1 code implementation • 30 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.
no code implementations • 5 Oct 2020 • Ryoji Tanabe, Alex Fukunaga
We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs.
no code implementations • 2 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.
no code implementations • 2 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.
1 code implementation • 16 Aug 2017 • Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
no code implementations • 10 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.
no code implementations • 8 May 2017 • Satoru Horie, Alex Fukunaga
We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*).
1 code implementation • 29 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.
no code implementations • 11 Mar 2017 • Shuwa Miura, Alex Fukunaga
Axioms can be used to model derived predicates in domain- independent planning models.