VARIABLES
Curiosity rewards, Intrinsic rewards
DOMAINS: Artificial intelligence
Contributors: Patricia McKenna
DEVELOPERS
Jürgen Schmidhuber (early 1990s)
BACKGROUND
Artificial curiosity theory - "believes algorithms can be written that allow the programming of curiosity itself. What's interesting? Many interesting things are unexpected, but not all unexpected things are interesting or surprising. According to Schmidhuber's formal theory of surprise & novelty & interestingness & attention & creativity & intrinsic motivation, curious agents are interested in learnable but yet unknown regularities, and get bored by both predictable and inherently unpredictable things. His active reinforcement learners translate mismatches between expectations and reality into curiosity rewards or intrinsic rewards for curious, creative, exploring agents which like to observe / create truly surprising aspects of the world, to learn novel patterns".
Singularity Summit "in our research, virtual and real worlds actually complement each other. We use machine learning and artificial curiosity to learn or improve simulations of the real world, then train the robot in the sim to achieve desirable goals". "Fundamental Principle of Artificial Curiosity and Creativity: Reward the reward- optimizing controller for actions yielding data that cause improvements of the adaptive predictor or data compressor! (Formulated in the early 1990s; basis of much of the recent work in Developmental Robotics since 2004) "
Relationship to Other Theories:
REFERENCES ~ Coding Spreadsheet - Web View