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Evolving Behaviour Tree Structures Using Grammatical Evolution

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Abstract

Behaviour Trees are control structures with many applications in computer science, including robotics, control systems, and computer games. They allow the specification of controllers from very broad behaviour definitions (close to the root of the tree) down to very specific technical implementations (near the leaves); this allows them to be understood and extended by both behaviour designers and technical programmers. This chapter describes the process of applying Grammatical Evolution (GE) to evolve Behaviour Trees for a real-time video-game: the Mario AI Benchmark. The results obtained show that these structures are quite amenable to artificial evolution using GE, and can provide a good balance between long-term (pathfinding) and short-term (reactiveness to hazards and power-ups) planning within the same structure.

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Notes

  1. 1.

    The sum of absolute differences in Cartesian coordinates.

  2. 2.

    This and all results reported in this chapter are averaged over 30 independent runs. Videos of the best controllers of some runs are available online (http://tinyurl.com/gebtMarioAI).

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Author information

Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Diego Perez-Liebana

  2. Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland

    Miguel Nicolau

Authors
  1. Diego Perez-Liebana
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  2. Miguel Nicolau
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Correspondence to Diego Perez-Liebana .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    Conor Ryan

  2. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  3. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    JJ Collins

Appendix: Actions, Conditions, Filters and Sub-trees for Mario

Appendix: Actions, Conditions, Filters and Sub-trees for Mario

This section contains tables with the actions, conditions, filters and sub-trees used by the approaches described in this chapter to evolve Behaviour Trees. Note that some actions and conditions can be analogous in both the controllers with and without A* (i.e. IsBreakableUp vs. UnderBrick); they are, however, different: while the A* version checks the nodes in the graph, the no-A* implementation needs to analyse the contents of each cell. Also, note that actions use the terms left and right, which imply movement, while conditions use ahead (for right) and back (for left). Entries marked with a † denote sub-trees that have an analogous left (or back) variant (Tables 4 and 5).

Table 4 Filters available to the GE to evolve a BT
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Table 5 Conditions, actions and sub-trees available to the GE to evolve a BT
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Perez-Liebana, D., Nicolau, M. (2018). Evolving Behaviour Tree Structures Using Grammatical Evolution. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-78717-6_18

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