scholarly journals A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108418-108428 ◽  
Author(s):  
Zhihao Xing ◽  
Shikui Tu
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Search Approach

scholarly journals Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Audrey Gaymann ◽  
Francesco Montomoli
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Topology Optimization ◽  
Deep Neural Network ◽  
Level Set Methods ◽  
Test Case ◽  
Tree Search ◽  
Fluid Structure ◽  
Monte Carlo Tree Search ◽  
Structure Topology

Abstract This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.

scholarly journals An Entropy-Guided Monte Carlo Tree Search Approach for Generating Optimal Container Loading Layouts

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 866 ◽  
Author(s):  
Richard Cant ◽  
Ayodeji Remi-Omosowon ◽  
Caroline Langensiepen ◽  
Ahmad Lotfi
Keyword(s):  
Monte Carlo ◽  
Deterministic Algorithm ◽  
Computational Time ◽  
Entropy Measure ◽  
Tree Search ◽  
Container Loading ◽  
Monte Carlo Tree Search ◽  
Spatial Entropy ◽  
Novel Approach ◽  
Search Approach

In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.

scholarly journals A Monte Carlo Tree Search approach to Active Malware Analysis

2017 ◽  
Author(s):  
Riccardo Sartea ◽  
Alessandro Farinelli
Keyword(s):  
Monte Carlo ◽  
Prior Information ◽  
Learning Algorithm ◽  
Stochastic Game ◽  
Tree Search ◽  
Malware Analysis ◽  
Design Strategies ◽  
Monte Carlo Tree Search ◽  
Analysis Techniques ◽  
Search Approach

Active Malware Analysis (AMA) focuses on acquiring knowledge about dangerous software by executing actions that trigger a response in the malware. A key problem for AMA is to design strategies that select most informative actions for the analysis. To devise such actions, we model AMA as a stochastic game between an analyzer agent and a malware sample, and we propose a reinforcement learning algorithm based on Monte Carlo Tree Search. Crucially, our approach does not require a pre-specified malware model but, in contrast to most existing analysis techniques, we generate such model while interacting with the malware. We evaluate our solution using clustering techniques on models generated by analyzing real malware samples. Results show that our approach learns faster than existing techniques even without any prior information on the samples.

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