Hydraulic engineering analysis of Water Distribution Systems (WDSs) is performed by running simulation softwares. the starting point to setup a simulation is to reconstruct the WDS topology. In many countries, includi...
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ISBN:
(纸本)9783031742088;9783031742095
Hydraulic engineering analysis of Water Distribution Systems (WDSs) is performed by running simulation softwares. the starting point to setup a simulation is to reconstruct the WDS topology. In many countries, including Italy, it often happens that data on the WDS is not readily available and accessible in digital format, and topology-reconstruction has to be carried out manually. this paper describes an application of Answer Set programming to partially automate the task of determining a WDS topology from geospatial data. Also, we report on a real-world use case that shows the feasibility of our approach.
In this paper, we present the integration of MiniZinc into ASP Chef, expanding its capabilities to include constraint programming alongside Answer Set programming (ASP). By leveraging the web assembly version of MiniZ...
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ISBN:
(纸本)9783031742088;9783031742095
In this paper, we present the integration of MiniZinc into ASP Chef, expanding its capabilities to include constraint programming alongside Answer Set programming (ASP). By leveraging the web assembly version of MiniZinc, this integration allows for running MiniZinc models directly in the browser, eliminating the need for additional software installations. this browser-based approach is particularly advantageous for educational settings and rapid prototyping, offering a seamless and accessible environment for learners and practitioners. To facilitate the incorporation of MiniZinc in ASP recipes, we have implemented a mapping mechanism that converts facts to MiniZinc data and vice versa. this integration not only broadens the scope of problems that can be addressed using ASP Chef but also simplifies the workflow for users, making it a versatile tool for complex computational tasks.
Explainability in Artificial Intelligence (XAI) is crucial for enhancing the transparency and trustworthiness of AI systems. Our work focuses on providing clear explanations for why certain atoms in a given answer set...
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ISBN:
(纸本)9783031742088;9783031742095
Explainability in Artificial Intelligence (XAI) is crucial for enhancing the transparency and trustworthiness of AI systems. Our work focuses on providing clear explanations for why certain atoms in a given answer set are evaluated as such, hence contributing to the understanding of the decisions made by Answer Set programming (ASP) systems. We employ simple inference rules to elucidate these decisions, avoiding complex derivations to maintain clarity. Moreover, we introduce the notion of preferred unit-provable unsatisfiable subsets (preferred 1-PUS) to identify relevant portions of ASP encodings, prioritizing program rules over assignments, withthe objective of minimizing the assumptions involved in the explanation process. the proposed principles are implemented in a new XAI system.
this article explores the integration of Structured Declarative Language (SDL) into ASP Chef, a low-code web application designed to facilitate the development of pipelines for combinatorial search and optimization. S...
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ISBN:
(纸本)9783031742088;9783031742095
this article explores the integration of Structured Declarative Language (SDL) into ASP Chef, a low-code web application designed to facilitate the development of pipelines for combinatorial search and optimization. SDL is a recent proposal aimed at simplifying the syntax of Answer Set programming (ASP), inspired by the straightforwardness of SQL. the integration is achieved through the implementation of a server that compiles SDL specifications into ASP programs and the addition of a new operation in ASP Chef to manage data exchange withthe server. this integration aims to streamline the development process and make it more accessible for users working on combinatorial optimization tasks.
Computational argumentation (CA) has emerged, in recent decades, as a powerful formalism for knowledge representation and reasoning in the presence of conflicting information, notably when reasoningnon-monotonically ...
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Computational argumentation (CA) has emerged, in recent decades, as a powerful formalism for knowledge representation and reasoning in the presence of conflicting information, notably when reasoningnon-monotonically with rules and exceptions. Much existing work in CA has focused, to date, on reasoning with given argumentation frameworks (AFs) or, more recently, on using AFs, possibly automatically drawn from other systems, for supporting forms of XAI. In this short paper we focus instead on the problem of learning AFs from data, with a focus on neuro-symbolic approaches. Specifically, we overview existing forms of neuro-argumentative (machine) learning, resulting from a combination of neural machine learning mechanisms and argumentative (symbolic) reasoning. We include in our overview neuro-symbolic paradigms that integrate reasoners with a natural understanding in argumentative terms, notably those capturing forms of non-monotonicreasoning in logicprogramming. We also outline avenues and challenges for future work in this spectrum.
the proceedings contain 16 papers. the special focus in this conference is on Functional and logicprogramming. the topics include: Term Evaluation Systems with Refinements: First-Order, Second-Order, and Contextual I...
ISBN:
(纸本)9789819722990
the proceedings contain 16 papers. the special focus in this conference is on Functional and logicprogramming. the topics include: Term Evaluation Systems with Refinements: First-Order, Second-Order, and Contextual Improvement;a Complete Dependency Pair Framework for Almost-Sure Innermost Termination of Probabilistic Term Rewriting;tabulation with Zippers;declarative Pearl: Rigged Contracts;System Description: DeepLLM, Casting Dialog threads into logic Programs;a Constraint-Based Mathematical Modeling Library in Prolog with Answer Constraint Semantics;grants4Companies: Applying Declarative Methods for Recommending and reasoning About Business Grants in the Austrian Public Administration (System Description);inferring non-failure Conditions for Declarative Programs;being Lazy When It Counts Practical Constant-Time Memory Management for Functional programming;MetaOCaml: Ten Years Later System Description;An ML-Style Module System for Cross-Stage Type Abstraction in Multi-stage programming;rhyme: A Data-Centric Multi-paradigm Query Language Based on Functional logic Metaprogramming System Description;language-parameterized Proofs for Functional Languages with Subtyping;system Description: A theorem-Prover for Subregular Systems: the Language Toolkit and Its Interpreter, Plebby.
this paper presents stableKanren, a miniKanren extension with normal logicprogramming support under stable model semantics. MiniKanren is a relational programming solver implemented atop Scheme via shallow embedding,...
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ISBN:
(纸本)9798400708121
this paper presents stableKanren, a miniKanren extension with normal logicprogramming support under stable model semantics. MiniKanren is a relational programming solver implemented atop Scheme via shallow embedding, which means the predicate in each rule is encoded as a goal function directly. the solver utilizes the pattern matching macro in Scheme to transform the input goal function and form a static search stream through continuations to achieve the essential features, resolution and unification, in Prolog. However, the static stream only works on monotonicreasoning. Even though the core miniKanren is designed to be easily modified and extended with new features, none of the existing extensions support solving normal logic programs. Also, no normal logic program solvers are based on a functional programming language. We identify and categorize the roles of resolution and unification in top-down solving. And we realize that a dynamic search stream is needed to support non-monotonicreasoning. So we evolve both resolution and unification with new roles, and we exploit the advantages of using macros and continuations further to weave the information generated during runtime into future streams dynamically. We create multiple innovative macros to compile the normal logic program into a program with its complement form, obtain the domain of a variable under different contexts, and generate the new search stream during solving. And we use the coinductive resolution to handle the loop in the normal logic program. In future work, we plan to apply bottom-up optimization to improve our top-down system performance and support various input rules.
the existing belief models based on knowledge science and engineering have not yet formed a complete theoretical system in terms of formation of knowledge, the representation of uncertainty, and the updating and revis...
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ISBN:
(数字)9789819754984
ISBN:
(纸本)9789819754977;9789819754984
the existing belief models based on knowledge science and engineering have not yet formed a complete theoretical system in terms of formation of knowledge, the representation of uncertainty, and the updating and revision of beliefs. To address this problem, this paper takes knowledge ecology as a main reference frame and proposes a belief evolution model using non-Axiomatic logic as a formalization way. the proposed belief systems organize perceived environmental information into evidence, and transform it into normative knowledge, commonsense knowledge, and intelligent strategies through a unified reasoning and learning approach. In addition, the model uniformly characterizes the randomness, vagueness, imprecision, ignorance and other kinds of uncertainty information existing in the knowledge with NAL, which provides a formal representation and semantic explanation of the shape of the knowledge at each stage. the proposed model not only reveals the developmental laws of the internal growth of knowledge, but also explicitly characterizes the transformation and developmental laws between information, knowledge and intelligence, reflecting the constructivist cognitive theory of knowledge and understanding established by Jean Piaget.
We propose an approach for reasoning about actions with domain descriptions including an EL perpendicular to ontology in a temporal action theory. the action theory is based on a Dynamic Linear Time Temporal logic, wh...
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ISBN:
(纸本)9783031157073;9783031157066
We propose an approach for reasoning about actions with domain descriptions including an EL perpendicular to ontology in a temporal action theory. the action theory is based on a Dynamic Linear Time Temporal logic, whose extensions are defined through temporal answer sets. the work provides conditions under which action consistency can be guaranteed with respect to an EL perpendicular to ontology, by polynomially encoding an EL perpendicular to knowledge base into a domain description of the temporal action theory.
An interesting feature that traditional approaches to inductive logicprogramming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logicprogramming have been rec...
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ISBN:
(纸本)9783031157073;9783031157066
An interesting feature that traditional approaches to inductive logicprogramming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logicprogramming have been recently proposed to combine the advantages of inductive logicprogramming, in terms of interpretability and generalization capability, withthe characteristic capacity of deep learning to treat noisy and nonlogical data. this paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logicprogrammingthat have been proposed in the last five years. the considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning.
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