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Heuristic Search

Heuristic Search is a problem-solving method that uses practical rules or "guides" (heuristics) to find solutions more quickly than exhaustive search, by focusing on the most promising paths first.

Papers

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Showing 151–200 of 261 papers

TitleStatusHype
Dynamic User Pairing for Non-Orthogonal Multiple Access in Downlink Networks—0
Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned SystemsCode0
Learning Heuristic Selection with Dynamic Algorithm ConfigurationCode0
Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions—0
Reactive Sample Size for Heuristic Search in Simulation-based Optimization—0
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising—0
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)—0
gBeam-ACO: a greedy and faster variant of Beam-ACO—0
Multi-Resolution A*—0
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics—0
A Neural Architecture Search based Framework for Liquid State Machine Design—0
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization—0
ARMS: Automated rules management system for fraud detectionCode0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing—0
Optimizing Dynamic Structures with Bayesian Generative Search—0
Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters—0
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing—0
Counterfactual Explanation Algorithms for Behavioral and Textual DataCode0
The αμ Search Algorithm for the Game of Bridge—0
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning—0
Action Selection for MDPs: Anytime AO* vs. UCT—0
Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction—0
Temporal Planning with Intermediate Conditions and Effects—0
A Heuristic for Efficient Reduction in Hidden Layer Combinations For Feedforward Neural Networks—0
Compiler-Level Matrix Multiplication Optimization for Deep Learning—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters—0
Discovering Reliable Correlations in Categorical DataCode0
GAN Path Finder: Preliminary resultsCode1
ASNets: Deep Learning for Generalised PlanningCode0
Iterative Budgeted Exponential Search—0
Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering—0
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates—0
Heuristic Authorship ObfuscationCode0
Hybrid Planning for Dynamic Multimodal Stochastic Shortest PathsCode0
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces—0
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees—0
Exponential-Binary State-Space Search—0
Optimal Decision Making Under Strategic BehaviorCode1
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs—0
Improved Safe Real-time Heuristic Search—0
Semantic variation operators for multidimensional genetic programmingCode0
A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters—0
Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments—0
Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices—0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
Learning Loop Invariants for Program VerificationCode0
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise—0
Single-Agent Policy Tree Search With GuaranteesCode0
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