MABLE: a framework for learning from natural instruction

• Authors: Roger Mailler;Daniel Bryce;Jiaying Shen;Ciaran O'Reilly
• Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
• Year: 2009

The Modular Architecture for Bootstrapped Learning Experiments (MABLE) is a system that is being developed to allow humans to teach computers in the most natural manner possible: by using combinations of descriptions, demonstrations, and feedback. MABLE is a highly modular, well-engineered, and extendable system that provides generalized services, such as control, knowledge representation, and execution management. MABLE works by accepting instruction from a teacher and forms concrete learning tasks that are fed to state-of-the-art machine learning algorithms. To make the learning tractable, specialized heuristics, in the form of learning strategies, are used to derive bias from the instruction. The output of the learning is then incorporated into the system's background knowledge to be used in performing tasks or as the basis for simplifying the process of learning difficult concepts. Although still in development, MABLE has already demonstrated the ability to learn four different types of knowledge (definitions, rules, functions, and procedures) from three different modes of student/teacher interaction on two separate, qualitatively different domains. MABLE presents a unique opportunity for machine learning researchers to easily plug in and test algorithms in the context of instructible computing. In the near future, MABLE will be freely available as an open source project.