Skip to main content

Advertisement

SpringerLink
Log in

On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition

  • Technical Contribution
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Recognizing causal activities of human protagonists, and jointly inferring context information like location of objects and agents from noisy sensor data is a challenging task. Causal models can be used, which describe the activity structure symbolically, e.g. by precondition-effect actions. Recently, probabilistic programming languages (PPLs) arose as an abstraction mechanism that allow to concisely define probabilistic models by a general-purpose programming language, and provide off-the-shelf, general-purpose inference algorithms. In this paper, we empirically investigate whether PPLs provide a feasible alternative for implementing causal models for human activity recognition, by comparing the performance of three different PPLs (Anglican, WebPPL and Figaro) on a multi-agent scenario. We find that PPLs allow to concisely express causal models, but general-purpose inference algorithms that are typically implemented in PPLs are outperformed by an application-specific inference algorithm by orders of magnitude. Still, PPLs can be a valuable tool for developing probabilistic models, due to their expressiveness and simple applicability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Interestingly, the state space size relates exponentially to the number of agents (due to the exponential number of agent permutations). Thus, when the scenario complexity is increased exponentially, the estimation error (in terms of JSD) grows only linearly.

References

  1. Andrieu C, Doucet A, Holenstein R (2010) Particle markov chain monte carlo methods. J R Stat Soc Ser B (Stat Methodol) 72(3):269–342

    Article  MathSciNet  Google Scholar 

  2. Bingham E, Chen J.P, Jankowiak M, Obermeyer F, Pradhan N, Karaletsos T, Singh R, Szerlip P, Horsfall P, Goodman N.D (2018) Pyro: Deep Universal Probabilistic Programming. arXiv preprint arXiv:1810.09538

  3. Borgström J, Gordon A.D, Greenberg M, Margetson J, Gael J.V (2011) Measure transformer semantics for Bayesian machine learning. In: European symposium on programming, pp. 77–96. Springer, Berlin. https://doi.org/10.1007/978-3-642-19718-5_5

    Chapter  Google Scholar 

  4. Carpenter B, Gelman A, Hoffman M.D, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017) Stan: a probabilistic programming language. J Stat Softw. https://doi.org/10.18637/jss.v076.i01

  5. De Raedt L, Kersting K, Natarajan S, Poole D (2016) Statistical relational artificial intelligence: logic, probability, and computation. Synth Lect Artif Intell Mach Learn 10:1–189

    Article  Google Scholar 

  6. Doucet A, de Freitas N, Gordon N (2001) Sequential Monte Carlo Methods in Practice. Springer, Berlin

    Book  Google Scholar 

  7. Fierens D, Van den Broeck G, Renkens J, Shterionov D, Gutmann B, Thon I, Janssens G, De Raedt L (2015) Inference and learning in probabilistic logic programs using weighted boolean formulas. Theory Pract Logic Programm 15(3):358–401

    Article  MathSciNet  Google Scholar 

  8. Goodman N, Mansinghka V, Roy D, Bonawitz K, Tenenbaum J (2008) Church: a language for generative models. In: Proceedings of the conference on uncertainty in artificial intelligence

  9. Goodman N.D, Stuhlmüller A (2014) The design and implementation of probabilistic programming languages. http://dippl.org. Accessed 29 Mar 2018

  10. Häggström O (2002) Finite Markov chains and algorithmic applications, vol 52. Cambridge University Press, Cambridge

    Book  Google Scholar 

  11. Krüger F, Nyolt M, Yordanova K, Hein A, Kirste T (2014) Computational state space models for activity and intention recognition. A feasibility study. PLoS One 9(11):e109381. https://doi.org/10.1371/journal.pone.0109381

    Article  Google Scholar 

  12. Krüger F, Steiniger A, Bader S, Kirste T (2012) Evaluating the robustness of activity recognition using computational causal behavior models. In: Proceedings of the international workshop on situation, activity and goal awareness held at Ubicomp 2012, pp. 1066–1074. ACM, Pittsburgh, PA, USA. https://doi.org/10.1145/2370216.2370443

  13. Kulkarni T.D, Kohli P, Tenenbaum J.B, Mansinghka V (2015) Picture: a probabilistic programming language for scene perception. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp. 4390–4399

  14. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) Winbugs—a bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10(4):325–337

    Article  Google Scholar 

  15. McCallum A, Schultz K, Singh S (2009) FACTORIE: probabilistic programming via imperatively defined factor graphs. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22, Curran Associates, Inc., pp. 1249–1257. http://papers.nips.cc/paper/3654-factorie-probabilistic-programming-via-imperatively-defined-factor-graphs.pdf

  16. Nyolt M, Kirste T (2015) On resampling for Bayesian filters in discrete state spaces. In: Proceedings 2015 IEEE 27th international conference on tools with artificial intelligence, pp. 526–533. IEEE computer society. https://doi.org/10.1109/ICTAI.2015.83

  17. Nyolt M, Krüger F, Yordanova K, Hein A, Kirste T (2015-06) Marginal filtering in large state spaces. Int J Approx Reason 61:16–32. https://doi.org/10.1016/j.ijar.2015.04.003

    Article  MathSciNet  Google Scholar 

  18. Paige B, Wood F (2014) A compilation target for probabilistic programming languages. In: Xing EP, Jebara T (eds) Proceedings of the 31st international conference on machine learning, proceedings of machine learning research, vol 32, pp. 1935–1943. PMLR, Beijing, China

  19. Patil A, Huard D, Fonnesbeck CJ (2010) Pymc: Bayesian stochastic modelling in python. J Stat Softw 35(4):1

    Article  Google Scholar 

  20. Pfeffer A (2016) Practical probabilistic programming, 1st edn. Manning Publications Co., Shelter Island

    Google Scholar 

  21. Poon H, Domingos P (2006) Sound and efficient inference with probabilistic and deterministic dependencies. AAAI 6:458–463

    Google Scholar 

  22. Popko M, Lüdtke S (2018) On the applicability of probabilistic programming languages for causal activity recognition. https://doi.org/10.5281/zenodo.1635591

  23. Sato T, Kameya Y (2008) New advances in logic-based probabilistic modeling by PRISM. Springer, Berlin. pp 118–155 https://doi.org/10.1007/978-3-540-78652-8_5

    Chapter  Google Scholar 

  24. Tolpin D, van de Meert JW, Yang H, Wood F (2016) Design and implementation of probabilistic programming language Anglican. In: Proceedings of the 28th symposium on the implementation and application of functional programming languages, pp. 6:1–6:12. ACM

  25. Turliuc CR, Dickens L, Russo A, Broda K (2016-11) Probabilistic abductive logic programming using Dirichlet priors. Int J Approx Reason 78:223–240. https://doi.org/10.1016/j.ijar.2016.07.001

    Article  MathSciNet  Google Scholar 

  26. Wood F, van de Meert JW, Mansinghka V (2014) A new approach to probabilistic programming inference. In: Artificial intelligence and statistics

Download references

Acknowledgements

We are grateful to the anonymous reviewers for their comments and suggestions, which vastly improved the presentation and discussion of our work.

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Rostock, Rostock, Germany

    Stefan Lüdtke, Maximilian Popko & Thomas Kirste

Authors
  1. Stefan Lüdtke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maximilian Popko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Kirste
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Lüdtke.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lüdtke, S., Popko, M. & Kirste, T. On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition. Künstl Intell 33, 389–399 (2019). https://doi.org/10.1007/s13218-019-00580-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-019-00580-7

Keywords

Search

Navigation