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1.
Fabrizio Riguzzi 《Machine Learning》2008,70(2-3):207-223
Logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge
in logic programming. In this paper we consider the problem of learning ground LPADs starting from a set of interpretations
annotated with their probability. We present the system ALLPAD for solving this problem. ALLPAD modifies the previous system
LLPAD in order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution
rather than a perfect one. A number of experiments have been performed on real and artificial data for evaluating ALLPAD,
showing the feasibility of the approach.
Editors: Stephen Muggleton, Ramon Otero, Simon Colton. 相似文献
2.
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
3.
We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in
metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance
(NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia
of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches—abductive Stochastic Logic Programs (SLPs)
and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability
predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead
of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based
on a general technique for introducing probability labels within a standard scientific experimental setting involving control
and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from
probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied
by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. 相似文献
4.
PRL: A probabilistic relational language 总被引:1,自引:0,他引:1
In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent
work in Probabilistic Relational Models (PRMs) into a logic programming framework. We show how to represent varying degrees
of complexity in the semantics including attribute uncertainty, structural uncertainty and identity uncertainty. Our approach
is similar in spirit to the work in Bayesian Logic Programs (BLPs), and Logical Bayesian Networks (LBNs). However, surprisingly,
there are still some important differences in the resulting formalism; for example, we introduce a general notion of aggregates
based on the PRM approaches. One of our contributions is that we show how to support richer forms of structural uncertainty
in a probabilistic logical language than have been previously described. Our goal in this work is to present a unifying framework
that supports all of the types of relational uncertainty yet is based on logic programming formalisms. We also believe that
it facilitates understanding the relationship between the frame-based approaches and alternate logic programming approaches,
and allows greater transfer of ideas between them.
Editors: Hendrik Blockeel, David Jensen and Stefan Kramer
An erratum to this article is available at . 相似文献
5.
6.
This paper presents a novel revision of the framework of Hybrid Probabilistic Logic Programming, along with a complete semantics
characterization, to enable the encoding of and reasoning about real-world applications. The language of Hybrid Probabilistic
Logic Programs framework is extended to allow the use of non-monotonic negation, and two alternative semantical characterizations
are defined: stable probabilistic model semantics and probabilistic well-founded semantics. These semantics generalize the
stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of
Hybrid Probabilistic Logic programs for programs without negation. It is the first time that two different semantics for Hybrid
Probabilistic Programs with non-monotonic negation as well as their relationships are described. This proposal provides the
foundational grounds for developing computational methods for implementing the proposed semantics. Furthermore, it makes it
clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning.
An erratum to this article can be found at 相似文献
7.
A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol (TCP) networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control (PFC) system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional (3-D) membership function (MF) is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among "fuzziness-randomness-state". The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment. 相似文献
8.
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
9.
In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case.By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case.Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call theory reformulation the process adopted in this situation of complete knowledge.In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of incomplete knowledge, and call theory revision the extended process. In order to fill in knowledge gaps we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions.In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory.We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods. 相似文献
10.
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
11.
Relational Instance-Based Learning with Lists and Terms 总被引:3,自引:0,他引:3
The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance-based learner ribl that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and empirically demonstrate its additional power on a problem from the domain of biochemistry. The paper also includes a thorough reconstruction of ribl's overall algorithm. 相似文献
12.
Samir Khuller M. Vanina Martinez Dana Nau Amy Sliva Gerardo I. Simari V. S. Subrahmanian 《Annals of Mathematics and Artificial Intelligence》2007,51(2-4):295-331
The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics. We propose a variant
of PLPs called action probabilistic logic programs or -programs that use a two-sorted alphabet to describe the conditions under which certain real-world entities take certain
actions. In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find
the most probable world (MPW) for -programs. In contrast, past work on PLPs has primarily focused on the problem of entailment.
This paper quickly presents the syntax and semantics of -programs and then shows a naive algorithm to solve the MPW problem
using the linear program formulation commonly used for PLPs. As such linear programs have an exponential number of variables,
we present two important new algorithms, called and to solve the MPW problem exactly. Both these algorithms can significantly reduce the number of variables in the linear programs.
Subsequently, we present a “binary” algorithm that applies a binary search style heuristic in conjunction with the Naive,
and algorithms to quickly find worlds that may not be “most probable.” We experimentally evaluate these algorithms both for accuracy
(how much worse is the solution found by these heuristics in comparison to the exact solution) and for scalability (how long
does it take to compute). We show that the results of are very accurate and also very fast: more than 1030,000 worlds can be handled in a few minutes. Subsequently, we develop parallel versions of these algorithms and show that they
provide further speedups.
相似文献
13.
14.
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
15.
归纳逻辑程序设计的核心问题是如何从背景知识中优选谓词构造满足约束的归纳假设,按Occam准则,满足约束的最精简归纳假设为优,但迄今归纳逻辑程序设计中精简归纳假设构造的计算复杂性尚未解决。 相似文献
16.
17.
This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of acyclicity; (iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries. 相似文献
18.
统计关系学习研究进展 总被引:4,自引:0,他引:4
统计关系学习是人工智能领域的一个新研究热点,它将关系表示、似然性理论和机器学习相结合,能更好地解决现实世界中复杂的关系数据问题,在生物信息学、web导航、社会网、地理信息系统和自然语言理解等领域有着重要的应用.首先对统计关系学习的研究内容以及研究任务进行了介绍和总结,然后根据概率表示和推理机制的不同,对当前的统计关系学习方法进行了分类,并对各类方法进行了详细介绍,最后讨论了当前统计关系学习存在的问题,并指出了今后研究和发展的方向. 相似文献
19.
动作模型学习可以使Agent主动适应动态环境中的变化,从而提高Agent的自治性,同时也可为动态域建模提供一个初步模型,为后期的模型完善和修改提供了基础.通过结合归纳逻辑程序设计(Inductive Logic Program-ming,ILP)和回答集程序设计(Answer Set Programming,ASP),设计了一个学习B语言描述的动作模型算法,该算法可以在混合规模的动态域中进行学习,并采用经典规划实例验证了该学习算法的有效性. 相似文献
20.
近年来,概率逻辑学习研究取得了很大进展,已经提出各种不同的形式化方法和学习方法,包括概率关系模(PRMs)、贝叶斯逻辑程序(BLPs)、逻辑贝叶斯网络(LBNs)和随机逻辑程序(SLPs)等。文章重点介绍了贝叶斯网络与一阶逻辑的结合,并以PRMs、BLPs和LBNs为例,描述了基于贝叶斯网络的概率逻辑模型(PLMs)的知识表示方法,给出了此类PLMs一般使用的参数估计方法和结构学习方法,并给出了建议的研究方向。 相似文献