Search Results for author: Alex Fukunaga

Found 9 papers, 3 papers with code

Classical Planning in Deep Latent Space

1 code implementation30 Jun 2021 Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise

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

TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods

no code implementations5 Oct 2020 Ryoji Tanabe, Alex Fukunaga

We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs.

How Far Are We From an Optimal, Adaptive DE?

no code implementations2 Oct 2020 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 that of existing, adaptive mechanisms for DE.

Reviewing and Benchmarking Parameter Control Methods in Differential Evolution

no code implementations2 Oct 2020 Ryoji Tanabe, Alex Fukunaga

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.


A Survey of Parallel A*

1 code implementation16 Aug 2017 Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

A* is a best-first search algorithm for finding optimal-cost paths in graphs.

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

no code implementations10 Jun 2017 Yuu Jinnai, Alex Fukunaga

We show that Abstract Zobrist hashing outperforms previous methods on search domains using hand-coded, domain specific feature projection functions.

graph partitioning

Block-Parallel IDA* for GPUs (Extended Manuscript)

no code implementations8 May 2017 Satoru Horie, Alex Fukunaga

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*).

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

1 code implementation29 Apr 2017 Masataro Asai, Alex Fukunaga

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.

Axioms in Model-based Planners

no code implementations11 Mar 2017 Shuwa Miura, Alex Fukunaga

Axioms can be used to model derived predicates in domain- independent planning models.

Cannot find the paper you are looking for? You can Submit a new open access paper.