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20112026
most citedReviewing and Benchmarking Parameter Control Methods in Differential Evolution

85 citations · 230 across the 14 of their papers we have counts for

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11 papers · 1 filter

cs.AI2026

Expansion Counts under Standard A* Tie-Breaking Strategies on the Final Plateau

Alex Fukunaga

In the A* search algorithm, the tie-breaking strategies for nodes with the same -value determines which states A* expands on the final -layer. For nine standard tie-breaking…

cs.AI2023

Plausibility-Based Heuristics for Latent Space Classical Planning

Yuta Takata, Alex Fukunaga

Recent work on LatPlan has shown that it is possible to learn models for domain-independent classical planners from unlabeled image data. Although PDDL models acquired by LatPlan c…

cs.AI2023

Learning Search-Space Specific Heuristics Using Neural Networks

Yu Liu, Ryo Kuroiwa, Alex Fukunaga

We propose and evaluate a system which learns a neuralnetwork heuristic function for forward search-based, satisficing classical planning. Our system learns distance-to-goal estima…

cs.AI2021★ 6 cited

Classical Planning in Deep Latent Space

Masataro Asai, Hiroshi Kajino, Alex Fukunaga +1

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, alth…

cs.AI2017★ 3 cited

A Survey of Parallel A*

Alex Fukunaga, Adi Botea, Yuu Jinnai +1

A* is a best-first search algorithm for finding optimal-cost paths in graphs. A* benefits significantly from parallelism because in many applications, A* is limited by memory usage…

cs.AI2017★ 2 cited

On Hash-Based Work Distribution Methods for Parallel Best-First Search

Yuu Jinnai, Alex Fukunaga

Parallel best-first search algorithms such as Hash Distributed A* (HDA*) distribute work among the processes using a global hash function. We analyze the search and communication o…

cs.AI2017★ 1 cited

Block-Parallel IDA* for GPUs (Extended Manuscript)

Satoru Horie, Alex Fukunaga

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*). We show that straightforward thread-based parallelization techniques which were previously proposed for m…

cs.AI2017★ 30 cited

Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

Masataro Asai, Alex Fukunaga

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, alth…

cs.AI2017★ 1 cited

Axioms in Model-based Planners

Shuwa Miura, Alex Fukunaga

Axioms can be used to model derived predicates in domain- independent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search…

cs.AI2012★ 45 cited

Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy

Akihiro Kishimoto, Alex Fukunaga, Adi Botea

Large-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard searc…

cs.AI2011★ 43 cited

Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems

A. S. Fukunaga, R. E. Korf

Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems c…