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Language acquisition and machine learning
In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
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