If you use Projective Simulation in your work (in particular academic publications), the appropriate way to acknowledge the authors who created it is by citing the pertinent publication. If you just need a general reference for projective simulation, the seminal paper is
Hans J. Briegel & Gemma De las Cuevas, Projective simulation for artificial intelligence, Sci. Rep. 2, 400 (2012).
If you are using code that implements specific features or applications, you can find the relevant references inside the docstring; or try the search function:
List of publications on projective simulation
Benchmarking projective simulation in navigation problems Journal Article
Active learning machine learns to create new quantum experiments Journal Article
Proc. Natl. Acad. Sci. U.S.A., 115 (6), pp. 1221-1226, 2018.
Projective simulation with generalization Journal Article
Sci. Rep., 7 , pp. 14430, 2017.
Modelling collective motion based on the principle of agency Journal Article
Advances in quantum reinforcement learning Inproceedings
In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 282-287, 2017.
Meta-learning within projective simulation Journal Article
IEEE Access, 4 , pp. 2110-2122, 2016.
Robotic playing for hierarchical complex skill learning Inproceedings
Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2799-2804, 2016.
Quantum-enhanced machine learning Journal Article
Phys. Rev. Lett., 117 , pp. 130501, 2016.
A chance for attributable agency Journal Article
Minds Mach., 25 (3), pp. 261–279, 2015.
New Gener. Comput., 33 (1), pp. 69-114, 2015.
Sci. Rep., 5 , pp. 12874, 2015.
New J. Phys., 17 (2), pp. 023006, 2015.
Coherent controlization using superconducting qubits Journal Article
Sci. Rep., 5 , pp. 18036, 2015.
Quantum speed-up for active learning agents Journal Article
Phys. Rev. X, 4 , pp. 031002, 2014.
Projective simulation for artificial intelligence Journal Article
Sci. Rep., 2 , pp. 400, 2012.
PS features, as part of a larger architecture, also in
- S. Hangl, A. Mennel, J. Piater, A novel skill-based programming paradigm based on autonomous playing and skill-centric testing, arXiv:1709.06049 (2017)
- S. Hangl, E. Ugur, J. Piater, Autonomous robots: potential, advances and future direction, e & i Elektrotechnik und Informationstechnik 134 (6), 293-298 (2017)