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logical English Demonstration 39

Logical English Demonstration

引用

39th international conference on logic programming, iclp 2023

作者： Kowalski, Robert Dávila, Jacinto Department of Computing Imperial College London United Kingdom Universidad de Los Andes Merida Venezuela

logical English (LE) is a natural language syntax for pure Prolog and other logic programming languages, such as ASP and s(CASP). Its main applications until now have been to explore the representation of a wide range of legal texts, helping to identify ambiguities, explore alternative representations of the same text, and compare the logical consequences of the alternatives. the texts include portions of loan agreements, accountancy law, Italian citizenship, EU law on criminal rights, international Swaps and Derivative contracts, and insurance contracts. the current implementation in SWI Prolog can be accessed at https://***. © 2023 Open Publishing Association. All rights reserved.

关键词： logic programming

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Summary of Statistical Statements in Probabilistic logic programming 39

Summary of Statistical Statements in Probabilistic Logic Pro...

引用

39th international conference on logic programming, iclp 2023

作者： Azzolini, Damiano Bellodi, Elena Riguzzi, Fabrizio University of Ferrara Italy

Statistical statements, also called Type 1 statements by Halpern, have a new representation in terms of Probabilistic Answer Set programming under the credal semantics. In this extended abstract we summarize that cont... 详细信息

关键词： Semantics

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A Dataset of Contractual Events in Court Decisions 39

A Dataset of Contractual Events in Court Decisions

引用

39th international conference on logic programming workshops, iclp workshops 2023

作者： Paulino-Passos, Guilherme Satoh, Ken Toni, Francesca Department of Computing Imperial College London United Kingdom Principles of Informatics Research Division National Institute of Informatics Tokyo Japan

the promise of automation of legal reasoning is developing technology that reduces human time required for legal tasks or that improves human performance on such tasks. In order to do so, different methods and systems based on logic programming were developed. However, in order to apply such methods on legal data, it is necessary to provide an interface between human users and the legal reasoning system, and the most natural interface in the legal domain is natural language, in particular, written text. In order to perform reasoning in written text using logic programming methods, it is then necessary to map expressions in text to atoms and predicates in the formal language, a task referred generally as information extraction. In this work, we propose a new dataset for the task of information extraction, in particular event extraction, in court decisions, focusing on contracts. Our dataset captures contractual relations and events that affect them in some way, such as negotiations preceding a (possible) contract, the execution of a contract, or its termination. We conducted text annotation with law students and graduates, resulting in a dataset with 207 documents, 3934 sentences, 4440 entities, and 1794 events. We describe here this resource, the annotation process, its evaluation with inter-annotator agreement metrics, and discuss challenges during the development of this resource and for the future. 2023 Copyright for this paper by its authors.

关键词： logic programming

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Semantically Guided Scene Generation via Contextual Reasoning and Algebraic Measures 39

Semantically Guided Scene Generation via Contextual Reasonin...

引用

39th international conference on logic programming workshops, iclp workshops 2023

作者： Bozzato, Loris Eiter, thomas Kiesel, Rafael Stepanova, Daria Fondazione Bruno Kessler Via Sommarive 18 Trento38123 Italy Bosch Center for Artificial Intelligence Renningen Germany Institute of Logic and Computation Technische Universität Wien Favoritenstraße 9-11 ViennaA-1040 Austria

We recently presented the MR-CKR framework to reason with knowledge overriding across contexts organized in multi-relational hierarchies. Reasoning is realized via ASP with Algebraic Measures, allowing for flexible definitions of preferences. In this paper, we show how to apply our theoretical work to autonomous-vehicle scene data: we apply MR-CKR to the problem of generating challenging scenes for autonomous vehicle learning. In practice, most of the scene data for AV learning models common situations, thus it might be difficult to capture cases where a particular situation occurs (e.g. partial occlusions of a crossing pedestrian). the MR-CKR model allows for data organization exploiting the multi-dimensionality of such data (e.g., temporal and spatial dimension). Reasoning over multiple contexts enables the verification and configuration of scenes, using the combination of different scene ontologies. We describe a framework for semantically guided data generation, based on a combination of MR-CKR and algebraic measures. the framework is implemented in a proof-of-concept prototype exemplifying some cases of scene generation. 2023 Copyright for this paper by its authors.

关键词： Autonomous vehicles

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Legal Compliance Checking of Autonomous Driving with Formalized Traffic Rule Exceptions 39

Legal Compliance Checking of Autonomous Driving with Formali...

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39th international conference on logic programming workshops, iclp workshops 2023

作者： Manas, Kumar Paschke, Adrian Freie Universität Berlin Department of Computer Science and Mathematics Germany Fraunhofer FOKUS Germany

Autonomous driving (AD) systems need to obey traffic rules and sometimes execute critical maneuvers that breach existing rules to ensure safe and rule-compliant driving. To endow such legal knowledge to the AD module, we need to formalize rules considering expressiveness, decidability, scalability, and adaptability. this paper critically examines possible formalization methods and demonstrates how we can model traffic rule exceptions for compliance checking of AD models. this ensures that AD systems are safe and can identify situations requiring more complex reasoning, such as exempting ongoing rule processes. We formalize legal traffic rule exceptions hierarchically and modularly in temporal logic and ground them to sensor data for assessing model compliance. Moreover, we introduce a parsed tree structure that supports and aids neural network-based models with formal rules. We evaluate our approach by monitoring vehicle trajectories against formalized traffic rules and handling rule exceptions in various traffic scenarios. Our results show that our approach can effectively represent complex traffic rules and monitor the safety and efficiency of AD systems against legal specifications. this paper contributes to the field of legal reasoning and compliance checking by providing a methodology for formalizing traffic rules from a rule-exception perspective in a machine-readable form based on sensor data limitations. 2022 Copyright for this paper by its authors.

关键词： Formal logic

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Bottom-Up Stratified Probabilistic logic programming with Fusemate 39

Bottom-Up Stratified Probabilistic Logic Programming with Fu...

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39th international conference on logic programming (iclp)

作者： Baumgartner, Peter Tartaglia, Elena CSIRO Data61 Canberra ACT Australia CSIRO Data61 Melbourne Vic Australia

this paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.

关键词： Markov processes

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Benchmarking for Integrating logic Rules with Everything Else 39

Benchmarking for Integrating Logic Rules with Everything Els...

引用

39th international conference on logic programming (iclp)

作者： Liu, Yanhong A. Stoller, Scott D. Tong, Yi Tekle, K. Tuncay SUNY Stony Brook Comp Sci Dept Stony Brook NY 11794 USA

Integrating logic rules with other language features is increasingly sought after for advanced applications that require knowledge-base capabilities. To address this demand, increasingly more languages and extensions for such integration have been developed. How to evaluate such languages? this paper describes a set of programming and performance benchmarks for evaluating languages supporting integrated use of rules and other features, and the results of evaluating such an integrated language together with logic languages and languages not supporting logic rules.

关键词： Benchmarking

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Demonstrating (Hybrid) Active logic Documents and the Ciao Prolog Playground, and an Application to Verification Tutorials 39

Demonstrating (Hybrid) Active Logic Documents and the Ciao P...

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39th international conference on logic programming (iclp)

作者： Ferreiro, Daniela Morales, Jose F. Abreu, Salvador Hermenegildo, Manuel V. Univ Politecn Madrid UPM Madrid Spain IMDEA Software Inst Madrid Spain Univ Evora NOVA LINCS Evora Portugal

We propose a demonstration of the Active logic Documents (ALDs) approach and the Ciao Playground, as well as a recent extension to ALDs to facilitate the integration of other tools into the system for creating Hybrid ... 详细信息

关键词： Computer circuits

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Semantic Analysis of Assurance Cases using s(CASP) 39

Semantic Analysis of Assurance Cases using s(CASP)

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39th international conference on logic programming workshops, iclp workshops 2023

作者： Murugesan, Anitha Wong, Isaac Hong Stroud, Robert Arias, Joaquín Salazar, Elmer Gupta, Gopal Bloomfield, Robin Varadarajan, Srivatsan Rushby, John Honeywell Aerospace United States United Kingdom CETINIA - Universidad Rey Juan Carlos Spain University of Texas Dallas United States SRI International United States

the use of assurance cases is gaining popularity, particularly in the safety-critical system industry, as an organized approach to submitting documentation for the safety and security certification of systems. However, these arguments can become overwhelming and complicated, even for moderately complex systems. therefore, there is a compelling requirement to develop new automation strategies that can aid in creating and assessing assurance cases. Existing assurance-case tools primarily automate syntactic analysis, focusing on structural completeness, while providing limited or no support for semantically evaluating the logical aspects of the assurance case. In prior work, we introduced a framework called Assurance 2.0, which aims to enhance the rigor of assurance cases by emphasizing the reasoning process, evidence utilized, and explicit identification of counter-claims (defeaters) and counter-evidence. In this paper, we present a new approach to enhancing Assurance 2.0 by incorporating semantic rule-based analysis capabilities. Firstly, we systematically convert the assurance case into Prolog predicates and constraints. then, leveraging the analysis capabilities of the s(CASP), a goal-directed top-down solver for Constraints Answer Set Programs, we evaluate the semantic properties of assurance cases, including logical consistency, completeness, and indefeasibility. the application of these analyses provides both authors and evaluators with higher confidence when assessing the assurance case. 2023 Copyright for this paper by its authors.

关键词： Syntactics

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Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational logic Techniques 39

Beyond Traditional Neural Networks: Toward adding Reasoning ...

引用

39th international conference on logic programming (iclp)

作者： Rafanelli, Andrea Univ Pisa Dept Comp Sci Pisa Italy Univ Laquila Dept Informat Engn Comp Sci & Math Laquila Italy

Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues. Neuro-SymbolicAI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning. Symbolic Knowledge Injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems. this work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS).

关键词： Computer circuits

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