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EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning


Core Concepts
EXPLORER is a neuro-symbolic agent that combines neural exploration with symbolic exploitation to excel in text-based games by learning interpretable policies and achieving better generalization over unseen entities.
Abstract
In the paper "EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning," the authors introduce a neuro-symbolic agent designed for text-based games. The agent, EXPLORER, utilizes both neural and symbolic modules to enhance performance in tasks requiring natural language understanding and reinforcement learning. By combining exploration and exploitation, EXPLORER learns generalized symbolic policies that outperform baseline agents on Text-World cooking and commonsense games. The paper highlights the importance of non-monotonic reasoning in partially observable worlds and demonstrates how default theories can be learned with exceptions for text-based games. Additionally, a novel information-gain based rule generalization algorithm leveraging WordNet is introduced to improve policy generalization over unseen entities.
Stats
Code available at: https://github.com/kinjalbasu/explorer TW-Cooking domain levels 1-4 from GATA dataset used for testing. TWC games with easy, medium, and hard difficulty levels generated for evaluation. Performance comparison results provided for IN distribution (same entities as training) and OUT distribution (new entities) games.
Quotes
"EXPLORER presents a scalable design integrating any neural module with a symbolic module." "Our experiments show that EXPLORER outperforms baseline agents on Text-World cooking and Text-World Commonsense games." "Due to its neuro-symbolic nature, EXPLORER achieves better generalization over unseen entities."

Key Insights Distilled From

by Kinjal Basu,... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10692.pdf
EXPLORER

Deeper Inquiries

How does the incorporation of commonsense knowledge impact the performance of neuro-symbolic agents like EXPLORER

Incorporating commonsense knowledge has a significant impact on the performance of neuro-symbolic agents like EXPLORER. By leveraging commonsense knowledge, these agents can make more informed decisions and predictions based on general principles and reasoning that go beyond the specific data they have been trained on. This allows them to generalize better to unseen scenarios, handle ambiguous situations, and make more contextually appropriate choices. In the case of EXPLORER, integrating commonsense knowledge helps in rule generalization, exception learning, and overall decision-making processes. It enables the agent to reason about entities or objects not explicitly encountered during training by relating them to known concepts through semantic relationships.

What are the potential implications of using a hybrid neuro-symbolic approach in other AI applications beyond text-based games

The hybrid neuro-symbolic approach demonstrated by EXPLORER in text-based games holds immense potential for various AI applications beyond gaming contexts. One key implication is in natural language understanding tasks where combining neural networks for pattern recognition with symbolic reasoning for logic-based inference can enhance comprehension and decision-making capabilities. For instance, in chatbots or virtual assistants, this approach could improve dialogue management by incorporating contextual information from previous interactions while maintaining interpretability through symbolic rules. Moreover, in autonomous systems such as self-driving cars or robotics, a neuro-symbolic framework could enable more robust decision-making by blending deep learning for perception with logical reasoning for planning and control. This integration would allow machines to adapt to dynamic environments effectively while ensuring transparency in their actions. Additionally, applications in healthcare could benefit from a hybrid approach by enhancing medical diagnosis systems with both data-driven insights from neural networks and domain-specific rules derived from medical knowledge bases. This combination could lead to more accurate diagnoses and treatment recommendations while providing explanations for clinical decisions.

How can the concept of non-monotonic reasoning be applied in real-world scenarios outside of simulated environments like TBGs

Non-monotonic reasoning plays a crucial role in real-world scenarios outside simulated environments like Text-Based Games (TBGs) by enabling adaptive decision-making based on changing beliefs or new evidence. In practical settings such as fraud detection systems or cybersecurity applications, non-monotonic reasoning can help identify anomalies or threats that deviate from established patterns without requiring constant retraining of models. Furthermore, non-monotonic logic is valuable in legal frameworks where laws may have exceptions or interpretations that evolve over time. By using non-monotonic reasoning techniques, legal experts can model complex legal arguments efficiently while accommodating new precedents or regulations seamlessly. In business analytics and financial forecasting domains, non-monotonic reasoning aids in handling uncertain data sources and adjusting predictions based on emerging trends or market conditions dynamically. This flexibility allows predictive models to incorporate novel information without discarding existing knowledge structures entirely.
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