Ewa Lach

fACT - animation framework for generation of virtual characters behaviours

Virtual characters are widely used in many applications, especially in computer games, films and educational systems. Creation of intelligent characters capable of deciding about their actions is currently afield of extensive research. A new behavioural animation framework fACT, described in this paper, is an answer to a problem of finding an application that allows for using unrestricted number of techniques for generation of virtual characters behaviours. fACTs architecture permits easy substitution of one technique by another. It also allows for easy integration with existing tools: applications and libraries. The paper presents frameworks modules and how these are integrated. Some programming issues and the main steps of the simulation flow are also analysed.

A Quick Method for Dynamic Difficulty Adjustment of a Computer Player in Computer Games

Games are boring when they are too easy and frustrating when they are too hard. A game in which the challenge level matches the skill of the human player has the greatest entertainment value. In this paper a simple and fast method for adjusting a difficulty level of a computer opponent is introduced. An empirical investigation of the method when playing a FPS game is conducted. Different kinds of players are analyzed. The results show that proposed method matches the difficulty level of an opponent to a player’s skill level even without full information about player’s abilities.

Dynamic Difficulty Adjustment for Serious Game Using Modified Evolutionary Algorithm.

Dynamic Difficulty Adjustment (DDA) seeks to adapt the challenge a game poses to a human player. When the game is too easy the player can become bored, when it is too hard - frustrated. In the case of a serious game (educational game), additionally, without a balance between the player competence and the game challenge the game could repeatedly exploit the developed skills, or fail to achieve the pedagogical goals. In this paper evolutionary algorithm (EA) is used to find game settings suitable for the player of a serious math game. To reduce the number of training data and accelerate the search for the ‘right’ game difficulty level EA modifications are introduced. Various experiments are performed. The obtained results show that proposed methods can substantially decrease the time a human player has to wait for a suitable game level.

New Adaptations for Evolutionary Algorithm Applied to Dynamic Difficulty Adjustment System for Serious Game

The aim of the Dynamic Difficulty Adjustment is to dynamically balance the difficulty level of the games in order to keep the user interested in playing. Generally, a game in which the challenge level matches the skill of the human player has a greater entertainment value than a game that is either too easy (boring) or too hard (frustrating). An entertainment has an important role to play in serious games (educational games), contributing to their motivational and engaging qualities leading to players voluntarily playing serious games for extended periods of time. In this paper, we present new adaptations for reducing the number of training data for the evolutionary algorithms used to find game settings suitable for the player of a serious game. The training process for a human player should be as short as possible. Various experiments are performed. The obtained results show that the proposed adaptation causes a substantial decrease in training data for different players.