An Approximate Algorithm for Min-Based Possibilistic Networks
Min‐based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min‐based networks, which consists of propagating information through the network structure to answer queries. Exact inferenc...
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| Veröffentlicht in: | International journal of intelligent systems Jg. 29; H. 7; S. 615 - 633 |
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Hoboken, NJ
Blackwell Publishing Ltd
01.07.2014
Wiley John Wiley & Sons, Inc |
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| ISSN: | 0884-8173, 1098-111X |
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| Abstract | Min‐based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min‐based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min‐based possibilistic networks. More precisely, we adapt the well‐known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP. |
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| AbstractList | Min-based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min-based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min-based possibilistic networks. More precisely, we adapt the well-known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP. Min-based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min-based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min-based possibilistic networks. More precisely, we adapt the well-known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP. [PUBLICATION ABSTRACT] |
| Author | Ajroud, Amen Benferhat, Salem |
| Author_xml | – sequence: 1 givenname: Amen surname: Ajroud fullname: Ajroud, Amen email: amen.ajroud@isetso.rnu.tn organization: PRINCE - ISITCOM, Université de Sousse, Hammam Sousse, Tunisia – sequence: 2 givenname: Salem surname: Benferhat fullname: Benferhat, Salem organization: CRIL - CNRS UMR 8188, Université d'Artois, Lens, France |
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| References | Darwiche A. Modeling and reasoning with Bayesian networks, 1st edition. Cambridge, UK: Cambridge University Press; 2009. Peot MA, Shachter RD. Fusion and propagation with multiple observations in belief networks. Artif Intell 1991;48:299-318. Chavira M, Darwiche A, Jaeger M. Compiling relational Bayesian networks for exact inference. Int J Approx Reasoning 2006;42(1,2):4-20. Cooper GF. Computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 1990; pp. 393-405. Cadoli M, Donini FM. A survey on knowledge compilation. AI Commun 1998;10(3,4):137-150. Jaakkola TS, Jordan MI. Variational probabilistic inference and the qmr dt network. J Artif Intell Res 1999;10:291-322. Ayachi R, Ben Amor N, Benferhat S. Inference using compiled product-based possibilistic networks. Inf Process Manage Uncertainty 2013;470-480. Lauritzen SL, Spiegelhalter DJ. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B 1988;50:157-224. Geman S, Geman D. Stochastic relaxations, gibbs distributions and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984;6(6):721-742. Weiss Y. Correctness of local probability propagation in graphical models with loops. Neural Comput 2000;12(1):1-41. Pearl J. Fusion, propagation and structuring in belief networks. Artif Intell 1986;29:241-288. Ben Amor N, Benferhat S, Mellouli K. Anytime propagation algorithm for min-based possibilistic graphs. Soft Comput 2003;8:150-161. Mittal A. Bayesian network technologies: applications and graphical models. Hershey, PA: IGI Publishing; 2007. Pearl J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann; 1988. Krishna Prasad PESN, Madhavi K, Prasad BDCN. Representation of uncertain data using possibilistic network models. Comput Sci Inf Technol, 2012; pp. 267-278. Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in Practice. London: Chapman and Hall/CRC; 1996. McEliece RJ, MacKay DJC, Cheng J-F. Turbo decoding as an instance of Pearl's "belief propagation" algorithm. In: IEEE Journal on Selected Areas in Communication, Berlin, Germany: Springer-Verlag; 1998;16(2):140-152. Borgelt C, Kruse R. Graphical models: methods for data analysis and mining. New York: Wiley; 2002. Cowell RG, Dawid PA, Lauritzen SL, Spiegelhalter DJ. Probabilistic networks and expert systems (Information science and statistics). New York: Springer; 2003. 2012 2011 2010 2009 1997 2008 2007 1996 1994 1988; 50 2003 2002 1999 1998; 16 2006; 42 1991; 48 1990 2000; 12 2003; 8 1984; 6 1986; 29 1999; 10 1983 2013 1998; 10 1989 1988 |
| References_xml | – reference: Mittal A. Bayesian network technologies: applications and graphical models. Hershey, PA: IGI Publishing; 2007. – reference: Pearl J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann; 1988. – reference: Darwiche A. Modeling and reasoning with Bayesian networks, 1st edition. Cambridge, UK: Cambridge University Press; 2009. – reference: Lauritzen SL, Spiegelhalter DJ. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B 1988;50:157-224. – reference: Chavira M, Darwiche A, Jaeger M. Compiling relational Bayesian networks for exact inference. Int J Approx Reasoning 2006;42(1,2):4-20. – reference: Cooper GF. Computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 1990; pp. 393-405. – reference: Pearl J. Fusion, propagation and structuring in belief networks. Artif Intell 1986;29:241-288. – reference: Ben Amor N, Benferhat S, Mellouli K. Anytime propagation algorithm for min-based possibilistic graphs. Soft Comput 2003;8:150-161. – reference: Cowell RG, Dawid PA, Lauritzen SL, Spiegelhalter DJ. Probabilistic networks and expert systems (Information science and statistics). New York: Springer; 2003. – reference: Cadoli M, Donini FM. A survey on knowledge compilation. AI Commun 1998;10(3,4):137-150. – reference: Weiss Y. Correctness of local probability propagation in graphical models with loops. Neural Comput 2000;12(1):1-41. – reference: Jaakkola TS, Jordan MI. Variational probabilistic inference and the qmr dt network. J Artif Intell Res 1999;10:291-322. – reference: Krishna Prasad PESN, Madhavi K, Prasad BDCN. Representation of uncertain data using possibilistic network models. Comput Sci Inf Technol, 2012; pp. 267-278. – reference: McEliece RJ, MacKay DJC, Cheng J-F. Turbo decoding as an instance of Pearl's "belief propagation" algorithm. In: IEEE Journal on Selected Areas in Communication, Berlin, Germany: Springer-Verlag; 1998;16(2):140-152. – reference: Borgelt C, Kruse R. Graphical models: methods for data analysis and mining. New York: Wiley; 2002. – reference: Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in Practice. London: Chapman and Hall/CRC; 1996. – reference: Peot MA, Shachter RD. Fusion and propagation with multiple observations in belief networks. Artif Intell 1991;48:299-318. – reference: Geman S, Geman D. Stochastic relaxations, gibbs distributions and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984;6(6):721-742. – reference: Ayachi R, Ben Amor N, Benferhat S. Inference using compiled product-based possibilistic networks. Inf Process Manage Uncertainty 2013;470-480. – year: 2009 – volume: 50 start-page: 157 year: 1988 end-page: 224 article-title: Local computations with probabilities on graphical structures and their application to expert systems publication-title: Journal of the Royal Statistical Society, Series B – start-page: 470 year: 2013 end-page: 480 article-title: Inference using compiled product‐based possibilistic networks publication-title: Inf Process Manage Uncertainty – year: 1994 article-title: Réseaux d'inférence pour le raisonnement possibiliste – year: 2007 – start-page: 267 year: 2012 end-page: 278 article-title: Representation of uncertain data using possibilistic network models publication-title: Comput Sci Inf Technol – year: 2003 – year: 1996 – volume: 8 start-page: 150 year: 2003 end-page: 161 article-title: Anytime propagation algorithm for min‐based possibilistic graphs publication-title: Soft Comput – volume: 42 start-page: 4 issue: 1,2 year: 2006 end-page: 20 article-title: Compiling relational Bayesian networks for exact inference publication-title: Int J Approx Reasoning – start-page: 467 year: 1999 end-page: 475 – volume: 29 start-page: 241 year: 1986 end-page: 288 article-title: Fusion, propagation and structuring in belief networks publication-title: Artif Intell – start-page: 108 year: 1997 end-page: 121 article-title: Background and perspectives of possibilistic graphical models, qualitative and quantitative practical reasoning (ECSQARU/FAPR'97) – year: 2013 article-title: A generic framework for a compilation‐based inference in probabilistic and possibilistic networks – start-page: 190 year: 1983 end-page: 193 – volume: 12 start-page: 1 issue: 1 year: 2000 end-page: 41 article-title: Correctness of local probability propagation in graphical models with loops publication-title: Neural Comput – year: 2010 article-title: Compiling possibilistic networks: alternative approaches to possibilistic inference – year: 2002 – year: 1988 – volume: 6 start-page: 721 issue: 6 year: 1984 end-page: 742 article-title: Stochastic relaxations, gibbs distributions and the bayesian restoration of images publication-title: IEEE Trans Pattern Anal Mach Intell – year: 1989 article-title: Bounded conditioning: Flexible inference for decisions under scarce resources – start-page: 393 year: 1990 end-page: 405 article-title: Computational complexity of probabilistic inference using Bayesian belief networks publication-title: Artif Intell – start-page: 329 year: 2003 end-page: 338 – start-page: 321 year: 2008 end-page: 326 article-title: An approximate propagation algorithm for product‐based possibilistic networks – start-page: 1 year: 2011 end-page: 6 – volume: 10 start-page: 137 issue: 3,4 year: 1998 end-page: 150 article-title: A survey on knowledge compilation publication-title: AI Commun – volume: 48 start-page: 299 year: 1991 end-page: 318 article-title: Fusion and propagation with multiple observations in belief networks publication-title: Artif Intell – volume: 16 start-page: 140 issue: 2 year: 1998 end-page: 152 article-title: Turbo decoding as an instance of Pearl's “belief propagation” algorithm publication-title: IEEE Journal on Selected Areas in Communication, Berlin, Germany: Springer‐Verlag – volume: 10 start-page: 291 year: 1999 end-page: 322 article-title: Variational probabilistic inference and the qmr dt network publication-title: J Artif Intell Res |
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| SubjectTerms | Algorithms Applied sciences Approximation Computer science; control theory; systems Convergence Exact sciences and technology Inference Information retrieval. Graph Intelligent systems Networks Propagation Queries Tasks Theoretical computing |
| Title | An Approximate Algorithm for Min-Based Possibilistic Networks |
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