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33
Using Datalog with binary decision diagrams for program analysis
 In Proceedings of Programming Languages and Systems: Third Asian Symposium
, 2005
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Toward a Highperformance System for Symbolic and Statistical Modeling
"... We present in this paper a stateoftheart implementation of PRISM, a language based on Prolog that supports statistical modeling and learning. We start with an interpreter of the language that incorporates a naive learning algorithm, and then turn to improve the interpreter. One of the improv ..."
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Cited by 16 (12 self)
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We present in this paper a stateoftheart implementation of PRISM, a language based on Prolog that supports statistical modeling and learning. We start with an interpreter of the language that incorporates a naive learning algorithm, and then turn to improve the interpreter. One of the improvements is to refine the learning algorithm such that it works on explanation graphs rather than flat explanations.
New advances in logicbased probabilistic modeling by PRISM
 Probabilistic Inductive Logic Programming
, 2008
"... Abstract. We review a logicbased modeling language PRISM and report recent developments including belief propagation by the generalized insideoutside algorithm and generative modeling with constraints. The former implies PRISM subsumes belief propagation at the algorithmic level. We also compare t ..."
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Cited by 16 (6 self)
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Abstract. We review a logicbased modeling language PRISM and report recent developments including belief propagation by the generalized insideoutside algorithm and generative modeling with constraints. The former implies PRISM subsumes belief propagation at the algorithmic level. We also compare the performance of PRISM with stateoftheart systems in statistical natural language processing and probabilistic inference in Bayesian networks respectively, and show that PRISM is reasonably competitive. 1
CONTEXTSENSITIVE POINTER ANALYSIS USING BINARY DECISION DIAGRAMS
, 2007
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Foundations of Modal Logic Programming: The Direct Approach (release 2.0)”, manuscript (provided as a technical report), available at http://www.mimuw.edu. pl/~nguyen/papers.html
"... 1.1 Classical Logic Programming............................ 5 1.2 Previous Works on Modal Logic Programming.................. 7 ..."
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Cited by 5 (5 self)
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1.1 Classical Logic Programming............................ 5 1.2 Previous Works on Modal Logic Programming.................. 7
Generalizing the QSQR evaluation method for Horn knowledge bases
 New Challenges in Applied Intelligence Technologies, volume 134 of Studies in Computational Intelligence
, 2008
"... Abstract. We generalize the QSQR evaluation method to give a setoriented depthfirst evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLDresolution (to take advantages of the goaldirected approach) and highly exploits setatatime tabling. Our generalized QSQR ..."
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Cited by 2 (2 self)
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Abstract. We generalize the QSQR evaluation method to give a setoriented depthfirst evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLDresolution (to take advantages of the goaldirected approach) and highly exploits setatatime tabling. Our generalized QSQR evaluation procedure is sound, complete, and tight. It does not use adornments and annotations. To deal with function symbols, our procedure uses iterative deepening search which iteratively increases termdepth bound for atoms occurring in the computation. When the termdepth bound is fixed, our evaluation procedure runs in polynomial time in the size of extensional relations. 1
PRISM User’s Manual (Version 2.0)
"... The past several years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, proba ..."
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The past several years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logicbased probabilistic learning has found its way into many application areas including bioinformatics, diagnosis and troubleshooting, stochastic language processing, information retrieval, linkage analysis and discovery, robot control, and probabilistic constraint solving. PRISM (PRogramming In Statistical Modeling) is a logicbased language that integrates logic programming and probabilistic reasoning including parameter learning. It allows for the description of independent probabilistic choices and their consequences in general logic programs. PRISM supports parameter learning, i.e. for a given set of possibly incomplete observed data, PRISM can estimate the probability distributions to best explain the data. This power is suitable for applications such as learning parameters of stochastic grammars, training stochastic models for gene sequence analysis, game record analysis, user modeling, and obtaining probabilistic information for tuning systems performance. PRISM offers incomparable flexibility compared with specific statistical tools such as hidden Markov models (HMMs) [4, 30], probabilistic context free grammars (PCFGs) [4] and discrete Bayesian networks. PRISM employs a prooftheoretic approach to learning. It conducts learning in two phases: the first phase searches for all the explanations for the observed data, and the second phase estimates the probability distributions by using the EM algorithm. Learning from flat explanations can be exponential in both space and time. To speed up learning, the authors proposed learning from explanation graphs and using tabling to reduce redundancy in the construction of explanation graphs. The PRISM programming system is implemented on top of BProlog
PRISM User’s Manual (Version 1.12.1)
"... The past several years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, proba ..."
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The past several years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logicbased probabilistic learning has found its way into many application areas including bioinformatics, diagnosis and troubleshooting, stochastic language processing, information retrieval, linkage analysis and discovery, robot control, and probabilistic constraint solving. PRISM (PRogramming In Statistical Modeling) is a logicbased language that integrates logic programming and probabilistic reasoning including parameter learning. It allows for the description of independent probabilistic choices and their consequences in general logic programs. PRISM supports parameter learning, i.e. for a given set of possibly incomplete observed data, PRISM can estimate the probability distributions to best explain the data. This power is suitable for applications such as learning parameters of stochastic grammars, training stochastic models for gene sequence analysis, game record analysis, user modeling, and obtaining probabilistic information for tuning systems performance. PRISM offers incomparable flexibility compared with specific statistical tools such as hidden Markov models (HMMs) [4, 30], probabilistic context free grammars (PCFGs) [4] and discrete Bayesian networks. PRISM employs a prooftheoretic approach to learning. It conducts learning in two phases: the first phase searches for all the explanations for the observed data, and the second phase estimates the probability distributions by using the EM algorithm. Learning from flat explanations can be exponential in both space and time. To speed up learning, the authors proposed learning from explanation graphs and using tabling to reduce redundancy in the construction of explanation graphs. The PRISM programming system is implemented on top of BProlog
PRISM User’s Manual (Version 1.11)
"... The past few years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabili ..."
Abstract
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The past few years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logicbased probabilistic learning has found its way into many application areas including bioinformatics, diagnosis and troubleshooting, stochastic language processing, information retrieval, linkage analysis and discovery, robot control, and probabilistic constraint solving. PRISM (PRogramming In Statistical Modeling) is a logicbased language that integrates logic programming and probabilistic reasoning including parameter learning. It allows for the description of independent probabilistic choices and their consequences in general logic programs. PRISM supports parameter learning, i.e. for a given set of possibly incomplete observed data, PRISM can estimate the probability distributions to best explain the data. This power is suitable for applications such as learning parameters of stochastic grammars, training stochastic models for gene sequence analysis, game record analysis, user modeling, and obtaining probabilistic information for tuning systems performance. PRISM offers incomparable flexibility compared with specific statistical tools such as hidden Markov models (HMMs) [4, 25], probabilistic context free grammars (PCFGs) [4] and discrete Bayesian networks. PRISM employs a prooftheoretic approach to learning. It conducts learning in two phases: the first phase searches for all the explanations for the observed data, and the second phase estimates the probability distributions by using the EM algorithm. Learning from flat explanations can be exponential in both space and time. To speed up learning, the authors proposed learning from explanation graphs and using tabling to reduce redundancy in the construction of explanation graphs. The PRISM programming system is implemented on top of BProlog
PRISM User’s Manual (Version 1.10)
"... The past few years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabili ..."
Abstract
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The past few years have witnessed a tremendous interest in logicbased probabilistic learning as testified by the number of formalisms and systems and their applications. Logicbased probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logicbased probabilistic learning has found its way into many application areas including bioinformatics, diagnosis and troubleshooting, stochastic language processing, information retrieval, linkage analysis and discovery, robot control, and probabilistic constraint solving. PRISM (PRogramming In Statistical Modeling) is a logicbased language that integrates logic programming and probabilistic reasoning including parameter learning. It allows for the description of independent probabilistic choices and their consequences in general logic programs. PRISM supports parameter learning, i.e. for a given set of possibly incomplete observed data, PRISM can estimate the probability distributions to best explain the data. This power is suitable for applications such as learning parameters of stochastic grammars, training stochastic models for gene sequence analysis, game record analysis, user modeling, and obtaining probabilistic information for tuning systems performance. PRISM offers incomparable flexibility compared with specific statistical tools such as hidden Markov models (HMMs) [2, 14], probabilistic context free grammars (PCFGs) [2] and discrete Bayesian networks. PRISM employs a prooftheoretic approach to learning. It conducts learning in two phases: the first phase searches for all the explanations for the observed data, and the second phase estimates the probability distributions by using the EM algorithm. Learning from flat explanations can be exponential in both space and time. To speed up learning, the authors proposed learning from explanation graphs and using tabling to reduce redundancy in the construction of explanation graphs. The PRISM programming system is implemented on top of BProlog