Knowledge and Information Systems

, Volume 50, Issue 3, pp 917–943 | Cite as

Active inference for dynamic Bayesian networks with an application to tissue engineering

  • Caner Komurlu
  • Jinjian Shao
  • Banu Akar
  • Elif S. Bayrak
  • Eric M. Brey
  • Ali Cinar
  • Mustafa BilgicEmail author
Regular Paper


In temporal domains, agents need to actively gather information to make more informed decisions about both the present and the future. When such a domain is modeled as a temporal graphical model, what the agent observes can be incorporated into the model by setting the respective random variables as evidence. Motivated by a tissue engineering application where the experimenter needs to decide how early a laboratory experiment can be stopped so that its possible future outcomes can be predicted within an acceptable uncertainty, we first present a dynamic Bayesian network (DBN) model of vascularization in engineered tissues and compare it with both real-world experimental data and agent-based simulations. We then formulate the question of “how early an experiment can be stopped to guarantee an acceptable uncertainty about the final expected outcome” as an active inference problem for DBNs and empirically and analytically evaluate several search algorithms that aim to find the ideal time to stop a tissue engineering laboratory experiment.


Active inference Dynamic Bayesian networks Tissue engineering Vascularization 



This material is based upon work supported by the National Science Foundation under Grant No. IIS-1125412.


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceIllinois Institute of TechnologyChicagoUSA
  2. 2.Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoUSA
  3. 3.Department of Chemical and Biological EngineeringIllinois Institute of TechnologyChicagoUSA

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