Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"symbolic learning"”.
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Neuro-Symbolic Learning for Bilevel Robot Planning
… thesis proposes the first unified system for learning all the abstractions needed for bilevel planning. Beyond learning to make planning possible, this thesis also considers learning to make planning fast, especially in environments with many objects. A final contribution considers planning to …
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A Neuro-Symbolic Reinforcement Learning Architecture: Integrating Perception, Reasoning, and Control
In recent years, neuro-symbolic learning methods have demonstrated promise in tasks re- quiring a semantic understanding that can often be missed by traditional deep learning techniques. By integrating symbolic reasoning with deep learning, neuro-symbolic architec- tures aim to be both …
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Incorporating the simplicity first methodology into a machine learning genetic algorithm
… powerful yet domain-independent tool for concept learning. However, in general, learning systems based on the genetic algorithm generally do not perform as well as symbolic learning algorithms. Robert Holte's symbolic learning algorithm 1R demonstrated that simple rules can perform well in …
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HyperSketch : Language for Implementing Generic Neuro-Symbolic Program Synthesizers
Recent developments in neuro-symbolic learning, including program synthesis and deep learning have surprised in the rate of growth of scale, scope, and variety of models used and problems solved. However, existing frameworks are usually specialized for one style of model learning / synthesis and …
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A symbol's role in learning low-level control functions.
This thesis demonstrates how the power of symbolic processing can be exploited in the learning of low level control functions. It proposes a novel hybrid architecture with a tight coupling between a variant of symbolic planning and reinforcement learning. This architecture combines the strengths of …
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Symbolic and connectionist machine learning techniques for short-term electric load forecasting
This work applies connectionist neural network learning techniques and symbolic machine learning techniques to the problem of short-term electric load forecasting. The short-term electric load forecasting problem considered here is the prediction of bus loads one day ahead. The forecast quantities …
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Learning symbolic concepts and domain-specific languages
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms