Case-based Action Planning in a First Person Scenario Game

Pascal Reuß, Jannis Hillmann, Sebastian Viefhaus, Klaus-Dieter Althoff

In: Rainer Gemulla , Simone Ponzetto , Christian Bizer , Margret Keuper , Heiner Stuckenschmidt (Hrsg.). LWDA 2018 - Lernen, Wissen, Daten, Analysen - Workshop Proceedings. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2018) August 22-24 Mannheim Germany Universität Mannheim 8/2018.


Creating a comprehensive and human-similar artificial intelligence in games in an interesting challenge and has been addressed in research and industry for several years. Several methods an technologies can be used to create computer controlled non-player characters, teammates, or opponents. Depending on the genre of the game, for example real-time strategy, board games, or first person scenarios, the tasks and challenges for an intelligent agent differs. In our scenario we choose a first-person scenario, where two software agents play against each other. While the behavior of one agent is rule-based, the other agent uses a case-based reasoning system to plan his tactics and actions. In this paper we present the first-person scenario and the rules and assumptions or it. We describe the knowledge modeling for our case-based agent in detail: the case structure and similarity model as well as the decision making process of the intelligent agent. We close the the paper with the presentation of an evaluation of our approach and a short outlook.

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paper_24.pdf (pdf, 1 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence