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This paper describes an approach used to build a practical AI solution for a 3D boxing simulation game. The features of the designed AI agent are based on our deliberate concentration on believability, i.e. human-likeness of agent’s behavior. We show how learning by observation and case-based reasoning techniques can be used to create an AI(More)
This paper describes an approach used to build and optimize a practical AI solution for a 3D boxing simulation game. The two main features of the designed AI agent are believability (human-likeness of agent's behavior) and effectiveness (agent's capability to reach own goals). We show how learning by observation and case-based reasoning techniques are used(More)
The rapid development of complex virtual worlds (most notably, in 3D computer and video games) introduces new challenges for the creation of virtual agents, controlled by artificial intelligence (AI) systems. Two important subproblems in this topic area which need to be addressed are (a) believability and (b) effectiveness of agents' behavior, i.e.(More)
The rapid development of complex virtual worlds (most notably, in 3D computer and video games) introduces new challenges for the creation of virtual agents, controlled by Artificial Intelligence (AI) systems. Two important sub-problems in this topic area that need to be addressed are (a) believability and (b) effectiveness of agents' behavior (i.e.(More)
We describe a method used to build a practical AI system for a mobile game of tennis. The chosen approach had to support two goals: (1) provide a large number of believable and diverse AI characters, and (2) let the users train AI “ghost” characters able to substitute them. We achieve these goals by learning AI agents from collected behavior(More)
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