Goal-Driven Learning (GDL) views learning as a strategic process in which the learner attempts to identify and satisfy its learning needs in the context of its tasks and goals. This is modeled as a planful process where the learner analyzes its reasoning traces to identify learning goals, and composes a set of learning strategies (modeled as planning operators) into a plan to learn by satisfying those learning goals.
Traditional GDL frameworks were based on traditional planners. However, modern AI systems often deal with real-time scenarios where learning and performance happen in a reactive real-time fashion, or are composed of multiple agents that use different learning and reasoning paradigms. In this talk, I discuss new GDL frameworks that handle such problems, incorporating reactive and multi-agent planning techniques in order to manage learning in these kinds of AI systems.
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