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 Predicting Game Difficulty and Engagement Using AI Players

2021 ◽  
Vol 5 (CHI PLAY) ◽  
pp. 1-17
Author(s):  
Shaghayegh Roohi ◽  
Christian Guckelsberger ◽  
Asko Relas ◽  
Henri Heiskanen ◽  
Jari Takatalo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Accuracy ◽  
Selection Strategy ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Average Performance ◽  
Novel Approach ◽  
Player Modelling ◽  
Human Player

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. We have previously demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.

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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