the acquisition of control structures in programming poses a significant challenge for K12 students, often requiring more time than typically allocated in standard lecture schedules. this study uses three distinct exp...
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the proceedings contain 8 papers. the topics discussed include: explaining answers to datalog queries;DatalogMTL: datalog with metric temporal logic operators;incremental evaluation of dynamic datalog programs as a hi...
the proceedings contain 8 papers. the topics discussed include: explaining answers to datalog queries;DatalogMTL: datalog with metric temporal logic operators;incremental evaluation of dynamic datalog programs as a higher-order DBSP program;a tool for reasoning over CNL sentences with temporal constructs;experiencing hypothetical datalog in SQL puzzles;LLM-based DatalogMTL modelling of MiCAR-compliant crypto-assets markets;an exploration of datalog applications to language documentation and reclamation;and Nemo: a scalable and versatile datalog engine.
Uncertainty occurs in the world in many ways. For instance, image processing programs identify the content of images with some levels of uncertainty. Prediction programs predict when events will occur with certain pro...
ISBN:
(纸本)9783540429357
Uncertainty occurs in the world in many ways. For instance, image processing programs identify the content of images with some levels of uncertainty. Prediction programs predict when events will occur with certain probabilities. In this tutorial, I will focus on probabilistic methods to handle uncertainty.
In this paper we present a logicprogramming based framework for the integration of possibly inconsistent databases. In particular we consider the problem of ‘merging’ databases and, since the resulting ‘merged’ d...
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Multi-adjoint logic programs has been recently introduced [9, 10] as a generalization of monotonic logic programs [2, 3], in that simultaneous use of several implications in the rules and rather general connectives in...
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there was a time when logic was the dominant paradigm for human reasoning. As George Boole put it around one hundred and fifty years ago,logic was synonymous withthe “Laws of thought”. Later, for ...
ISBN:
(纸本)9783540429357
there was a time when logic was the dominant paradigm for human reasoning. As George Boole put it around one hundred and fifty years ago,logic was synonymous withthe “Laws of thought”. Later, for most of the latter half of the twentieth century,it was the mainstream of Artificial Intelligence. But then it all went wrong. Artificial Intelligence researchers, frustrated by the lack of progress, blamed many of their problems on the logic-based approach. they argued that humans do not reason logically, and therefore machines should not be designed to reason logically either. Other approaches began to make progress where logic was judged to have failed - approaches that were designed to simulate directly the neurological mechanisms of animal and human intelligence. Insect-like robots began to appear,and the beginning of a new Machine Intelligence was born. logic seemed to be dieing - and to be taking logicprogramming (LP) with it.
In the paper we establish the fixed-parameter complexity for several parameterized decision problems involving models, supported models and stable models of logic programs. We also establish the fixed parameter comple...
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Tabled logicprogramming (LP) systems have been applied to elegantly and quickly solving very complex problems (e.g., model checking). However, techniques currently employed for incorporating tabling in an existing LP...
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We revisit an application developed originally using abductive Inductive logicprogramming (ILP) for modeling inhibition in metabolic networks. the example data was derived from studies of the effects of toxins on rat...
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We revisit an application developed originally using abductive Inductive logicprogramming (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 withthe PILP models learned from non-probabilistic examples.
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