IBAL

IBAL: a probabilistic rational programming language. In a rational programming language, a program specifies a situation faced by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. This paper presents IBAL, a rational programming language for probabilistic and decision-theoretic agents. IBAL provides a rich declarative language for describing probabilistic models. The expression language allows the description of arbitrarily complex generative models. In addition, IBAL’s observation language makes it possible to express and compose rejective models that result from conditioning on the observations. IBAL also integrates Bayesian parameter estimation and decisiontheoretic utility maximization thoroughly into the framework. All these are packaged together into a programming language that has a rich type system and built-in extensibility. This paper presents a detailed account of the syntax and semantics of IBAL, as well as an overview of the implementation.


References in zbMATH (referenced in 26 articles )

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  1. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  2. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)
  3. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  4. Carvalho, Rommel N.; Laskey, Kathryn B.; Costa, Paulo C. G.: PR-OWL - a language for defining probabilistic ontologies (2017)
  5. Huang, Daniel; Morrisett, Greg: An application of computable distributions to the semantics of probabilistic programming languages (2016)
  6. Wingate, David; Kane, Jonathan; Wolinsky, Matt; Sylvester, Zoltán: A new approach for conditioning process-based geologic models to well data (2016)
  7. Crubillé, Raphaëlle; Dal Lago, Ugo; Sangiorgi, Davide; Vignudelli, Valeria: On applicative similarity, sequentiality, and full abstraction (2015)
  8. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  9. Howard, Catherine; Stumptner, Markus: A survey of directed entity-relation-based first-order probabilistic languages (2014)
  10. Sangiorgi, Davide: Higher-order languages: bisimulation and coinductive equivalences (extended abstract) (2014)
  11. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2013)
  12. Gordon, Andrew D.; Aizatulin, Mihhail; Borgstrom, Johannes; Claret, Guillaume; Graepel, Thore; Nori, Aditya V.; Rajamani, Sriram K.; Russo, Claudio: A model-learner pattern for Bayesian reasoning (2013)
  13. Hutter, Marcus; Lloyd, John W.; Ng, Kee Siong; Uther, William T. B.: Probabilities on sentences in an expressive logic (2013)
  14. Freer, Cameron E.; Roy, Daniel M.: Computable de Finetti measures (2012)
  15. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2011)
  16. Gutmann, Bernd; Thon, Ingo; Kimmig, Angelika; Bruynooghe, Maurice; De Raedt, Luc: The magic of logical inference in probabilistic programming (2011)
  17. Freer, Cameron E.; Roy, Daniel M.: Computable exchangeable sequences have computable de Finetti measures (2009)
  18. van Otterlo, Martijn: The logic of adaptive behavior. Knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains. (2009)
  19. Dietterich, Thomas G.; Domingos, Pedro; Getoor, Lise; Muggleton, Stephen; Tadepalli, Prasad: Structured machine learning: The next ten years (2008) ioport
  20. Ng, K. S.; Lloyd, J. W.; Uther, W. T. B.: Probabilistic modelling, inference and learning using logical theories (2008)

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