Abstract
This paper focuses on the design of autonomous behaviors based on humans behaviors observation. In this context, the contribution of the Orion model is to gather and to take advantage of two approaches: data mining techniques (to extract knowledge from the human) and behavior models (to control the autonomous behaviors). In this paper, the Orion model is described by UML diagrams. More than a model, Orion is an operational tool allowing to represent, transform, visualize and predict data; it also integrates operational standard behavioral models. Orion is illustrated to control a bot in the game Unreal Tournament. Thanks to Orion, we can collect data of low level behaviors through three scenarios performed by human players: movement, long range aiming and close combat. We can easily transform the data and use some data mining techniques to learn behaviors from human players observation. Orion allows us to build a complete behavior using an extension of a Behavior Tree integrating ad hoc features in order to manage aspects of behavior that we have not been able to learn automatically.
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Notes
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Reflection provides information about the class to which an object belongs and also the methods of that class which can be executed by using the object.
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Data discretization is a pre-processing method that reduces the number of values for a given continuous variable by dividing its range into a finite set of disjoint intervals, and then relates these intervals with meaningful labels.
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Appendices
Appendix 1
See Fig. 11.
Orion data model
Appendix 2
See Fig. 12.
Orion data transformation model
Appendix 3
See Fig. 13.
Orion data visualization model
Appendix 4
See Fig. 14.
Orion prediction model
Appendix 5
See Fig. 15.
Orion data mining behaviors
Appendix 6
See Fig. 16.
Orion online learning model
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Buche, C., Even, C., Soler, J. (2020). Orion: A Generic Model and Tool for Data Mining. In: Gavrilova, M., Tan, C., Sourin, A. (eds) Transactions on Computational Science XXXVI. Lecture Notes in Computer Science(), vol 12060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61364-1_1
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