Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses, exemplified by Progol, and others that, produce multiple clauses in response to a single seed e...
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ISBN:
(纸本)9783642042379
Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses, exemplified by Progol, and others that, produce multiple clauses in response to a single seed example. A common denominator of these systems, is a restricted hypothesis search space, within which each clause must individually explain some example E, or some member of an abductive explanation for E. this paper proposes a new IE approach, called Induction on Failure (IoF), that generalises existing Horn clause learning systems by allowing the computation of hypotheses within a larger search space, namely that of Connected theories. A proof procedure for IoF is proposed that generalises existing IE systems and also resolves Yamamoto's example. A prototype implementation is also described. Finally, a semantics is presented called Connected theory Generalisation, which is proved to extend Kernel Set Subsumption and to include hypotheses constructed within this new IoF approach.
the proceedings contain 20 papers. the topics discussed include: building theories of the world: human and machine learning perspectives;SRL without tears: an ilp perspective;semantic web meets ilp: unconsumated love,...
ISBN:
(纸本)3540859276
the proceedings contain 20 papers. the topics discussed include: building theories of the world: human and machine learning perspectives;SRL without tears: an ilp perspective;semantic web meets ilp: unconsumated love, or no love lost?;learning expressive models of gene regulation;information overload and FP7 funding opportunities in 2009-10;a model to study phase transition and plateaus in relational learning;top-down induction of relational model trees in multi-instance learning;challenges in relational learning for real-time systems applications;discriminative structure learning of Markov logic networks;an experiment in robot discovery withilp;using the bottom clause and mode declarations on FOL theory revision from examples;DL-FOIL: concept learning in description logics;feature discovery with type extension trees;and feature construction using theory-guided sampling and randomised search.
A new approach based on constraint solving techniques was recently proposed for verification of hybrid systems. this approach works by searching for inductive invariants of a given form. In this paper, we extend that ...
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ISBN:
(纸本)9783540938996
A new approach based on constraint solving techniques was recently proposed for verification of hybrid systems. this approach works by searching for inductive invariants of a given form. In this paper, we extend that work to automatic synthesis of safe hybrid systems. Starting with a multi-modal dynamical system and a safety property, we present a sound technique for synthesizing a switching logic for changing modes so as to preserve the safety property. By construction, the synthesized hybrid system is well-formed and is guaranteed safe. Our approach is based on synthesizing a controlled invariant that is sufficient to prove safety. the generation of the controlled invariant is cast as a constraint, solving problem. When the system, the safety property, and the controlled invariant are all expressed only using polynomials, the generated constraint is an EA formula in the theory of reals, which we solve using SMT solvers. the generated controlled invariant is then used to arrive at the maximally liberal switching logic.
the field of inductivelogicprogramming (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 be...
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the field of inductivelogicprogramming (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.
the problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the progr...
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ISBN:
(纸本)9783540859277
the problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the program code or by running extensive timing analyses. this paper presents a new approach to the problem based on using Machine Learning in the form of ilp to infer program properties based on sample executions of the code. Additionally, significant improvements in the range of functions learnable and the time taken for learning can be made by the application of more advanced ilp techniques.
We investigate using the Mercury language to implement and design ilp algorithms, presenting our own ilp system IMP. Mercury provides faster execution than Prolog. Since Mercury is a purely declarative language, run-t...
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ISBN:
(纸本)9783540738466
We investigate using the Mercury language to implement and design ilp algorithms, presenting our own ilp system IMP. Mercury provides faster execution than Prolog. Since Mercury is a purely declarative language, run-time assertion of induced clauses is prohibited. Instead IMP uses a problem-specific interpreter of ground representations of induced clauses. the interpreter is used both for cover testing and bottom clause generation. the Mercury source for this interpreter is generated automatically from the user's background knowledge using Moose, a Mercury parser generator. Our results include some encouraging results on IMP's cover testing speed, but overall IMP is still generally a little slower than ALEPH.
Instruction selection is a compiler optimisation that translates the intermediate representation of a program into a lower intermediate representation or an assembler program. We use the SSA form as an intermediate re...
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ISBN:
(纸本)9781450378345
Instruction selection is a compiler optimisation that translates the intermediate representation of a program into a lower intermediate representation or an assembler program. We use the SSA form as an intermediate representation for instruction selection. Patterns are used for translation and are expressed as production rules in a graph grammar. the instruction selector seeks for a syntax derivation with minimal costs optimising execution time, code size, or a combination of both. Production rules are either base rules which match nodes in the SSA graph or chain rules which convert results of operations. We present a new algorithm for placing chain rules in a control flow graph. this new algorithm places chain rules optimally for an arbitrary cost metric. Experiments withthe MiBench and SPEC2000 benchmark suites show that our proposed algorithm is feasible and always yields better results than simple strategies currently in use. We reduce the costs for placing chain rules by 25% for the MiBench suite and by 11% for the SPEC2000 suite.
Narrowing-driven partial evaluation is a powerful technique for the specialization of (first-order) functional and functional logic programs. However, although it gives good results on small programs, it does not scal...
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inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in...
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inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in the life sciences, engineering and the arts. this paper begins by briefly reviewing some example applications, in order to illustrate the benefits of ilp. In turn, the applications have brought into focus the need for more research into specific topics. We enumerate and elaborate five of these: (1) novel search methods;(2) incorporation of explicit probabilities;(3) incorporation of special-purpose reasoners;(4) parallel execution using commodity components;and (5) enhanced human interaction. It is our hypothesis that progress in each of these areas can greatly improve the contributions that can be made withilp;and that, with assistance from research workers in other areas, significant progress in each of these areas is possible.
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