Fetching the paper…
Reading the bibliography…
In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees.
R. R. Burridge, A. A. Rizzi, and D. E. Koditschek, “Sequential Composition of Dynamically Dexterous Robot Behaviors,” The International Journal of Robotics Research , vol. 18, no. 6, pp. 534–555, 1999
1999
Earlier work this paper cites.
T. J. Perkins and A. G. Barto, “Lyapunov design for safe reinforcement learning,” Journal of Machine Learning Research , vol. 3, no. Dec, pp. 803–832, 2002
2002
Earlier work this paper cites.
B. Srinivasan, P. Huguenin, K. Guemghar, and D. Bonvin, “A global stabilization strategy for an inverted pendulum,” IFAC Proceedings Volumes , vol. 35, no. 1, pp. 133 – 138, 2002, 15th IFAC World Congress. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1474667015386936
2002
Earlier work this paper cites.
A. Champandard, M. Dawe, and D. Cerpa, “Behavior trees: Three ways of cultivating strong ai,” in Game Developers Conference, Audio Lecture , 2010
2010
Earlier work this paper cites.
C. Lim, R. Baumgarten, and S. Colton, “Evolving Behaviour Trees for the Commercial Game DEFCON,” Applications of Evolutionary Computation , pp. 100–110, 2010
2010
Earlier work this paper cites.
R. Dey and C. Child, “Ql-bt: Enhancing behaviour tree design and implementation with q-learning,” in 2013 IEEE Conference on Computational Intelligence in Games (CIG) . IEEE, 2013, pp. 1–8
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, p. 484, 2016
2016
Earlier work this paper cites.
M. El Chamie, Y. Yu, and B. Açıkmese, “Convex synthesis of randomized policies for controlled markov chains with density safety upper bound constraints,” in American Control Conference , 2016, pp. 6290–6295
2016
Earlier work this paper cites.
M. Nicolau, D. Perez-Liebana, M. O’Neill, and A. Brabazon, “Evolutionary behavior tree approaches for navigating platform games,” IEEE Transactions on Computational Intelligence and AI in Games , vol. 9, no. 3, pp. 227–238, 2016
2016
Cited alongside, same era.
Y. Fu, L. Qin, and Q. Yin, “A reinforcement learning behavior tree framework for game ai,” in 2016 International Conference on Economics, Social Science, Arts, Education and Management Engineering . Atlantis Press, 2016
2016
Cited alongside, same era.
M. Colledanchise and P. Ögren, “How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees,” IEEE Transactions on Robotics , vol. 33, no. 2, pp. 372–389, 2017
2017
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015, Accessed on: Apr 20, 2017. [Online]. Available: http://dx.doi.org/10.1038/nature14236
M. Colledanchise, R. Parasuraman, and P. Ögren, “Learning of Behavior Trees for Autonomous Agents,” IEEE Transactions on Games, DOI 10.1109/TG.2018.2816806 , 2018
2018
Closest in time.
N. Justesen, P. Bontrager, J. Togelius, and S. Risi, “Deep learning for video game playing,” IEEE Transactions on Games , 2019
2019
Closest in time.
French, Wu, Pan, Zhou, and Jenkins, “Learning Behavior Trees From Demonstration,” in Robotics and Automation (ICRA), 2019 IEEE International Conference on . IEEE, 2019
2019
Closest in time.
C. I. Sprague, D. Izzo, and P. Ögren, “Learning dynamic-objective policies from a class of optimal trajectories,” in 2020 59th IEEE Conference on Decision and Control (CDC) . IEEE, 2020, pp. 597–602
2020
Closest in time.
C. I. Sprague and P. Ögren, “Continuous-time behavior trees as discontinuous dynamical systems,” IEEE Control Systems Letters , vol. 6, pp. 1891–1896, 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
F. Berkenkamp, M. Turchetta, A. Schoellig, and A. Krause, “Safe model-based reinforcement learning with stability guarantees,” in Advances in Neural Information Processing Systems , 2017, pp. 908–918
2017
Cited alongside, same era.
I. Sagredo-Olivenza, P. P. Gómez-Martín, M. A. Gómez-Martín, and P. A. González-Calero, “Trained behavior trees: Programming by demonstration to support ai game designers,” IEEE Transactions on Games , 2017
2017
Cited alongside, same era.
C. Paxton, A. Hundt, F. Jonathan, K. Guerin, and G. D. Hager, “CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 564–571
2017
Cited alongside, same era.
2018
Cited alongside, same era.
M. Colledanchise and P. Ögren, Behavior Trees in Robotics and AI, an Introduction . Chapman and Hall/CRC, 2018
2018
Cited alongside, same era.
D. Conner, A. Rizzi, and H. Choset, “Composition of local potential functions for global robot control and navigation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS) , vol. 4. IEEE, pp. 3546–3551
Cited in the paper.
2021
Closest in time.
M. Iovino, E. Scukins, J. Styrud, P. Ögren, and C. Smith, “A survey of behavior trees in robotics and ai,” accepted for publication in Autonomous Robots (and on ArXiv) , 2022
2022
Closest in time.
O. Biggar, M. Zamani, and I. Shames, “On modularity in reactive control architectures, with an application to formal verification,” to appear in ACM Transactions on Cyber-Physical Systems , 2022
2022
Closest in time.
W. Saunders, G. Sastry, A. Stuhlmueller, and O. Evans, “Trial without error: Towards safe reinforcement learning via human intervention,” in Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems, 2018, pp. 2067–2069
2069
Closest in time.