The Experts below are selected from a list of 210 Experts worldwide ranked by ideXlab platform
Luc De Raedt - One of the best experts on this subject based on the ideXlab platform.
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AKBC - Scalable Rule Learning in Probabilistic Knowledge Bases
2019Co-Authors: Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van Den Broeck, Luc De RaedtAbstract:Knowledge Bases (KBs) are becoming increasingly large, sparse and Probabilistic. These KBs are typically used to perform query inferences and Rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs. Probabilistic Rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given Probabilistic KB, it learns Probabilistic first-order Rules from its relations to identify interesting patterns. But, the current Probabilistic Rule learning techniques perform grounding to do Probabilistic inference for evaluation of candidate Rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to Probabilistic KB completion that performs Probabilistic Rule learning using lifted Probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art Probabilistic Rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard Probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple Rules and is also significantly faster than ProbFOIL+.
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scalable Rule learning in Probabilistic knowledge bases
Automated Knowledge Base Construction, 2019Co-Authors: Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van Den Broeck, Luc De RaedtAbstract:Knowledge Bases (KBs) are becoming increasingly large, sparse and Probabilistic. These KBs are typically used to perform query inferences and Rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs. Probabilistic Rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given Probabilistic KB, it learns Probabilistic first-order Rules from its relations to identify interesting patterns. But, the current Probabilistic Rule learning techniques perform grounding to do Probabilistic inference for evaluation of candidate Rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to Probabilistic KB completion that performs Probabilistic Rule learning using lifted Probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art Probabilistic Rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard Probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple Rules and is also significantly faster than ProbFOIL+.
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Relational Affordance Learning for Task-Dependent Robot Grasping
Inductive Logic Programming, 2018Co-Authors: Laura Antanas, Anton Dries, Plinio Moreno, Luc De RaedtAbstract:Robot grasping depends on the specific manipulation scenario: the object, its properties, task and grasp constraints. Object-task affordances facilitate semantic reasoning about pre-grasp configurations with respect to the intended tasks, favoring good grasps. We employ Probabilistic Rule learning to recover such object-task affordances for task-dependent grasping from realistic video data.
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ILP - Probabilistic Rule learning
Inductive Logic Programming, 2011Co-Authors: Luc De Raedt, Ingo ThonAbstract:Traditionally, Rule learners have learned deterministic Rules from deterministic data, that is, the Rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade Rule learning to a Probabilistic setting, in which both the examples themselves as well as their classification can be Probabilistic. The setting is incorporated in the Probabilistic Rule learner ProbFOIL, which combines the principles of the relational Rule learner FOIL with the Probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.status: publishe
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Probabilistic Rule learning
Inductive Logic Programming, 2010Co-Authors: Luc De Raedt, Ingo ThonAbstract:Traditionally, Rule learners have learned deterministic Rules from deterministic data, that is, the Rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade Rule learning to a Probabilistic setting, in which both the examples themselves as well as their classification can be Probabilistic. The setting is incorporated in the Probabilistic Rule learner ProbFOIL, which combines the principles of the relational Rule learner FOIL with the Probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.
Raymond J Mooney - One of the best experts on this subject based on the ideXlab platform.
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Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases
Connection Science, 1993Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:Abstract This paper describes RAPTURE—a system for revising Probabilistic knowledge bases that combines connectionist and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a Probabilistic Rule base and it uses ID3's information-gain heuristic to add new Rules. Results on refining three actual expert Rule bases demonstrate that this combined approach generally performs better than previous methods.
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combining neural and symbolic learning to revise Probabilistic Rule bases
Neural Information Processing Systems, 1992Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:This paper describes RAPTURE - a system for revising Probabilistic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style Rule base and uses ID3's information gain heuristic to add new Rules. Results on refining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.
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NIPS - Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
1992Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:This paper describes RAPTURE - a system for revising Probabilistic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style Rule base and uses ID3's information gain heuristic to add new Rules. Results on refining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.
J. J Mahoney - One of the best experts on this subject based on the ideXlab platform.
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Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases
Connection Science, 1993Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:Abstract This paper describes RAPTURE—a system for revising Probabilistic knowledge bases that combines connectionist and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a Probabilistic Rule base and it uses ID3's information-gain heuristic to add new Rules. Results on refining three actual expert Rule bases demonstrate that this combined approach generally performs better than previous methods.
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NIPS - Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
1992Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:This paper describes RAPTURE - a system for revising Probabilistic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style Rule base and uses ID3's information gain heuristic to add new Rules. Results on refining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.
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combining neural and symbolic learning to revise Probabilistic Rule bases
Neural Information Processing Systems, 1992Co-Authors: J. J Mahoney, Raymond J MooneyAbstract:This paper describes RAPTURE - a system for revising Probabilistic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style Rule base and uses ID3's information gain heuristic to add new Rules. Results on refining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.
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Combining Symbolic and Neural Learning to Revise Probabilistic Rule Bases
1992Co-Authors: J. J MahoneyAbstract:This paper describes RAPTURE -- a system for revising Probabilistic Rule bases that combines symbolic and neural-network learning methods. RAPTURE uses a modified version of back- propagation to refine the certainty factors of a MYCIN-style Rule base and it uses ID''s information gain heuristic to add new Rules. Current results on two real-world domains are presented, demonstrating that this combined approach performs as well or better than previous methods. Future work for this project is discussed, which includes further testing on other domains, as well as experimentation with several network training techniques. Possible extensions to this project include using different Probabilistic formalisms for the Rule base such as Bayes Nets, Dempster- Schafer theory, and Fuzzy-Logic.
Hiroshi Tanaka - One of the best experts on this subject based on the ideXlab platform.
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An isotropic cellular automaton for excitable media
Physica A: Statistical Mechanics and its Applications, 2008Co-Authors: Akihiro Nishiyama, Hiroshi Tanaka, Tetsuji TokihiroAbstract:Abstract We propose a new cellular automaton (CA) model, which reproduces isotropic time-evolution patterns observed in the Belousov–Zhabotinsky reaction. Although several CA models have been proposed exhibiting isotropic patterns of the reaction, most of them need complicated Rules and a large number of neighboring cells. Our model can produce isotropic patterns from a simple Probabilistic Rule among a few (4 or 8) neighboring cells.
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primerose Probabilistic Rule induction method based on rough sets and resampling methods
Computational Intelligence, 1995Co-Authors: Shusaku Tsumoto, Hiroshi TanakaAbstract:Automated knowledge acquisition is an important research issue in machine learning. Several methods of inductive learning, such as ID3 family and AQ family, have been applied to discover meaningful knowledge from large databases and their usefulness is assured in several aspects. However, since their methods are of a deterministic nature and the reliability of acquired knowledge is not evaluated statistically, these methods are ineffective when applied to domains essentially Probabilistic in nature, such as medical domains. Extending concepts of rough set theory to a Probabilistic domain, we introduce a new approach to knowledge acquisition, which induces Probabilistic Rules based on rough set theory (PRIMEROSE) and develop a program that extracts Rules for an expert system from a clinical database, using this method. The results show that the derived Rules almost correspond to those of the medical experts.
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RSKD - PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory
Rough Sets Fuzzy Sets and Knowledge Discovery, 1994Co-Authors: Shusaku Tsumoto, Hiroshi TanakaAbstract:Automated knowledge acquisition is an important research issue in machine learning. There have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large database, and their usefulness is in some aspects ensured. However, in most of the cases, their methods are of deterministic nature, and reliability of the acquired knowledge is not evaluated statistically, which makes these methods ineffective when applied to the domain of essentially Probabilistic nature, such as medical one. Extending concepts of rough set theory to Probabilistic domain, we introduce a new approach to knowledge acquisition, which induces Probabilistic Rules based on rough set theory(PRIMEROSE) and develop a program that extracts Rules for an expert system from clinical database, using this method. The results show that the derived Rules almost correspond to those of medical experts.
Shusaku Tsumoto - One of the best experts on this subject based on the ideXlab platform.
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GrC - Probabilistic Rule induction based on incremental sampling scheme
2014 IEEE International Conference on Granular Computing (GrC), 2014Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:This paper proposes a new framework for Rule induction methods based on incremental sampling scheme and Rule layers constrained by inequalities of accuracy and coverage. Incremental sampling scheme shows that the number of patterns of updates of accuracy and coverage is four, which give two important inequalities of accuracy and coverage for induction of Probabilistic Rules. By using these two inequalities, the proposed method classifies a set of formulae into four layers: the Rule layer, subRule layer (in and out) and the non-Rule layer. Using these layers, updates of Probabilistic Rules are equivalent to their movement between layers. The proposed method was evaluated on datasets regarding headaches and meningitis, and the results show that the proposed method outperforms the conventional methods.
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IEEE ICCI - Incremental discovery of Probabilistic Rules from clinical databases based on rough set theory
9th IEEE International Conference on Cognitive Informatics (ICCI'10), 2010Co-Authors: Shusaku TsumotoAbstract:Extending concepts of Rule induction methods based on rough set theory, we introduce a new approach to knowledge acquistion, which induces Probabilistic Rules incrementally, called PRIMEROSE-INC (Probabilistic Rule Induction Method based on Rough Sets for Incremental Learning Methods). This method first uses coverage rather than accuracy, to search for the candidates of Rules, and secondly uses accuracy to select from the candidates. This system was evaluated on clinical databases on headache and meningitis. The results show that PRIMEROSE-INC induces the same Rules as those induced by PRIMEROSE, which extracts Rules from all the datasets, but that the former method requires much computational resources than the latter approach.
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primerose Probabilistic Rule induction method based on rough sets and resampling methods
Computational Intelligence, 1995Co-Authors: Shusaku Tsumoto, Hiroshi TanakaAbstract:Automated knowledge acquisition is an important research issue in machine learning. Several methods of inductive learning, such as ID3 family and AQ family, have been applied to discover meaningful knowledge from large databases and their usefulness is assured in several aspects. However, since their methods are of a deterministic nature and the reliability of acquired knowledge is not evaluated statistically, these methods are ineffective when applied to domains essentially Probabilistic in nature, such as medical domains. Extending concepts of rough set theory to a Probabilistic domain, we introduce a new approach to knowledge acquisition, which induces Probabilistic Rules based on rough set theory (PRIMEROSE) and develop a program that extracts Rules for an expert system from a clinical database, using this method. The results show that the derived Rules almost correspond to those of the medical experts.
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RSKD - PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory
Rough Sets Fuzzy Sets and Knowledge Discovery, 1994Co-Authors: Shusaku Tsumoto, Hiroshi TanakaAbstract:Automated knowledge acquisition is an important research issue in machine learning. There have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large database, and their usefulness is in some aspects ensured. However, in most of the cases, their methods are of deterministic nature, and reliability of the acquired knowledge is not evaluated statistically, which makes these methods ineffective when applied to the domain of essentially Probabilistic nature, such as medical one. Extending concepts of rough set theory to Probabilistic domain, we introduce a new approach to knowledge acquisition, which induces Probabilistic Rules based on rough set theory(PRIMEROSE) and develop a program that extracts Rules for an expert system from clinical database, using this method. The results show that the derived Rules almost correspond to those of medical experts.