This is the 1st-year of a 3-year continuing award. The objective of this research is to widen the currently narrow `information pipeline` between humans and machine learners; instead of solely providing labeled training examples to the learner as is typically done, the algorithms being designed also allow humans to provide broadly applicable advice regarding the task at hand. The human advice-giver does not have to understand the machine learner's internal representations and algorithms, nor does the provided advice have to be completely correct or fully explicit. The algorithms being developed use techniques from connectionist reinforcement learning to (slowly) learn from the feedback provided by the learner's environment. The advice-giver observes the machine learner's decision making and occasionally makes suggestions, expressed as instructions in a simple programming language. Based on techniques from the field of knowledge-based neural networks, the advice is inserted directly into the agent's knowledge base. Importantly, the learner can subsequently refine the assimilated advice both by neural training based on further experience and additional advice from the human advisor. This work promises to simplify the interaction between humans and machine learners, and may lead to flexible, adaptive software that tunes itself to specific users via this project's flexible advice-giving framework.
