publication . Preprint . Part of book or chapter of book . 2017

RankPL: A Qualitative Probabilistic Programming Language

Tjitze Rienstra;
Open Access English
  • Published: 19 May 2017
Abstract
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn’s ranking theory. Broadly speaking, RankPL can be used to represent and reason about processes that exhibit uncertainty expressible by distinguishing “normal” from “surprising” events. RankPL allows (iterated) revision of rankings over alternative program states and supports various types of reasoning, including abduction and causal inference. We present the language, its denotational semantics, and a number of practical examples. We also discuss an implementation of RankPL that is availabl...
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doi: 10.1007/978-3-319-61581-3_42
Subjects
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Programming Languages, Functional logic programming, Fifth-generation programming language, Modeling language, Artificial intelligence, business.industry, business, Denotational semantics, Semantics, Machine learning, computer.software_genre, computer, Inductive programming, Probabilistic programming language, Natural language processing, Computer science, Programming language theory
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1. Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artificial intelligence 89(1-2), 1-29 (1996)

2. G¨ardenfors, P., Rott, H.: Handbook of logic in artificial intelligence and logic programming (vol. 4). chap. Belief Revision, pp. 35-132. Oxford University Press, Oxford, UK (1995)

3. Goldszmidt, M., Pearl, J.: Qualitative probabilities for default reasoning, belief revision, and causal modeling. Artificial Intelligence 84(1), 57-112 (1996) [OpenAIRE]

4. Goodman, N.D., Mansinghka, V.K., Roy, D.M., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: McAllester, D.A., Myllym¨aki, P. (eds.) UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9-12, 2008. pp. 220-229. AUAI Press (2008)

5. Halpern, J.Y.: Reasoning about uncertainty. MIT Press (2005)

6. Kozen, D.: Semantics of probabilistic programs. J. Comput. Syst. Sci. 22(3), 328- 350 (1981) [OpenAIRE]

7. Kyburg Jr, H.E.: Probability and the logic of rational belief (1961)

8. Mansinghka, V.K., Selsam, D., Perov, Y.N.: Venture: a higher-order probabilistic programming platform with programmable inference. CoRR abs/1404.0099 (2014) [OpenAIRE]

9. Pearl, J.: System Z: A natural ordering of defaults with tractable applications to nonmonotonic reasoning. In: Parikh, R. (ed.) Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge, Pacific Grove, CA, March 1990. pp. 121-135. Morgan Kaufmann (1990)

10. Pfeffer, A.: Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report 137 (2009)

11. Spohn, W.: The Laws of Belief - Ranking Theory and Its Philosophical Applications. Oxford University Press (2014)

Related research
Abstract
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn’s ranking theory. Broadly speaking, RankPL can be used to represent and reason about processes that exhibit uncertainty expressible by distinguishing “normal” from “surprising” events. RankPL allows (iterated) revision of rankings over alternative program states and supports various types of reasoning, including abduction and causal inference. We present the language, its denotational semantics, and a number of practical examples. We also discuss an implementation of RankPL that is availabl...
Read more
doi: 10.1007/978-3-319-61581-3_42
Subjects
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Programming Languages, Functional logic programming, Fifth-generation programming language, Modeling language, Artificial intelligence, business.industry, business, Denotational semantics, Semantics, Machine learning, computer.software_genre, computer, Inductive programming, Probabilistic programming language, Natural language processing, Computer science, Programming language theory
Related Organizations
Communities
Digital Humanities and Cultural Heritage
Download fromView all 2 versions
arXiv.org e-Print Archive
Preprint . 2017
Provider: arXiv.org e-Print Archive
http://arxiv.org/pdf/1705.0722...
Part of book or chapter of book
Provider: UnpayWall
https://link.springer.com/chap...
Part of book or chapter of book
Provider: Microsoft Academic Graph
http://link.springer.com/conte...
Part of book or chapter of book
Provider: Crossref

1. Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artificial intelligence 89(1-2), 1-29 (1996)

2. G¨ardenfors, P., Rott, H.: Handbook of logic in artificial intelligence and logic programming (vol. 4). chap. Belief Revision, pp. 35-132. Oxford University Press, Oxford, UK (1995)

3. Goldszmidt, M., Pearl, J.: Qualitative probabilities for default reasoning, belief revision, and causal modeling. Artificial Intelligence 84(1), 57-112 (1996) [OpenAIRE]

4. Goodman, N.D., Mansinghka, V.K., Roy, D.M., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: McAllester, D.A., Myllym¨aki, P. (eds.) UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9-12, 2008. pp. 220-229. AUAI Press (2008)

5. Halpern, J.Y.: Reasoning about uncertainty. MIT Press (2005)

6. Kozen, D.: Semantics of probabilistic programs. J. Comput. Syst. Sci. 22(3), 328- 350 (1981) [OpenAIRE]

7. Kyburg Jr, H.E.: Probability and the logic of rational belief (1961)

8. Mansinghka, V.K., Selsam, D., Perov, Y.N.: Venture: a higher-order probabilistic programming platform with programmable inference. CoRR abs/1404.0099 (2014) [OpenAIRE]

9. Pearl, J.: System Z: A natural ordering of defaults with tractable applications to nonmonotonic reasoning. In: Parikh, R. (ed.) Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge, Pacific Grove, CA, March 1990. pp. 121-135. Morgan Kaufmann (1990)

10. Pfeffer, A.: Figaro: An object-oriented probabilistic programming language. Charles River Analytics Technical Report 137 (2009)

11. Spohn, W.: The Laws of Belief - Ranking Theory and Its Philosophical Applications. Oxford University Press (2014)

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publication . Preprint . Part of book or chapter of book . 2017

RankPL: A Qualitative Probabilistic Programming Language

Tjitze Rienstra;

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