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Found 8 papers, 1 papers with code
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving
We design a neural network to simulate SOIRE matching and theoretically prove that certain assignments of the set of parameters learnt by the neural network, called faithful encodings, are one-to-one corresponding to SOIREs for a bounded size.
A DQN-based Approach to Finding Precise Evidences for Fact Verification
Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV.
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.
Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification
By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs.
CoAPI: An Efficient Two-Phase Algorithm Using Core-Guided Over-Approximate Cover for Prime Compilation of Non-Clausal Formulae
The state-of-the-art approaches generate all primes along with a prime cover constructed by prime implicates using dual rail encoding.
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem
Based on the BLP and DTCC, we develop a local search algorithm named BDCC and improve it by a hyperheuristic strategy.
