In this paper, we investigate an integration of individual and social learning, utilising co-evolutionary neural networks. Individual learning takes place by playing copies of a player against itself. Social learning allows poor performing players to learn from those players, which are playing at a higher level. The networks are evolved via evolutionary strategies with the network output being used as input to a minimax search tree. Our experiments show that learning is taking place at the 99% confidence level. In terms of performance, the co-evolutionary neural network player has the ability to block two adjacent stones of an opponent
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Ac...
The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for...
An interesting problem is under what circumstances will a collection of interacting agents realize e...
Baru-baru ini, penyongsang digunakan secara meluas dalam aplikasi industri. Walaubagaimanapun, tekn...
The series of studies about the convergence or not of the evolutionary strategies of players that us...
In recent years, much research attention has been paid to evolving self-learning game players. Fogel...
We study the evolution of cooperation in a structured population, combining insights from evolutiona...
When evolving a game-playing neural network, fitness is usually measured by playing against existing...
We study the evolution of cooperation in a structured population, combining insights from evolutiona...
In this thesis, neural-fitted temporal difference learning, a form of reinforcement learning, is use...
We study the role of the interaction network in a collaborative learning model known as the classifi...
This paper presents Neurogenetic Connect Four (NGC4), an evolutionary connectionist game solution, w...
We discuss the co-evolutionary learning method, applied to human learning, as a means toward a media...
We study evolutionary game theory in a setting where individuals learn from each other. We extend th...
This research investigates three applications of the Individual Evolutionary Learning (IEL) model. C...
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Ac...
The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for...
An interesting problem is under what circumstances will a collection of interacting agents realize e...
Baru-baru ini, penyongsang digunakan secara meluas dalam aplikasi industri. Walaubagaimanapun, tekn...
The series of studies about the convergence or not of the evolutionary strategies of players that us...
In recent years, much research attention has been paid to evolving self-learning game players. Fogel...
We study the evolution of cooperation in a structured population, combining insights from evolutiona...
When evolving a game-playing neural network, fitness is usually measured by playing against existing...
We study the evolution of cooperation in a structured population, combining insights from evolutiona...
In this thesis, neural-fitted temporal difference learning, a form of reinforcement learning, is use...
We study the role of the interaction network in a collaborative learning model known as the classifi...
This paper presents Neurogenetic Connect Four (NGC4), an evolutionary connectionist game solution, w...
We discuss the co-evolutionary learning method, applied to human learning, as a means toward a media...
We study evolutionary game theory in a setting where individuals learn from each other. We extend th...
This research investigates three applications of the Individual Evolutionary Learning (IEL) model. C...
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Ac...
The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for...
An interesting problem is under what circumstances will a collection of interacting agents realize e...