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Found 12 papers, 4 papers with code
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners.
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck.
On the Transit Obfuscation Problem
Concealing an intermediate point on a route or visible from a route is an important goal in some transportation and surveillance scenarios.
On Hash-Based Work Distribution Methods for Parallel Best-First Search
We show that Abstract Zobrist hashing outperforms previous methods on search domains using hand-coded, domain specific feature projection functions.
A Survey of Parallel A*
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
Axioms in Model-based Planners
Axioms can be used to model derived predicates in domain- independent planning models.
Block-Parallel IDA* for GPUs (Extended Manuscript)
We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*).
Reviewing and Benchmarking Parameter Control Methods in Differential Evolution
We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.
How Far Are We From an Optimal, Adaptive DE?
We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to that of existing, adaptive mechanisms for DE.
TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs.
Learning Search-Space Specific Heuristics Using Neural Networks
We propose and evaluate a system which learns a neuralnetwork heuristic function for forward search-based, satisficing classical planning.
Plausibility-Based Heuristics for Latent Space Classical Planning
Recent work on LatPlan has shown that it is possible to learn models for domain-independent classical planners from unlabeled image data.
