共查询到19条相似文献,搜索用时 62 毫秒
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模型检测是通信顺序进程(communicating sequential processes,简称CSP)形式化验证的重要手段.当前, CSP模型检测方法基于操作语义,需将进程转化为迁移系统,进而提取语义模型,但转化过程较为复杂;待验证性质采用CSP语言进行描述,虽然有利于精炼检测(refinement checking),但描述能力较弱,通用性不强.鉴于此,提出了一种新的CSP指称语义模型——关键迹模型(critical-trace model)及基于该指称语义模型的CSP模型检测方法,并证明了其验证的可靠性,避免了上述问题.关键迹模型采用递归策略计算,待验证性质采用线性时态逻辑(linear temporal logic,简称LTL)描述.基于回答集程序设计(answer set programming,简称ASP)实现了关键迹模型的自动生成及LTL的自动验证,并开发了一个CSP模型检测原型系统——T_ASP.实验结果表明:与类似系统相比,该系统的描述能力更强,验证结果的准确性更高,且可同时验证多条性质,在性质不满足时还可提供多条反例. 相似文献
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归纳逻辑程序设计(ILP)是机器学习的一个重要分支,给定一个样例集和相关背景知识,ILP研究如何构建与其相一致的逻辑程序,这些逻辑程序由有限一阶子句组成。文章描述了一种综合当前一些ILP方法多方面优势的算法ICCR,ICCR溶合了以FOIL为代表的自顶向下搜索策略和以GOLEM为代表的自底向上搜索策略,并能根据需要发明新谓词、学习递归逻辑程序,对比实验表明,对相同的样例及背景知识,ICCR比FOIL和GOLEM能学到精度更高的目标逻辑程序。 相似文献
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MDA为软件的自动化构造提供了一种良好的途径,但是MDA更多地关注PIM到PSM的转换,却忽视了需求阶段对PIM精确性的影响。本文针对这种问题,首先对MDA的基本过程进行改进,提出对需求进行必要的形式化描述的思路,使得需求模型到PIM的转换变得更为容易,从而达到增强PIM精确性的目的;其次,通过对三种形式化语言的对比研究,发现B语言在需求模型的表示及转换方面具有优势,并结合例子展示了如何使用B语言对软件需求进行形式化描述;最后,本文结合模型转换框图,给出了B方法和类图的ecore元模型的图形化描述,进一步给出了用B语言描述的需求模型到PIM的基本转换规则,并借助JavaCC、JJTree和ATL插件等工具实现了需求模型到PIM的转换。 相似文献
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学习自动机(Learning automation,LA)是一种自适应决策器。其通过与一个随机环境不断交互学习从一个允许的动作集里选择最优的动作。在大多数传统的LA模型中,动作集总是被取作有限的。因此,对于连续参数学习问题,需要将动作空间离散化,并且学习的精度取决于离散化的粒度。本文提出一种新的连续动作集学习自动机(Continuous action set learning automaton,CALA),其动作集为一个可变区间,同时按照均匀分布方式选择输出动作。学习算法利用来自环境的二值反馈信号对动作区间的端点进行自适应更新。通过一个多模态学习问题的仿真实验,演示了新算法相对于3种现有CALA算法的优越性。 相似文献
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智能规划中基于遗传算法的动作模型学习 总被引:4,自引:0,他引:4
在动作间的状态未知条件下,利用遗传算法,从不完整的领域描述和规划实例中学习动作模型,并且设计了AMLS-GA(Action Model Learning System Based on Genetic Algorithm)系统来具体实现这一思想.作者为每一个动作构建一个可能谓词集,这个谓词集覆盖了动作前提表、增加表和删除表中的所有谓词.采用二进制编码的方式,把动作模型编码成GA搜索空间中的一个假设,学习过程是在标准的遗传算法框架下进行的.把学习结果的正确性定义为尽可能多的解释规划实例,并且通过实验的方法对比学习到的模型与专家预定义模型之间的差别.实验结果表明,算法能在较短的时间内,学习到一个逼近专家描述的动作模型. 相似文献
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Steve Dworschak Susanne Grell Victoria J. Nikiforova Torsten Schaub Joachim Selbig 《Constraints》2008,13(1-2):21-65
We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose
an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and
semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally,
we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage. 相似文献
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归纳学习的目的在于发现样例与离散的类之间的映射关系,样例及归纳的映射都需用某个形式化语言描述.归纳学习器采用的形式化语言经历了属性-值语言、一阶逻辑、类型化的高阶逻辑三个阶段,后者能克服前二者在知识表达及学习过程中的很多缺点.本文首先阐述了基于高阶逻辑的复杂结构归纳学习产生的历史背景;其次介绍了基于高阶逻辑的编程语言--Escher的知识描述形式及目前已提出的三种学习方法;复杂结构的归纳学习在机器学习领域的应用及如何解决一些现实问题的讨论随后给出; 最后分析了复杂结构归纳学习的研究所面临的挑战性问题. 相似文献
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The least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses
of predicates with string arguments. We propose a specific generalization (SG) for strings to reduce over-generalization.
SGs of strings are used in the generalization of a set of strings representing the arguments of a set of positive examples
of a predicate with string arguments. In order to create a SG of two strings, first, a unique match sequence between these
strings is found. A unique match sequence of two strings consists of similarities and differences to represent similar parts
and differing parts between those strings. The differences in the unique match sequence are replaced to create a SG of those
strings. In the generalization process, a coverage algorithm based on SGs of strings or learning heuristics based on match
sequences are used.
Ilyas Cicekli received a Ph.D. in computer science from Syracuse University in 1991. He is currently a professor of the Department of Computer
Engineering at Bilkent University. From 2001 till 2003, he was a visiting faculty at University of Central Florida. His current
research interests include example-based machine translation, machine learning, natural language processing, and inductive
logic programming.
Nihan Kesim Cicekli is an Associate Professor of the Department of Computer Engineering at the Middle East Technical University (METU). She graduated
in computer engineering at the Middle East Technical University in 1986. She received the MS degree in computer engineering
at Bilkent University in 1988; and the PhD degree in computer science at Imperial College in 1993. She was a visiting faculty
at University of Central Florida from 2001 till 2003. Her current research interests include multimedia databases, semantic
web, web services, data mining, and machine learning. 相似文献
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Thomas Eiter Wolfgang Faber Michael Fink Stefan Woltran 《Annals of Mathematics and Artificial Intelligence》2007,51(2-4):123-165
Answer set programming is a declarative programming paradigm rooted in logic programming and non-monotonic reasoning. This
formalism has become a host for expressing knowledge representation problems, which reinforces the interest in efficient methods
for computing answer sets of a logic program. The complexity of various reasoning tasks for general answer set programming
has been amply studied and is understood quite well. In this paper, we present a language fragment in which the arities of
predicates are bounded by a constant. Subsequently, we analyze the complexity of various reasoning tasks and computational
problems for this fragment, comprising answer set existence, brave and cautious reasoning, and strong equivalence. Generally
speaking, it turns out that the complexity drops significantly with respect to the full non-ground language, but is still
harder than for the respective ground or propositional languages. These results have several implications, most importantly
for solver implementations: Virtually all currently available solvers have exponential (in the size of the input) space requirements
even for programs with bounded predicate arities, while our results indicate that for those programs polynomial space should
be sufficient. This can be seen as a manifestation of the “grounding bottleneck” (meaning that programs are first instantiated
and then solved) from which answer set programming solvers currently suffer. As a final contribution, we provide a sketch
of a method that can avoid the exponential space requirement for programs with bounded predicate arities.
Some results in this paper have been presented in preliminary form at KR 2004 [15]. 相似文献
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Daniel R. Brooks Esra Erdem Selim T. Erdoğan James W. Minett Don Ringe 《Journal of Automated Reasoning》2007,39(4):471-511
We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative
knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures
about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages
and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies
for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we
can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction
of some phylogenies that are not plausible.
This paper is a revised and extended version of [3]. 相似文献
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Structured machine learning: the next ten years 总被引:4,自引:1,他引:3
Thomas G. Dietterich Pedro Domingos Lise Getoor Stephen Muggleton Prasad Tadepalli 《Machine Learning》2008,73(1):3-23
The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years. 相似文献
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This paper introduces a novel logical framework for concept-learning called brave induction. Brave induction uses brave inference for induction and is useful for learning from incomplete information. Brave induction
is weaker than explanatory induction which is normally used in inductive logic programming, and is stronger than learning from satisfiability, a general setting of concept-learning in clausal logic. We first investigate formal properties of brave induction, then
develop an algorithm for computing hypotheses in full clausal theories. Next we extend the framework to induction in nonmonotonic logic programs. We analyze computational complexity of decision problems for induction on propositional theories. Further, we provide examples
of problem solving by brave induction in systems biology, requirement engineering, and multiagent negotiation. 相似文献
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We study several complexity parameters for first order formulas and their suitability for first order learning models. We
show that the standard notion of size is not captured by sets of parameters that are used in the literature and thus they
cannot give a complete characterization in terms of learnability with polynomial resources. We then identify an alternative
notion of size and a simple set of parameters that are useful for first order Horn Expressions. These parameters are the number
of clauses in the expression, the maximum number of distinct terms in a clause, and the maximum number of literals in a clause.
Matching lower bounds derived using the Vapnik Chervonenkis dimension complete the picture showing that these parameters are
indeed crucial.
This work has been partly supported by NSF Grant IIS-0099446. A preliminary version of this paper appeared in the proceeding
of the conference on Inductive Logic Programming 2003.
Most of this work was done while M.A. was at Tufts University.
Editors: Tamás Horváth and Akihiro Yamamoto 相似文献