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 20 of 22 for “"Neuro-Symbolic"”.
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Neuro-symbolic fact verification
… To address these issues, this thesis explores neuro-symbolic methods for fact verification, which integrate symbolic systems with neural representations. We focus in particular on natural logic, a framework of compositional entailment which operates directly on natural language by capturing the …
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Neuro-Symbolic Learning for Bilevel Robot Planning
Decision-making in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning, where decision-making is decomposed into reasoning about “what to do” (task planning) and “how to do it” …
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Learning Neuro-Symbolic Skills for Bilevel Planning
… In a setting where expert demonstrations, symbolic predicates for state abstraction, and manually designed parameterized policies are given, prior work has shown how to learn symbolic operators and neural samplers for TAMP. But Manually designing parameterized policies can be difficult and …
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Feature-Based Neuro-Symbolic Networks for Global Diagnostics
… of momentum) to create a feature-based neuro-symbolic network. This network is very similar to a neural network, except that it is based on a physical equation, and it uses features instead of raw data. Results from this network identify patterns of behavior that display whether the …
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Towards a neuro-symbolic approach to moral judgment
… evaluate chatGPT’s success, and build towards a neuro-symbolic framework to improve upon this baseline. By investigating one problem in depth, we hope to uncover nuances, intricacies, and details that might be overlooked in a broader exploration. Our insights intend to spark curiosity, rather …
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Neuro-Symbolic Integration in Artificial Intelligence and its Applications
L'abstract è presente nell'allegato / the abstract is in the attachment
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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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Neuro-Symbolic Methods for Natural Language Inference and Question Answering
… neural network models to incorporate logic and symbolic operations. Although deep neural network models have achieved state-of-the-art performance on multiple natural language processing benchmarks, those black-box models can hardly provide explanations for their inner mechanisms. They still …
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NEURO-SYMBOLIC AI APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION
… in the general machine learning community, Neuro-Symbolic AI (NeSy) methods are emerging to combine DL models with more traditional symbolic AI techniques that rely on knowledge-based reasoning to improve models' interpretability while reducing their reliance on labeled data during training. …
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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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Reinforcement Learning–Based Discrete Prompt Optimization for Neuro-Symbolic Structured Simplification of Complex Game Descriptions with Large Language Models
… prompt optimization problem and introduces a neuro-symbolic pipeline that maps raw natural language into controlled GameChangineer sentences via scenario normalization, retrieval-augmented code generation, and AST-based FACTS extraction. A reinforcement learning framework based on Proximal …
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Augmenting Transformers for Open Domain Procedural Text Comprehesion
… the task of procedural text comprehension using neuro-symbolic techniques. We use this task as a testbed for exploring the limitations of state-of-the-art systems such as GPT on the task of predicting the resulting state changes from the text description of a procedure. We also experiment with …
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Combining Diverse Forms of Human and Machine Intelligence
… combinations of three forms of intelligence: symbolic artificial intelligence, neural artificial intelligence, and human intelligence. First, diverse forms of Neuro-Symbolic AI through three pipelines consisting respectively of neural perception with symbolic reasoning, symbolic inputs with …
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PDDL.jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning
… (PDDL) is a formal specification language for symbolic planning problems and domains that is widely used by the AI planning community. However, most implementations of PDDL are closely tied to particular planning systems and algorithms, and are not designed for interoperability or modular use …
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Learning, Reasoning, and Planning with Relational and Temporal Neural Networks
… ourselves. This thesis gives an overview of a neuro-symbolic framework for learning, reasoning, and planning with relational and temporal neural networks. The key idea is to exploit a structural bias in neural network learning that enables us to describe complex relational-temporal events and …
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Program Synthesis for Quantum Applications
… and hardware.Finally, we propose NuQes, a neuro-symbolic quantum error correction (QEC) code synthesisframework that leverages heuristic functions generated by large language models (LLMs) tooptimize QEC code design. Together, these frameworks advance quantum program synthesis byimproving …
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Learning State and Action Abstractions for Effective and Efficient Planning
… Throughout the chapters, we show how to learn neuro-symbolic abstractions for bilevel planning; present a method for learning to generate context-specific abstractions of Markov decision processes; formalize and give a tractable algorithm for reasoning efficiently about relevant exogenous …
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COMPOSITIONAL GENERALIZATION IN INSTRUCTION FOLLOWING TASKS
… augmentation, to auxiliary tasks, to a simple neuro-symbolic algorithm. We present a compositional spatial representation language and discuss how using such a rich symbolic representation as auxiliary supervision can help generalization in complex, real-world, multi-modal instruction following …
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Transforming Free-Form Sentences into Sequence of Unambiguous Sentences with Large Language Model
… Next, the thesis also presents "IntentGuide," a neuro-symbolic integration framework to enhance the clarity and executability of human intentions expressed in freeform sentences. IntentGuide effectively integrates the rule-based error detection capabilities of symbolic AI with the powerful …
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Towards Interpretable, Equitable, and Safe Language Model Applications in Healthcare and Beyond
… healthcare settings and propose modularized, neuro-symbolic dialogue architectures for health coaching, maintaining interpretability and control while requiring minimal annotation. Having established interpretable architectures, we examine whether LLMs can be trusted to perform equitably …
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