Adaptation of Intelligent Characters to Changes of Game Environments

Comparison of AI Techniques for Fighting Action Games - Genetic Algorithms/Neural Networks/Evolutionary Neural Networks

“…One characteristic of board games is that you must be aware of the overall situation of the pieces on the board and determine their movement. Recently, there have been studies to apply neural networks to fighting action games [7,8,9]. These studies used the action and step of the opponent character and the distance between characters as the input for neural networks, and the difference of scores resulting from the actions of two characters as the reinforcement value so as to make the intelligent characters learn whether or not their current action is appropriate.…”

“…These studies used the action and step of the opponent character and the distance between characters as the input for neural networks, and the difference of scores resulting from the actions of two characters as the reinforcement value so as to make the intelligent characters learn whether or not their current action is appropriate. [7,8,9] expressed the neural network to represent intelligent characters as Figure 1. In Figure 1, input is the information related to the opponent character, and PA(t) indicates the action of the opponent character at time "t", while T indicates the progress level of a particular action, and D indicates the distance between the intelligent character and the opponent character.…”

“…For the neural network, the feedforward neural networks are used which calculate the output in the same way as the general feedforward neural networks. Also, the reinforcement learning method is used for learning by neural networks [7,8]. …”

“…In such genres as fighting action games, it is more important to make the characters intelligent so that they can cope appropriately according to the location and action of surrounding opponents, rather than to tune up the overall strategy of the game. Due to this reason, many have carried out studies to implement intelligent characters in fighting action games [7,8,9,10]. They used genetic algorithms, neural networks, and evolutionary neural networks to create intelligent characters for fighting action games.…”

“…Studies using neural networks used the action and step of the opponent character and the distance between characters as the input for neural networks, and the difference of scores resulting from the actions of two characters as the reinforcement value so as to make the intelligent characters learn whether or not their current action is appropriate [8,9]. However, because neural networks that use the error backpropagation algorithm have such shortcomings as falling in local minima and slow convergence speed, methods to apply evolutionary neural networks were studied [7]. These studies expressed the weight of links in a neural network as a gene, and evolved chromosomes by expressing the weights of all links of intelligent characters in the form of one-dimensional array.…”