the proceedings contain 11 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance;...
ISBN:
(纸本)9783030492090
the proceedings contain 11 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance;learning Probabilistic logic Programs over Continuous Data;towards Meta-interpretive Learning of programming Language Semantics;Towards an ILP Application in Machine Ethics;on the Relation Between Loss Functions and T-Norms;rapid Restart Hill Climbing for Learning Description logic Concepts;neural Networks for Relational Data;learning logic Programs from Noisy State Transition Data;a New Algorithm for Computing Least Generalization of a Set of Atoms.
the proceedings contain 7 papers. the topics discussed include: care robots learning rules of ethical behavior under the supervision of an ethical teacher;a parallelization approach for hybrid-AI-based models: an appl...
the proceedings contain 7 papers. the topics discussed include: care robots learning rules of ethical behavior under the supervision of an ethical teacher;a parallelization approach for hybrid-AI-based models: an application study for semantic segmentation of medical images;towards inductive learning of domain-specific heuristics for ASP;mining sequences in phone recordings with answer set programming;estimation-based verification of cyber-physical systems via statistical model checking;explainability via short formulas: the case of propositional logic with implementation;and evaluating epistemic logic programs via answer set programming with quantifiers.
the proceedings of the internationalconference on logicprogramming (ICLP) have had several publishers, including MIT Press and Springer's Lecture Notes in Computer Science. Beginning in 2010, the proceedings hav...
the proceedings of the internationalconference on logicprogramming (ICLP) have had several publishers, including MIT Press and Springer's Lecture Notes in Computer Science. Beginning in 2010, the proceedings have been published in a dual format: with regular papers contained in a special issue of theory and Practice of logicprogramming (TPLP), and technical communications as a Dagstuhl LIPics series publication. the reason for the change was that compared to researchers in other fields, computer scientists publish more in conferences or symposia and less in journals. the thinking went that since many ICLP papers are of journal quality – or nearly so – why not publish them in a journal straight away? And why not TPLP?
System-on-chip (SoC) design requires complex reasoning about the interactions between an architectural specification, the microarchitectural datapath (e.g., functional units), and the control logic (which coordinates ...
ISBN:
(纸本)9798400703911
System-on-chip (SoC) design requires complex reasoning about the interactions between an architectural specification, the microarchitectural datapath (e.g., functional units), and the control logic (which coordinates the datapath) to facilitate the critical computing tasks on which we all depend. Hardware specialization is now the expectation rather than the exception, meaning we need new hardware design tools to bring ideas to reality with both agility and *** introduce a new technique, "control logic synthesis", which automatically generates control logic given a datapath description and an architectural specification. this enables an entirely new hardware design process where the designer only needs to write a datapath sketch, leaving the control logic as "holes." then, guided by an architectural specification, we adapt program synthesis techniques to automatically generate a correct hardware implementation of the control logic, filling the holes and completing the *** evaluate control logic synthesis over two classes of control (state machines and instruction decoders) and different architectures (embedded-class RISC-V cores and hardware accelerators for cryptography). We demonstrate how agile-oriented SoC developers can iterate over designs without writing control logic by hand yet still retain formal assurances with only minimal microarchitectural information.
the links to the online only Technical Communications in Lamma and Swift (2013) are unfortunately broken. All of the Technical Communications can be found here: https://***/core/journals/theory-and-practice-of-logic-p...
the links to the online only Technical Communications in Lamma and Swift (2013) are unfortunately broken. All of the Technical Communications can be found here: https://***/core/journals/theory-and-practice-of-logic-programming/article/editorial-29th-international-conference-on-logic-programming-special-issue/82FDD81073DC30A563ED242516CADAAE#fndtn-supplementary-materials
We introduce a new application for inductivelogicprogramming: learning the semantics of programming languages from example evaluations. In this short paper, we explore a simplified task in this domain using the Meta...
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ISBN:
(数字)9783030492106
ISBN:
(纸本)9783030492090;9783030492106
We introduce a new application for inductivelogicprogramming: learning the semantics of programming languages from example evaluations. In this short paper, we explore a simplified task in this domain using the Metagol meta-interpretive learning system. We high-light the challenging aspects of this scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and propose extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.
FO(.)(IDP3) extends first-order logic withinductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. the grounder uses L...
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FO(.)(IDP3) extends first-order logic withinductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. the grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and uses XSB Prolog to compute their interpretation. the interpretation of these predicates is then added to the input structure, their definitions are removed from the theory and further processing is done by the standard IDP3 system. Experimental results show the effectiveness of our method.
the field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logicprogramming (PLP): the enabli...
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ISBN:
(数字)9783030492106
ISBN:
(纸本)9783030492090;9783030492106
the field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logicprogramming (PLP): the enabling of stochastic primitives in logicprogramming. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductivelogicprogramming and related techniques have paved much of the way for the last few decades, but a major limitation of this exciting landscape is that only discrete features and distributions are handled. Many disciplines express phenomena in terms of continuous models. In this paper, we propose a new computational framework for inducing probabilistic logic programs over continuous and mixed discrete-continuous data. Most significantly, we show how to learn these programs while making no assumption about the true underlying density. Our experiments show the promise of the proposed framework.
Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. the class of ML known as inductivelogicprogramming (ILP) ...
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ISBN:
(数字)9783030492106
ISBN:
(纸本)9783030492090;9783030492106
Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. the class of ML known as inductivelogicprogramming (ILP) draws on the expressivity and rigour of subsets of First Order logic to represent both data and models. When Description logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ILP output is a prime candidate for explainable artificial intelligence;the expense being computational complexity. We have recently demonstrated how a critical component of ILP learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ILP learner in DL that benefits from this use of concurrency. the result is a fast, scalable tool that can be applied directly to large ontologies.
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