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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Neuro-Symbolic Distillation of Reinforcement Learning Agents

Abir, Farhan Fuad 01 January 2024 (has links) (PDF)
In the past decade, reinforcement learning (RL) has achieved breakthroughs across various domains, from surpassing human performance in strategy games to enhancing the training of large language models (LLMs) with human feedback. However, RL has yet to gain widespread adoption in mission-critical fields such as healthcare and autonomous vehicles. This is primarily attributed to the inherent lack of trust, explainability, and generalizability of neural networks in deep reinforcement learning (DRL) agents. While neural DRL agents leverage the power of neural networks to solve specific tasks robustly and efficiently, this often comes at the cost of explainability and generalizability. In contrast, pure symbolic agents maintain explainability and trust but often underperform in high-dimensional data. In this work, we developed a method to distill explainable and trustworthy agents using neuro-symbolic AI. Neuro-symbolic distillation combines the strengths of symbolic reasoning and neural networks, creating a hybrid framework that leverages the structured knowledge representation of symbolic systems alongside the learning capabilities of neural networks. The key steps of neuro-symbolic distillation involve training traditional DRL agents, followed by extracting, selecting, and distilling their learned policies into symbolic forms using symbolic regression and tree-based models. These symbolic representations are then employed instead of the neural agents to make interpretable decisions with comparable accuracy. The approach is validated through experiments on Lunar Lander and Pong, demonstrating that symbolic representations can effectively replace neural agents while enhancing transparency and trustworthiness. Our findings suggest that this approach mitigates the black-box nature of neural networks, providing a pathway toward more transparent and trustworthy AI systems. The implications of this research are significant for fields requiring both high performance and explainability, such as autonomous systems, healthcare, and financial modeling.
2

Can I open it? : Robot Affordance Inference using a Probabilistic Reasoning Approach

Aguirregomezcorta Aina, Jorge January 2024 (has links)
Modern autonomous systems should be able to interact with their surroundings in a flexible yet safe manner. To guarantee this behavior, such systems must learn how to approach unseen entities in their environment through the inference of relationships between actions and objects, called affordances. This research project introduces a neuro-symbolic AI system capable of inferring affordances using attribute detection and knowledge representation as its core principles. The attribute detection module employs a visuo-lingual image captioning model to extract the key object attributes of a scene, while the cognitive knowledge module infers the affordances of those attributes using conditional probability. The practical capabilities of the neuro-symbolic AI system are assessed by implementing a simulated robot system that interacts within the problem space of jars and bottles. The neuro-symbolic AI system is evaluated through its caption-inferring capabilities using image captioning and machine translation metrics. The scores registered in the evaluation show a successful attribute captioning rate of more than 71%. The robot simulation is evaluated within a Unity virtual environment by interacting with 50 jars and bottles, equally divided between lifting and twisting affordances. The robot system successfully interacts with all the objects in the scene due to the robustness of the architecture but fails in the inference process 24 out of the 50 iterations. Contrary to previous works approaching the problem as a classification task, this study shows that affordance inference can be successfully implemented using a cognitive visuo-lingual method. The study’s results justify further study into the use of neuro-symbolic AI approaches to affordance inference.

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