Research Projects

Effective Bayesian Transfer Learning (EBTL)

*** THIS PROJECT IS NO LONGER ACTIVE ***

Stuart J. Russell, Peter Bartlett and Michael Jordan

Transfer learning is what happens when someone finds it much easier to learn to play chess having already learned to play checkers; or to recognize tables having already learned to recognize chairs; or to learn Spanish having already learned Italian. Achieving significant levels of transfer learning across tasks--that is, achieving cumulative learning--is the central problem facing machine learning. The EBTL project involves a technical unification of two previously disjoint areas of research: knowledge-intensive learning in the logical tradition and hierarchical Bayesian learning in the probabilistic tradition. The unification involves applying Bayesian learning methods with strong prior knowledge represented in an expressive first-order probabilistic language. The approach applies not just to learning declarative knowledge, but also to learning decision-related quantities such as reward functions, value functions, policies, and task hierarchies. This approach allows us to use the same powerful transfer methods to generalize across-task environments to other task instances with different initial states, objects, goals, and physical laws. Our theory of transfer learning is being tested on real-time strategy games and on simulated object manipulation and perception. EBTL subcontractors include MIT (Leslie Kaelbling, Tomas Lozano-Perez, and Tommi Jaakkola), Stanford (Andrew Ng, Daphne Koller, and Sebastian Thrun), and Oregon State (Tom Dietterich, Alan Fern, and Prasad Tadepalli).