the logic FO(ID) extends classical first order logic withinductive definitions. this paper studies the satisifiability problem for PC(ID), its propositional fragment. We develop a framework for model generation in th...
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ISBN:
(纸本)354030553X
the logic FO(ID) extends classical first order logic withinductive definitions. this paper studies the satisifiability problem for PC(ID), its propositional fragment. We develop a framework for model generation in this logic, present an algorithm and prove its correctness. As FO(ID) is an integration of classical logic and logicprogramming, our algorithm integrates techniques from SAT and ASP. We report on a prototype system, called MIDL, experimentally validating our approach.
Many inductive systems, including ILP systems, learn from a knowledge base that is structured around examples. In practical situations this example-centered representation can cause a lot of redundancy. For instance, ...
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ISBN:
(纸本)3540005676
Many inductive systems, including ILP systems, learn from a knowledge base that is structured around examples. In practical situations this example-centered representation can cause a lot of redundancy. For instance, when learning from episodes (e.g. from games), the knowledge base contains consecutive states of a world. Each state is usually described completely even though consecutive states may differ only slightly. Similar redundancies occur when the knowledge base stores examples that share common structures (e.g. when representing complex objects as machines or molecules). these two types of redundancies can place a heavy burden on memory resources. In this paper we propose a method for representing knowledge bases in a more efficient way. this is accomplished by building a graph that implicitly defines examples in terms of other structures. We evaluate our method in the context of learning a Go heuristic.
We show how several novel tools in logicprogramming for AI (namely continuation-based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processin...
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ISBN:
(纸本)076950910X
We show how several novel tools in logicprogramming for AI (namely continuation-based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept we present a concise parser for datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words) that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parser's application: complete constituent coordination, and error diagnosis and correction.
We introduce an inductivelogicprogramming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and su...
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ISBN:
(纸本)9781577358763
We introduce an inductivelogicprogramming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.
Sequence mining is an active research field of data mining because algorithms designed in that domain lead to various valuable applications. To increase efficiency of basic sequence mining algorithms, generally based ...
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ISBN:
(纸本)3540005676
Sequence mining is an active research field of data mining because algorithms designed in that domain lead to various valuable applications. To increase efficiency of basic sequence mining algorithms, generally based on a levelwise approach, more recent algorithms try to introduce some constraints to prune the search space during the discovery process. Nevertheless, existing algorithms are actually limited to extract frequent sequences made up of items of a database. In this paper, we generalize the notion of sequence to define what we call logical sequence where each element of a sequence may contain some logical variables. then we show how we can extend constrained sequence mining to constrained frequent logical sequence mining(1).
We present an attempt to engage social media networks, bringing the crowdsourcing model into mobile environments. We introduce logic Crowd, a declarative programming paradigm for mobile crowdsourcing applications, dev...
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ISBN:
(纸本)9780769550220
We present an attempt to engage social media networks, bringing the crowdsourcing model into mobile environments. We introduce logic Crowd, a declarative programming paradigm for mobile crowdsourcing applications, developed as an extension of Prolog. logic Crowd aims at filling the gap between traditional machine computation, which operates upon its database, and social media, which is capable of interacting with real people. In this paper, we illustrate the potential of our approach via programming idioms, a prototype implementation and scenarios.
this paper presents a Knowledge Base prospect for FO(ID), in extension of classical logic withinductive definitions. this logic is a natural integration of classical logic and logicprogramming based on the view of a...
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ISBN:
(纸本)9783540899815
this paper presents a Knowledge Base prospect for FO(ID), in extension of classical logic withinductive definitions. this logic is a natural integration of classical logic and logicprogramming based on the view of a logic program as a definition. We discuss the relationship between inductive definitions and common sense reasoning and the strong similarities mid striking differences with ASP and Abductive LP. We report on inference systems that combine state-of-the-art techniques of SAT and ASP. Experiments show that FO(ID) model expansion systems are competitive withthe best ASP-solvers.
Recent work on neuro-symbolic inductivelogicprogramming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with different...
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ISBN:
(纸本)9781577358763
Recent work on neuro-symbolic inductivelogicprogramming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logicthat are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned "rules". In this paper, we propose learning rules withthe recently proposed logical neural networks (LNN). Compared to others, LNNs offer a strong connection to classical Boolean logicthus allowing for precise interpretation of learned rules while harboring parameters that can be trained with gradient-based optimization to effectively fit the data. We extend LNNs to induce rules in first-order logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable and can achieve comparable or higher accuracy due to their flexible parameterization.
We address the problem of belief revision of logic programs (LPs), i.e., how to incorporate to a LP P a new LP Q. Based on the structure of SE interpretations, Delgrande et al. (2008. Proc. of the 11thinternational C...
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We address the problem of belief revision of logic programs (LPs), i.e., how to incorporate to a LP P a new LP Q. Based on the structure of SE interpretations, Delgrande et al. (2008. Proc. of the 11thinternationalconference on Principles of Knowledge Representation and Reasoning (KR'08), 411-421;2013b. Proc. of the 12thinternationalconference on logicprogramming and Nonmonotonic Reasoning (LPNMR'13), 264-276) adapted the well-known AGM framework (Alchourron et al. 1985. Journal of Symbolic logic 50, 2, 510-530) to LP revision. they identified the rational behavior of LP revision and introduced some specific operators. In this paper, a constructive characterization of all rational LP revision operators is given in terms of orderings over propositional interpretations with some further conditions specific to SE interpretations. It provides an intuitive, complete procedure for the construction of all rational LP revision operators and makes easier the comprehension of their semantic and computational properties. We give a particular consideration to LPs of very general form, i.e., the generalized logic programs (GLPs). We show that every rational GLP revision operator is derived from a propositional revision operator satisfying the original AGM postulates. Interestingly, the further conditions specific to GLP revision are independent from the propositional revision operator on which a GLP revision operator is based. Taking advantage of our characterization result, we embed the GLP revision operators into structures of Boolean lattices, that allow us to bring to light some potential weaknesses in the adapted AGM postulates. To illustrate our claim, we introduce and characterize axiomatically two specific classes of (rational) GLP revision operators which arguably have a drastic behavior. We additionally consider two more restricted forms of LPs, i.e., the disjunctive logic programs (DLPs) and the normal logic programs (NLPs) and adapt our characterization result t
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