In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that J = T-P (I), where T-P is the immediate conse...
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ISBN:
(数字)9783319237084
ISBN:
(纸本)9783319237084;9783319237077
In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that J = T-P (I), where T-P is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.
Robustness in task execution requires tight integration of continual planning, monitoring, reasoning and learning processes. In this paper, we investigate how robustness can be ensured by learning from experience. Our...
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ISBN:
(纸本)9781467375092
Robustness in task execution requires tight integration of continual planning, monitoring, reasoning and learning processes. In this paper, we investigate how robustness can be ensured by learning from experience. Our approach is based on a learning guided planning process for a robot that gains its experience from action execution failures through lifelong experiential learning. inductive logic programming (ILP) is used as the learning method to frame hypotheses for failure situations. It provides first-order logic representation of the robot's experience. The robot uses this experience to construct heuristics to guide its future decisions. The performance of the learning guided planning process is analyzed on our Pioneer 3-AT robot. The results reveal that the hypotheses framed for failure cases are sound and ensure safety and robustness in future tasks of the robot.
Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe c...
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Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J logic Program 19-20:629-679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.
inductive logic programming (ILP) is one of the most popular approaches in machine learning. ILP can be used to automate the construction of first-order definite clause theories from examples and background knowledge....
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inductive logic programming (ILP) is one of the most popular approaches in machine learning. ILP can be used to automate the construction of first-order definite clause theories from examples and background knowledge. Although ILP has been successfully applied in various domains, it has the following weaknesses: (1) weak capabilities in numerical data processing. (2) zero noise tolerance, and (3) unsatisfactory learning time with a large number of arguments in the relation. This paper proposes a phenotypic genetic algorithm (PGA) to overcome these weaknesses. To strengthen the numerical data processing capabilities, a multiple level encoding structure is used that can represent three different types of relationships between two numerical data. To tolerate noise, PGA's goal of finding perfect rules is changed to finding top-k rules, which allows noise in the induction process. Finally, to shorten learning time, we incorporate the semantic roles constraint into PGA, reducing search space and preventing the discovery of infeasible rules. Stock trading data from Yahool Finance Online is used in our experiments. The results indicate that the PGA algorithm can find interesting trading rules from real data. (c) 2008 Elsevier Ltd. All rights reserved.
inductive logic programming (ILP) is a sub-field of machine learning that provides an excellent framework for multi-relational data mining applications. The advantages of ILP have been successfully demonstrated in com...
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inductive logic programming (ILP) is a sub-field of machine learning that provides an excellent framework for multi-relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logicprogramming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well-known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright (C) 2008 John Wiley & Sons, Ltd.
Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency...
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ISBN:
(纸本)9781424445387
Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs inductive logic programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%
Grid computing systems are extremely large and complex so, manually dealing with its failures becomes impractical. Recently, it has been proposed that the systems themselves should manage their own failures or malfunc...
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ISBN:
(纸本)9781424450824
Grid computing systems are extremely large and complex so, manually dealing with its failures becomes impractical. Recently, it has been proposed that the systems themselves should manage their own failures or malfunctions. This is referred as self-healing. To deal with this challenging, is required to predict and control the process through a number of automated learning and proactive actions. In this paper, we proposed inductive logic programming, a relational machine learning method, for prediction and root causal analysis that makes it possible the development of a self-healing component.
Wireless Sensor Networks (WSNs) are a promising technology to monitor distant or inaccessible areas. As nodes have a limited energy supply, many routing protocols are based on a clustering mechanism: some nodes are el...
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ISBN:
(纸本)9781509004799
Wireless Sensor Networks (WSNs) are a promising technology to monitor distant or inaccessible areas. As nodes have a limited energy supply, many routing protocols are based on a clustering mechanism: some nodes are elected as cluster heads and have to deal with most of the communication burden of the network, while the other nodes perform only simple operations. In this paper, we propose a new election mechanism with important features: it ensures that all nodes are in range of a cluster head while keeping the number of cluster heads low, it takes into account the residual energy of nodes, and it requires a small communication overhead. We compare the performance of our election mechanism with an optimal election, as well as with the election mechanism of LEACH, which is the main clustering algorithm for WSNs.
We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the...
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ISBN:
(纸本)9783642024801
We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the clusters. Furthermore, it can be used as a knowledge elicitation tool to explain clusters proposed by other clustering approaches, such as standard phylogenetic programs.
This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Define...
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This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.
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