Heuristic Search
77 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in Heuristic Search
Most implemented papers
Counterfactual Explanation Algorithms for Behavioral and Textual Data
This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets.
Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths
Sequential decision problems in applications such as manipulation in warehouses, multi-step meal preparation, and routing in autonomous vehicle networks often involve reasoning about uncertainty, planning over discrete modes as well as continuous states, and reacting to dynamic updates.
GAN Path Finder: Preliminary results
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it.
Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems
Machine learning is becoming increasingly important to control the behavior of safety and financially critical components in sophisticated environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption.
Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants
We illustrate the accuracy and speed of our new method on a variety of simulated scenarios as well as in the estimation of a phylogenetic network for the genus Canis.
Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects
First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i. e. the number of objects, state variables and their domain sizes).
Deep Generative Symbolic Regression
samholt/deepgenerativesymbolicregression
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Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery.
Domain-Independent Dynamic Programming
We experimentally compare our DIDP solvers with commercial MIP and CP solvers (solving MIP and CP models, respectively) on common benchmark instances of eleven combinatorial optimization problem classes.
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions.
Learning Heuristic Search via Imitation
In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand.
