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Towards One-Shot Learning for Text Classification using Inductive logic programming 39

Towards One-Shot Learning for Text Classification using Indu...

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39th international conference on logic programming (iclp)

作者： Milani, Ghazal Afroozi Cyrus, Daniel Tamaddoni-Nezhad, Alireza Univ Surrey Dept Comp Sci Surrey England

With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data. In this paper, we explore an Inductive logic programming approach for one-shot text classification. In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet. Results indicate that MIL can learn text classification rules from a small number of training examples, even one example. Moreover, the higher complexity of chosen example for one-shot learning, the higher accuracy of the outcome. Finally, we utilise two approaches, Background Knowledge Splitting and Average One-Shot Learning, to evaluate our model on a public News Category dataset. the outcomes validate MIL's superior performance to the Siamese net for one-shot learning from text.

关键词： Computer circuits

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On the Development of PASTA: Inference in Probabilistic Answer Set programming under the Credal Semantics 39

On the Development of PASTA: Inference in Probabilistic Answ...

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39th international conference on logic programming, iclp 2023

作者： Azzolini, Damiano University of Ferrara Italy

PASTA is a novel framework to perform inference, both exact and approximate, in Probabilistic Answer Set programming under the Credal Semantics and to solve other related tasks, such as abduction, decision theory, maximum a posteriori inference, and parameter learning. Moreover, it also allows the expression of statistical statements. this paper shows the overall structure of the PASTA tool as well as some of the main options. © 2023 Open Publishing Association. All rights reserved.

关键词： Semantics

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On the Independencies Hidden in the Structure of a Probabilistic logic Program 39

On the Independencies Hidden in the Structure of a Probabili...

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39th international conference on logic programming (iclp)

作者： Rueckschloss, Kilian Weitkaemper, Felix Ludwig Maximilians Univ Munchen Oettingenstr 67 D-80538 Munich Germany

Pearl and Verma developed d-separation as a widely used graphical criterion to reason about the conditional independencies that are implied by the causal structure of a Bayesian network. As acyclic ground probabilistic logic programs correspond to Bayesian networks on their dependency graph, we can compute conditional independencies from d-separation in the latter. In the present paper, we generalize the reasoning above to the non-ground case. First, we abstract the notion of a probabilistic logic program away from external databases and probabilities to obtain so-called program structures. We then present a correct meta-interpreter that decides whether a certain conditional independence statement is implied by a program structure on a given external database. Finally, we give a fragment of program structures for which we obtain a completeness statement of our conditional independence oracle. We close with an experimental evaluation of our approach revealing that our meta-interpreter performs significantly faster than checking the definition of independence using exact inference in ProbLog 2.

关键词： Bayesian networks

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A logic programming Approach to Global Logistics in a Co-Design Environment 39

A Logic Programming Approach to Global Logistics in a Co-Des...

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39th international conference on logic programming (iclp)

作者： Dietz, Emmanuelle Philipp, Tobias Schramm, Gerrit Zindel, Andreas Airbus Cent Res & Technol Manching Germany Secunet Secur Networks AG Essen Germany

In a co-design environment changes need to be integrated quickly and in an automated manner. this paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft within a co-design approach with respect to key performance indicators (like cost, time or resilience). the product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide. the goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system. the main motivation for approaching this challenge is to develop the industrial system in tandem with the product and making it more resilient against unforeseen events, reducing the risks of bottlenecks in the supply chain. this risk reduction ensures continued efficiency and operational success. To address this challenging and complex task we have chosen Answer Set programming (ASP) as the modeling language, formalizing the relevant requirements of the investigated industrial system. the approach presented in this paper covers three main aspects: the extraction of the relevant information from a knowledge graph, the translation into logic programs and the computation of existing configurations guided by optimization criteria. Finally we visualize the results for an effortless evaluation of these models. Internal results seem promising and yielded several new research questions for future improvements of the discussed use case.

关键词： Modeling languages

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Explainable Answer-set programming 39

Explainable Answer-set Programming

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39th international conference on logic programming (iclp)

作者： Geibinger, Tobias TU Wien Inst Log & Computat Knowledge Based Syst Grp Vienna Austria

the interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems. Answer-set programming (ASP) is used in many areas, among them are industrial optimisation, knowledge management or life sciences, and thus of great interest in the context of explainability. To ensure the successful application of ASP as a problem-solving paradigm in the future, it is thus crucial to investigate explanations for ASP solutions. Such an explanation generally tries to give an answer to the question of why something is, respectively is not, part of the decision produced or solution to the formulated problem. Although several explanation approaches for ASP exist, almost all of them lack support for certain language features that are used in practice. Most notably, this encompasses the various ASP extensions that have been developed in the recent years to enable reasoning over theories, external computations, or neural networks. this project aims to fill some of these gaps and contribute to the state of the art in explainable ASP. We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms, like contrastive explanations.

关键词： Knowledge management

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Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive logic programming Approach 39

Explainable and Trustworthy Traffic Sign Detection for Safe ...

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39th international conference on logic programming (iclp)

作者： Chaghazardi, Zahra Fallah, Saber Tamaddoni-Nezhad, Alireza Univ Surrey Dept Comp Sci Guildford Surrey England Univ Surrey Connected & Autonomous Vehicles Lab Guildford Surrey England

Traffic sign detection is a critical task in the operation of Autonomous Vehicles (AV), as it ensures the safety of all road users. Current DNN-based sign classification systems rely on pixel-level features to detect traffic signs and can be susceptible to adversarial attacks. these attacks involve small, imperceptible changes to a sign that can cause traditional classifiers to misidentify the sign. We propose an Inductive logic programming (ILP) based approach for stop sign detection in AVs to address this issue. this method utilises high-level features of a sign, such as its shape, colour, and text, to detect categories of traffic signs. this approach is more robust against adversarial attacks, as it mimics human-like perception and is less susceptible to the limitations of current DNN classifiers. We consider two adversarial attacking methods to evaluate our approach: Robust Physical Perturbation (PR2) and Adversarial Camouflage (AdvCam). these attacks are able to deceive DNN classifiers, causing them to misidentify stop signs as other signs with high confidence. the results show that the proposed ILP-based technique is able to correctly identify all targeted stop signs, even in the presence of PR2 and ADvCam attacks. the proposed learning method is also efficient as it requires minimal training data. Moreover, it is fully explainable, making it possible to debug AVs.

关键词： Traffic signs

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Enhancing ASP(Q) evaluation 22

Enhancing ASP(Q) evaluation

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22nd international conference of the Italian Association for Artificial Intelligence, AIxIA 2023 DP 2023

作者： Faber, Wolfgang Mazzotta, Giuseppe Ricca, Francesco Alpen-Adria Universität Klagenfurt Austria University of Calabria Rende Italy

is an extension of Answer Set programming (ASP) that enables the declarative and modular modeling of problems within the entire polynomial hierarchy. the first implementation of ASP(Q), known as qasp, utilized a translation to Quantified Boolean Formulae (QBF) to take advantage of the advanced and mature QBF-solving technology. However, the QBF encoding implemented by qasp is highly general, potentially leading to complex formulas that existing QBF solvers struggle to evaluate due to the large number of symbols and sub-clauses. In the paper titled "An efficient solver for ASP(Q) ", that has been presented during the 39th international conference on logic programming (iclp23), we introduced a novel implementation that builds upon the concepts of qasp and incorporates a more efficient encoding procedure, optimized encodings of ASP(Q) programs in QBF, and a machine learning model for the automatic selection of QBF-solving back-ends. In this paper, we give an overview of the results we have obtained in the above-mentioned paper and discuss possible future directions. © 2022 Copyright for this paper by its authors.

关键词： logic programming

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Learning to Break Symmetries for Efficient Optimization in Answer Set programming (Extended Abstract) 39

Learning to Break Symmetries for Efficient Optimization in A...

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39th international conference on logic programming, iclp 2023

作者： Tarzariol, Alice Gebser, Martin Schekotihin, Konstantin Law, Mark University of Klagenfurt Austria Graz University of Technology Austria ILASP Limited United Kingdom

the ability to efficiently solve hard combinatorial optimization problems is a key prerequisite to various applications of declarative programming paradigms. Symmetries in solution candidates pose a significant challenge to modern optimization algorithms since the enumeration of such candidates might substantially reduce their optimization performance. the full version of this paper proposes a novel approach using Inductive logic programming (ILP) to lift symmetry-breaking constraints for optimization problems modeled in Answer Set programming (ASP). Given an ASP encoding with optimization statements and a set of representative instances, our method augments ground ASP programs with auxiliary normal rules enabling the identification of symmetries by existing tools, like SBASS. then, the obtained symmetries are lifted to first-order constraints with ILP. We show the correctness of our method and evaluate it on optimization problems from the domain of automated configuration. Our experiments show significant improvements of optimization performance due to the learned first-order constraints. the full version of this paper was presented at AAAI 2023. © 2023 Open Publishing Association. All rights reserved.

关键词： Inductive logic programming (ILP)

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Reliable Natural Language Understanding with Large Language Models and Answer Set programming 39

Reliable Natural Language Understanding with Large Language ...

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39th international conference on logic programming (iclp)

作者： Rajasekharan, Abhiramon Zeng, Yankai Padalkar, Parth Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. they also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set programming (ASP). We show how LLMs can be used to effectively extract knowledge-represented as predicates-from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.

关键词： Stars

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Flexible Job-shop Scheduling for Semiconductor Manufacturing with Hybrid Answer Set programming (Extended Abstract) 39

Flexible Job-shop Scheduling for Semiconductor Manufacturing...

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39th international conference on logic programming, iclp 2023

作者： Ali, Ramsha El-Kholany, Mohammed M.S. Gebser, Martin University of Klagenfurt Austria Cairo University Egypt Graz University of Technology Austria

Modern semiconductor manufacturing involves complex production processes with hundreds of operations from lot release to completion, which often takes several months. In contrast to abstract job-shop scheduling benchmarks, our work addresses the scheduling of realistic semiconductor manufacturing processes. We model their sophisticated requirements by hybrid Answer Set programming and take advantage of difference logic for expressing machine processing, setup, and maintenance times. Unlike existing approaches that schedule semiconductor manufacturing processes locally by greedy heuristics or isolated optimization of specific machine groups, we explore the potential and limits of large-scale semiconductor fab scheduling. © 2023 Open Publishing Association. All rights reserved.

关键词： Job shop scheduling

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