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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 151–175 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
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