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Learning decomposition models for hierarchical planning and plan recognition
Dissertation   Open access

Learning decomposition models for hierarchical planning and plan recognition

Pavan Kantharaju
Doctor of Philosophy (Ph.D.), Drexel University
Nov 2020
DOI:
https://doi.org/10.17918/00000386
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Abstract

Combinatory logic Machine Learning
Classical AI planning aims to solve the problem of searching for a sequence of steps that transitions an agent from some state of the world to some goal. Hierarchical planning is a particular case of AI planning that assumes the existence of a decomposition model that represents how to break down tasks or goals into sequences of actions. While hierarchical planning allows us to reduce the search space compared to classical planning, it has been shown that hand authoring these decomposition models require domain-expert knowledge and time, and can be error-prone for complex application domains. This is also the case for hierarchical plan recognition, which aims to solve the problem of inferring the goals and plans of agent(s) given a sequence of actions and a decomposition model. Our goal is to study learning techniques for automatically constructing decomposition models from training data for hierarchical planning and plan recognition. Specifically, we focus on learning a decomposition model represented using Combinatory Categorial Grammars (CCGs). This dissertation describes a study on supervised and unsupervised CCG learning techniques for hierarchical planning and plan recognition, a novel CCG plan recognition algorithm, a theoretical analysis of supervised CCG learning algorithms, and the application of learned CCGs for playing computer games. In this dissertation, we provide a background of relevant research to the dissertation, describe our study on CCG learning, and outline future steps.

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