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Found 16 papers, 6 papers with code
Neuro-Symbolic Approaches for Text-Based Policy Learning
We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games.
Matching of Descriptive Labels to Glossary Descriptions
Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e. g., business terms, table column names) that are often consists of too short or too generic words and appears in their IT systems.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis
The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.
DiffG-RL: Leveraging Difference between State and Common Sense
We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder.
Commonsense Knowledge from Scene Graphs for Textual Environments
They are usually imperfect information games, and their interactions are only in the textual modality.
Deep Temporal Interpolation of Radar-based Precipitation
When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e. g. 5 minutes) is essential not to miss the cause of flooding in local regions.
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model
The experiments also showed that we can calibrate a corresponding DM in a virtual testing environment with up to 26% more accuracy than with fixed calibration methods.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning
The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Design and Implementation of Linked Planning Domain Definition Language
Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots.
Reinforcement Learning Testbed for Power-Consumption Optimization
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management.
Systems and Control
