85 citations · 230 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
Ryoji Tanabe, Alex Fukunaga
While a large number of adaptive Differential Evolution (DE) algorithms have been proposed, their Parameter Adaptation Methods (PAMs) are not well understood. We propose a Target f…
Reviewing and Benchmarking Parameter Control Methods in Differential Evolution
Ryoji Tanabe, Alex Fukunaga
Many Differential Evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral co…
How Far Are We From an Optimal, Adaptive DE?
Ryoji Tanabe, Alex Fukunaga
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…
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…
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…
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…
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…
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…
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…
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…