the generation and testing of hypotheses is widely considered to be the primary method by which Science progresses. So much so, that it is still common to find a scientific proposal or an intellectual argument damned ...
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ISBN:
(纸本)3540229418
the generation and testing of hypotheses is widely considered to be the primary method by which Science progresses. So much so, that it is still common to find a scientific proposal or an intellectual argument damned on the grounds that it has no hypothesis being tested, it is merely a fishing expedition, and so on. Extreme versions run if there is no hypothesis, it is not Science, the clear implication being that hypothesis-driven programmes (as opposed to data-driven studies) are the only contributor to the scientific endeavour. this misrepresents how knowledge and understanding are actually generated from the study of natural phenomena and laboratory experiments. Hypothesis-driven and inductive modes of reasoning are not competitive, but complementary, and both are required in post-genomic biology.
Road curb detection and tracking is essential for the autonomous driving of intelligent vehicles on highways and urban roads. In this paper, we present a fast and robust road curb detection algorithm using 3D lidar da...
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ISBN:
(纸本)9781467327435;9781467327428
Road curb detection and tracking is essential for the autonomous driving of intelligent vehicles on highways and urban roads. In this paper, we present a fast and robust road curb detection algorithm using 3D lidar data and Integral Laser Points (ILP) features. Range and intensity data of the 3D lidar is decomposed into elevation data and data projected on the ground plane. First, left and right road curbs are detected for each scan line using the ground projected range and intensity data and line segment features. then, curb points of each scan line are determined using elevation data. the ILP features are proposed to speed up the both detection procedures. Finally, parabola model and RANSAC algorithm is used to fit the left and right curb points and generate vehicle controlling parameters. the proposed method and feature provide fast and reliable road curb detection speed and performance. Experiments show good results on various highways and urban roads under different situations.
the proceedings contain 62 papers. the topics discussed include: recognizing modern Japanese magazines by combining deep learning with language models;mining high utility sequences with a novel utility function;fully ...
ISBN:
(纸本)9781665499750
the proceedings contain 62 papers. the topics discussed include: recognizing modern Japanese magazines by combining deep learning with language models;mining high utility sequences with a novel utility function;fully automated machine learning pipeline for echocardiogram segmentation;prioritizing automated test cases of web applications using reinforcement learning: an enhancement;design of an GIS-based investment heatmap system using topic classification and NER;on consistency of redundancy deduction of linguistic fuzzy rules;towards expert-guided elucidation of cyber-attacks through interactive inductivelogicprogramming;esports game updates and player perception: data analysis of PUBG steam reviews;resolving inconsistencies in probabilistic knowledge bases by quantitative modification;and improving graph convolutional networks with transformer layer in social-based items recommendation.
the K framework is a rewrite logic-based framework for defining programming language semantics suitable for formal reasoning about programs and programming languages. In this paper, we present K-Taint, a rewriting log...
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ISBN:
(纸本)9789897583001
the K framework is a rewrite logic-based framework for defining programming language semantics suitable for formal reasoning about programs and programming languages. In this paper, we present K-Taint, a rewriting logic-based executable semantics in the K framework for taint analysis of an imperative programming language. Our K semantics can be seen as a sound approximation of programs semantics in the corresponding security type domain. More specifically, as a foundation to this objective, we extend to the case of taint analysis the semantically sound flow-sensitive security type system by Hunt and Sands's, considering a support to the interprocedural analysis as well. With respect to the existing methods, K-Taint supports context- and flow-sensitive analysis, reduces false alarms, and provides a scalable solution. Experimental evaluation on several benchmark codes demonstrates encouraging results as an improvement in the precision of the analysis.
Several upgrades of Attribute-Value learning to inductivelogicprogramming have been proposed and used successfully. However, the Top-Down Data-Driven strategy, popularised by the AQ family, has not yet been transfer...
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ISBN:
(纸本)9783540738466
Several upgrades of Attribute-Value learning to inductivelogicprogramming have been proposed and used successfully. However, the Top-Down Data-Driven strategy, popularised by the AQ family, has not yet been transferred to ILP: if the idea of reducing the hypothesis space by covering a seed example is utilised with systems like PRO-GOL, Aleph or MIO, these systems do not benefit from the associated data-driven specialisation operator. this operator is given an incorrect hypothesis h and a covered negative example e and outputs a set of hypotheses more specific than h and correct wrt e. this refinement operator is very valuable considering heuristic search problems ILP systems may encounter when crossing plateaus in relational search spaces. In this paper, we present the data-driven strategy of AQ, in terms of a lgg-based change of representation of negative examples given a positive seed example, and show how it can be extended to ILP. We evaluate a basic implementation of AQ in the system PROPAL on a number of benchmark ILP datasets.
We continue to see an increase in applications based on multi-agent system technology. As the technology becomes more widespread, so does the requirement for agent systems to operate reliably. In this paper, we expand...
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ISBN:
(纸本)9789897584848
We continue to see an increase in applications based on multi-agent system technology. As the technology becomes more widespread, so does the requirement for agent systems to operate reliably. In this paper, we expand on the approach of using an agents logic to prove properties of agents. Our work describes a transformation from GOAL program code to an agent logic. We apply it to a Blocks World for Teams agent and prove a correctness property. Finally, we sketch future challenges of extending the framework.
the logic FO(ID) uses ideas from the field of logicprogramming to extend first order logic with non-monotone inductive definitions. the goal of this paper is to extend Gentzen's sequeut calculus to obtain a deduc...
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ISBN:
(纸本)9783642042379
the logic FO(ID) uses ideas from the field of logicprogramming to extend first order logic with non-monotone inductive definitions. the goal of this paper is to extend Gentzen's sequeut calculus to obtain a deductive inference method for FO(ID). the main difficulty in building Such a proof system is the representation and inference of unfounded sets. It turns out that we can represent unfounded sets by least fixpoint expressions borrowed from stratified least fixpoint logic (SLFP), which is a logic with a least fixpoint operator and characterizes the expressibility of stratified logic programs. therefore, in this paper, we integrate least fixpoint expressions into FO(ID) and define the logic FO(ID,SLFP). We investigate a sequeut calculus for FO(ID,SLFP), which extends the sequent calculus for SLFP with inference rules for the inductive definitions of FO(ID). We show that this proof system is sound with respect to a slightly restricted fragment of FO(ID) and complete for a more restricted fragment of FO(ID).
In machine learning we are often faced withthe problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and relational machine learning. It is therefore important to develop tec...
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ISBN:
(纸本)9781614995890;9781614995883
In machine learning we are often faced withthe problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and relational machine learning. It is therefore important to develop techniques to compensate for incomplete data. In inductivelogicprogramming (ILP) incomplete data can be in the form of missing values or missing predicates. In this paper, we investigate whether an ILP learner can compensate for missing background predicates through predicate invention. We conduct experiments on two datasets in which we progressively remove predicates from the background knowledge whilst measuring the predictive accuracy of three ILP learners with differing levels of predicate invention. the experimental results show that as the number of background predicates decreases, an ILP learner which performs predicate invention has higher predictive accuracies than the learners which do not perform predicate invention, suggesting that predicate invention can compensate for incomplete background knowledge.
Inverse entailment (IE) is known as a technique for finding inductive hypotheses in Horn theories. When a background theory is nonmonotonic, however, IE is not applicable in its present form. the purpose of this paper...
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