and internal states encoding abstractions of event sequences. In open-ended fashion, our POWERPLAY-driven RNNs learn to become increasingly general problem solvers, continually adding new problem solving procedures to the growing repertoire, exhibiting interesting developmental stages. © 2012 IEEE.
|Original language||English (US)|
|Title of host publication||2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012|
|State||Published - Dec 1 2012|