Inductive Logic Programming: Theory and methods

Muggleton, Stephen; Raedt, Luc De

doi:10.1016/0743-1066(94)90035-3

Cited by 1,130 publications

(745 citation statements)

References 35 publications

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“…They constitute the model of the relations. Rather than encoding these rules manually we learn them from examples of data using Inductive Logic Programming (ILP) methods [15]. Typically ILP methods use a top-down approach of refining a very general rule, where the refining process involves adding a body predicate one at a time with the purpose of "better fitting" the given training examples, i.e., eliminate negative examples while still maintaining the coverage of positive examples.…”

Section: Technical Preliminaries and Overviewmentioning

confidence: 99%

“…The added predicates can be base predicates, previously learned predicates, or arithmetic constraints (such as |X-Y| < δ). ILP methods have produced impressive practical results (see e.g., [15] and are now well established as mainstream machine learning technology. The strength of ILP methods is that learning is done in the presence of prior background knowledge encoded as facts.…”

Section: Technical Preliminaries and Overviewmentioning

confidence: 99%

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Model-driven Visual Analytics

Garg

Nam

Ramakrishnan

et al. 2008

2008 IEEE Symposium on Visual Analytics Science and Technology

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We describe a Visual Analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use Logic Programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the highdimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains. INTRODUCTIONModern day enterprises, be they commerce, government, science, engineering or medical, have to cope with voluminous amounts of data. Effective decision making based on large, dynamic datasets with many parameters requires a conceptual high-level understanding of the data. Acquiring such an understanding is a difficult problem, especially in the presence of incomplete, inconsistent, and noisy data acquired from disparate real-world sources.To make progress on this problem one must draw on the complementary strengths of computing machinery and human insight. Recognizing this promising human-computer synergy, Visual Analytics (VA), defined as the science of analytical reasoning facilitated by interactive visual interfaces [22], has become a major development thrust. It seeks to engage the fast visual circuitry of the human brain to quickly find relations in complex data, trigger creative thoughts, and use these elements to steer the underlying computational analysis processes towards the extraction of new information for further insight. VA has widespread applications, such as homeland security, the financial industry and internet security among others. Research papers and tools related to VA are beginning to emerge (see e.g.However, thus far the main emphasis in VA has been mostly on visualization, data management, and user interfaces (see e.g. [5] and other work mentioned in the following). As far as analytical computing goes, VA research has mainly focused on relatively low-level tasks, such as image [24] and video [12] analysis and database operations [21]. In today's VA systems, it is the human analyst who performs the actual reasoning and abstraction. Obviously this ty...

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Section: Technical Preliminaries and Overviewmentioning

confidence: 99%

Section: Technical Preliminaries and Overviewmentioning

confidence: 99%

Model-driven Visual Analytics

Garg

Nam

Ramakrishnan

et al. 2008

2008 IEEE Symposium on Visual Analytics Science and Technology

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“…Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples [22]. Inductive logic programming is particularly useful in bioinformatics and natural language processing [23]. The term inductive logic programming was first introduced in a paper by Stephen Muggleton [22].…”

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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Abstract-Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.Index Terms-Machine learning, adaptive control, repetitive control, robot manipulators. I. INTRODUCTIONMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data [1]. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science [2]. In this paper we are focused on learning algorithms for robot manipulators. II. MACHINE LEARNING ALGORITHMSMachine learning algorithms are organized into a taxonomy based on the desired outcome of the algorithm.Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function for robot manipulators [1].Unsupervised learning models a set of inputs, like clustering [1].Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier in manipulators [3].Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm [1] and [3].Transduction tries to predict new outputs based on training inputs, training outputs, an...

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“…-Being based on logic programming, it can build on the successes of inductive logic programming [Muggleton and De Raedt, 1994;Quinlan and Cameron-Jones, 1995;Muggleton, 1995]. The fact that parts of ICL theories are logic programs should aid in this effort.…”

Section: Icl and Learningmentioning

confidence: 99%

The Independent Choice Logic and Beyond

Poole

2008

Lecture Notes in Computer Science

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Abstract. The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

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Inductive Logic Programming: Theory and methods

Cited by 1,130 publications

References 35 publications

Model-driven Visual Analytics

Model-driven Visual Analytics

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

The Independent Choice Logic and Beyond

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