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LS-Draughts - A Draughts Learning System based on genetic algorithms, neural network and temporal differences

A tournament was promoted between the best player obtained by the LS-Draughts and the best available player of the NeuroDRAughts, which confirms that the GAs can be an important tool for improving the general performance of automatic players.
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ACE-RL-Checkers: decision-making adaptability through integration of automatic case elicitation, reinforcement learning, and sequential pattern mining

This study proposes an automatic Checkers player equipped with a dynamic decision-making module, which adapts to the profile of the opponent over the course of the game, and proposes a new module based on sequential pattern mining for generating a base of experience rules extracted from human expert's game records.
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LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method

LS-VisionDraughts is an efficient unsupervised evolutionary learning system for Checkers whose contribution is to automate the process of selecting an appropriate representation for the board states – by means of Evolutionary Computation – keeping a deep look-ahead at the moment of choosing an adequate move.
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MP-Draughts: Unsupervised Learning Multi-agent System Based on MLP and Adaptive Neural Networks

The accomplishment of the best architecture of MP-Draughts found in the investigations performed in this paper was evaluated in terms of the following parameters: coherence and appropriateness of the clustering process, and performance in tournaments against other unsupervised learning-based agents for Checkers.
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Improving the AHT in Telecommunication Companies by Automatic Modeling of Call Center Service

This paper proposes to enhance the customer service of an ISP company through the following strategy: firstly, performing a modelling of its CRM Data Warehouse; and secondly, using such model to improve the call center scripts, so as to reduce the Average Handle Time.
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Improving the Accuracy of the Cases in the Automatic Case Elicitation-Based Hybrid Agents for Checkers

The authors propose two alternative strategies to calculate the rating of the cases generated in ACE-RL-Checkers in such a way as to improve future performance and confirm the improvement in the accuracy of the Cases generated by the proposed strategies and their consequent performance in relation to the original strategy.
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ACE-RL-Checkers: Improving automatic case elicitation through knowledge obtained by reinforcement learning in player agents

The authors present the ACE-RL-Checkers player agent, a hybrid system that combines the best abilities from the automatic Checkers players CHEBR and LS-VisionDraughts and gains in terms of performance as well as adaptability in its decision-making — choosing moves based on the current game dynamics.
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LS-Draughts: Using Databases to Treat Endgame Loops in a Hybrid Evolutionary Learning System

The LS-Draughts shows that the GAs can be an important tool for improving the general performance of automatic players and also indicates to what extent the board sate represented by features in the Network input is favorable to the agent.
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Lattice Stability and Generation of Massive Dirac Fermions in α-Graphynes and Topological Line Defects in Monolayer BN

†Carbon allotropes named α-graphynes (αGy), with sp2 and sp3 bonded carbon atoms (Fig. 1), have been shown to display graphene-like electronic structures with the characteristic Dirac cones. Here, we…

LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method

LS-VisionDraughts is an efficient unsupervised evolutionary learning system for Checkers whose contribution is to automate the process of selecting an appropriate representation for the board states – by means of Evolutionary Computation – keeping a deep look-ahead at the moment of choosing an adequate move.
View on Springer (opens in a new tab)
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