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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 226–250 of 261 papers

TitleStatusHype
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments—0
Consolidating LAMA with Best-First Width Search—0
Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search—0
Defeasible Decisions: What the Proposal is and isn't—0
Dense Crowd Flow-Informed Path Planning—0
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control—0
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces—0
Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams—0
DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies—0
Dynamic Importance Sampling for Anytime Bounds of the Partition Function—0
Dynamic User Pairing for Non-Orthogonal Multiple Access in Downlink Networks—0
Ecological landscapes guide the assembly of optimal microbial communities—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring—0
Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Exponential-Binary State-Space Search—0
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics—0
Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Feature-Based Diversity Optimization for Problem Instance Classification—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution—0
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