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Learning, Planning and Representing Knowledge from Primitive Experience

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Abstract: Sequential decision-making and planning problems are ubiquitous in engineering, economics, finance, artificial intelligence and robotics. Many of the hardest and most important problems involve long-term consequences of immediate choices, and therefore vast search-spaces. This talk proposes a new paradigm for general-purpose planning and decision-making that is appropriate for these large and challenging problems. The inspiration for this approach comes from the game of Go. This task is widely viewed as a grand challenge for artificial intelligence, which has thwarted traditional approaches to knowledge representation, learning and planning. Recently, a radically different approach has been developed, resulting in the first program, MoGo, to perform at human master level. This revolution in computer Go is based on three key principles: a) knowledge is represented by predictions about future experience, b) predictions are learnt directly from experience, c) planning is achieved by simulating experience. This talk proposes a more general application of this experience-based strategy. Just like Go, many real-world planning and decision-making problems have enormous search-spaces that are intractable to traditional search algorithms. Furthermore, also just like Go, in the majority of these problems, expert knowledge is unavailable or unreliable.

This talk is part of the Microsoft Research Cambridge, public talks series.

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