A Study of Machine Learning using the Game of Fox and Geese
نویسندگان
چکیده
The game Fox and Geese is solved using retrograde analysis. A neural network trained using a co-evolutionary genetic algorithm with the help of the expert knowledge database was found to be a very capable Fox and Geese player after training, and quickly learned to beat training opponents. Key-Words: Game theory, rote-learning, neural networks, genetic algorithms, co-evolution.
منابع مشابه
An Adaptive Learning Game for Autistic Children using Reinforcement Learning and Fuzzy Logic
This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itsel...
متن کاملThe effect of using computer games on learning free throw Basketball skill and comparison with skilled and learning pattern
The aim of this experiment was to study the effect of computer game on acquisition and retention of free throw basketball skills (accuracy and pattern) and compare this method with two patterning methods (skilled and learning). 50 high school students age range 15-17 years were selected as a research samples. Subjects after participating in the pretest were divided into 5 equal groups and each ...
متن کاملSports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey
In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mini...
متن کاملEmotion Detection in Persian Text; A Machine Learning Model
This study aimed to develop a computational model for recognition of emotion in Persian text as a supervised machine learning problem. We considered Pluthchik emotion model as supervised learning criteria and Support Vector Machine (SVM) as baseline classifier. We also used NRC lexicon and contextual features as training data and components of the model. One hundred selected texts including pol...
متن کاملForecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function
Stock market forecasting has attracted so many researchers and investors that many studies have been done in this field. These studies have led to the development of many predictive methods, the most widely used of which are machine learning-based methods. In machine learning-based methods, loss function has a key role in determining the model weights. In this study a new loss function is ...
متن کامل