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Inverse entailment and Progol
, 1995
"... This paper firstly provides a reappraisal of the development of techniques for inverting deduction, secondly introduces ModeDirected Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol ..."
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Cited by 631 (59 self)
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This paper firstly provides a reappraisal of the development of techniques for inverting deduction, secondly introduces ModeDirected Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol is implemented in C and available by anonymous ftp. The reassessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.
A model for learning systems
, 1977
"... A model for learning systems is presented, and representative AI, pattern recognition, and control systems are discussed in terms of its framework. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and t ..."
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Cited by 20 (0 self)
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A model for learning systems is presented, and representative AI, pattern recognition, and control systems are discussed in terms of its framework. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and the specific environment in which it operates. These components are erformance element, instance selector, critic, P earning element, blackboard, and world model. Consideration of learning system design leads naturally to the concept of a layered system, each layer operating at a different level of abstraction. Descriptive Terms: adaptation, learning, conceptformatIon, induct ion, performance element, instance selector, critic, learning element, blackboard, world model, multilayered systems. 1
Learning Logical Exceptions In Chess
, 1994
"... This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess endgame "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to overc ..."
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Cited by 17 (2 self)
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This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess endgame "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to overchallenge stateof theart learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the bestperforming algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a nonmonotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio...
Learning Concepts by Performing Experiments
, 1981
"... Marvin is a program which is capable of learning concepts from many different environments. It achieves this by using a flexible description language based on first order predicate logic with quantifiers. Once a concept has been learnt, Marvin treats the concept description as a program which can be ..."
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Cited by 14 (4 self)
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Marvin is a program which is capable of learning concepts from many different environments. It achieves this by using a flexible description language based on first order predicate logic with quantifiers. Once a concept has been learnt, Marvin treats the concept description as a program which can be executed to produce an output. Thus the learning system can also be viewed as an automatic program synthesizer. The ability to treat a concept as a program permits the learning system to construct objects to show a human trainer. Given an initial example by the trainer, Marvin creates a concept intended to describe the class of objects containing the example. The validity of the description is tested when Marvin constructs an instance of the concept to show the trainer. If he indicates that the example constructed by the program belongs to the concept which is to be learnt, called the 'target', then Marvin attempts to generalize the description of its hypothesized concept. If the example does not belong to the target then the description must be made more specific so that a correct example can be constructed. This process is repeated until the description of
Induction in first order logic from noisy training examples and fixed example set size
 In PhD Thesis
, 1999
"... Abstract This dissertation investigates the field of inductive logic programming (ILP) and in so doing an ILP system, Lime, is designed and developed. Lime addresses the problem of noisy training examples; learning from only positive, only negative, or both positive and negative examples; efficientl ..."
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Cited by 6 (0 self)
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Abstract This dissertation investigates the field of inductive logic programming (ILP) and in so doing an ILP system, Lime, is designed and developed. Lime addresses the problem of noisy training examples; learning from only positive, only negative, or both positive and negative examples; efficiently biasing and searching the hypothesis space; and handling recursion efficiently and effectively. The Qheuristic is introduced to address the problem of learning with both noisy training examples and fixed numbers of positive and negative training examples. This heuristics is based on Bayes rule. Both a justification of its derivation and a description of the context in which it is appropriately applied are given. Because of the general nature of this heuristic its application is not restricted to ILP. Instead of employing a greedy covering approach to constructing clauses, Lime employs the Qheuristic to evaluate entire logic programs as hypotheses. To tame the inevitable explosion in the search space, the notion of a simple clause is introduced. These sets of literals may be viewed as subparts of clauses that are effectively independent in terms of variables used. Instead of growing a clause one literal at a time, Lime efficiently combines simple clauses to construct a set of gainful candidate clauses. Subsets of these candidate clauses are evaluated using the Qheuristic to find the final hypothesis. Details of the algorithms and data structures of Lime are discussed. Lime's handling of recursive logic programs is also described. Experimental results are provided to illustrate how Lime achieves its design goals of better noise handling, learning from a fixed set of examples (e.g., from only positive data), and of learning recursive logic programs. These results compare the performance of Lime with other leading ILP systems like Foil and Progol in a variety of domains. Empirical results with a boosted version of Lime are also reported.
A modular Equational Generalization Algorithm
, 2008
"... Abstract. This paper presents a modular equational generalization algorithm, where function symbols can have any combination of associativity, commutativity, and identity axioms (including the empty set). This is suitable for dealing with functions that obey algebraic laws, and are typically mechani ..."
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Cited by 3 (0 self)
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Abstract. This paper presents a modular equational generalization algorithm, where function symbols can have any combination of associativity, commutativity, and identity axioms (including the empty set). This is suitable for dealing with functions that obey algebraic laws, and are typically mechanized by means of equational atributes in rulebased languages such as ASF+SDF, Elan, OBJ, CafeOBJ, and Maude. The algorithm computes a complete set of least general generalizations modulo the given equational axioms, and is specified by a set of inference rules that we prove correct. This work provides a missing connection between least general generalization and computing modulo equational theories, and opens up new applications of generalization to rulebased languages, theorem provers and program manipulation tools such as partial evaluators, test case generators, and machine learning techniques, where function symbols obey algebraic axioms. A Web tool which implements the algorithm has been developed which is publicly available.