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检索条件"主题词=Logic programming"

共 8407 条记录，以下是461-470 订阅

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Step: A Highly Expressive Text Generation Language 18

Step: A Highly Expressive Text Generation Language

引用

18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2022

作者： Horswill, Ian Northwestern University United States

ISBN: (纸本)1577358775

Games often generate text from human-authored templates and adapt that text to relevant context. Many systems have been developed to aid this process using techniques such as context-free grammars, randomization, logic programming, global state, and HTN planning. In this paper, I present Step, a novel programming language for text generation. So far as I can determine, previous techniques can all be implemented within Step using a few lines of code. This allows designers to mix-and-match features as needed, without having to write an interpreter for a new language. While extremely expressive as a programming language, Step is also intended to allow writers to add new text to an existing system with minimal markup and little knowledge of programming. In a head-to-head comparison, the Step implementation was more compact than, and used less markup than, a Prolog implementation of the same generator. Copyright © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： logic programming

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An Automatically Inferable Format for Creating Big Data of Morals, Ethics and ORDER OF VALUES Written Almost in Natural Language

An Automatically Inferable Format for Creating Big Data of M...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Nakamura, Keisuke Ando, Tatsuyoshi Development and Sales Room KnowrelSystem Inc. Ishikawa-ken Kanazawa-shi Japan

ISBN: (纸本)9781665480451

If there is a format that is easy for both humans and AI (computers) to understand and infer, and also if the humans let the AI learn various morals, ethics and values in the above format, these data may be operated automatically by the AI in a way that the humans concerned are able to check for mistakes / lacks of thoughts in the automatic judgments by the AI, avoiding arbitrary thoughts / judgments by both the providers(humans) of such data and the *** the other hand, morals, etc. are diverse depending on the country, history, culture, social position, and/or individual. So in order to accurately express such data for reducing interpretation mistakes, lacks of required conditions of thought-rules and so on, we need to allow, as such data, expressions almost in (or from completely including all) the natural languages used in each cultural *** this paper, we (1) propose a format based on natural language that we called as "situation-specific-value-order-formula"as a starting point for an expression format that satisfy the above needs, (2) explain its components: situations / viewpoints and items ( positive / 0 / negative ) ordered by the value all based on natural language, (3) explain related things including the specific l ogical i nference f eature o f t he n atural language based programming where variables are embedded in the natural *** conclude that our proposed format is feasible and practical to some extent for the above Purpose and need. © 2022 IEEE.

关键词： logic programming

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Space Syntax with Prolog 13

Space Syntax with Prolog

引用

13th International Space Syntax Symposium, SSS 2022

作者： Fernandes, Pedro Afonso Portugal

ISBN: (纸本)9788293677673

This paper introduces a new way to perform space syntax analyses using Prolog, a logic programming language concerned with Artificial Intelligence. Developed in the 1970’s to process natural languages, Prolog can deal easily with simple declarations of facts like the connection (or permeability) between convex spaces or axial lines. Readily available on-line through the SWISH platform, in a fancy format inspired by Jupyter Notebooks, Prolog may help to understand the recursive nature of urban processes, given some elementary generators, or to describe or even check the structure (e.g. concentric) of some village. Mostly important, Prolog can compute space syntax measures such connectivity, control or integration in a comprehensive, transparent and attractive way, namely, for students and researchers on space syntax. The experience suggests that Prolog may be appropriate for gamma-analysis of small buildings like the Ashanti’s shrine, acting as flexible and easily replicable calculator of syntactic measures. This flexibility is reinforced by the free and open-source nature of the code stored in the SWISH platform, as well as by the declarative nature of logic programming, which facilitates the description of the patterns of discrete systems as social knowables. In fact, a Prolog program represents a certain amount of knowledge, which is used to answer queries about the social and economic consequences of some spatial design. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

关键词： logic programming

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CARING for xAI 37

CARING for xAI

引用

37th Italian Conference on Computational logic, CILC 2022

作者： Bertini, Flavio Dal Palù, Alessandro Fabiano, Francesco Iotti, Eleonora Department of Mathematical Physical and Computer Sciences University of Parma Parco Area delle Scienze 53/A Parma43124 Italy

Over the last few years, Artificial Intelligence (AI) has pervaded our lives. As a result, automated tools that "reason" on different scenarios have become more and more common. As this trend continues to grow, it has become necessary to ensure that newly developed tools and technologies can be safely adopted, as demonstrated by the numerous EU regulations. This is especially true when the concept of AI is intertwined with the field of medicine, where every decision may be critical. That is why, in this work, we decided to tackle the problem of automated interpretation of Computed Tomography (CT) scans using an explainable approach. In fact, while several methods based on Machine Learning (ML) are currently available, these are still outperformed by medical doctors and provide answers that cannot be traced back to a logical deduction. This paper presents CARING, a new methodology based on Answer Set programming (ASP), which returns reliable, easy-to-program and explainable interpretations of CT scans. In particular, CARING makes use of transparent technologies in order to handle medical knowledge provided either by experts or by verified ontologies. This proof of concept shows that logic programming is a mature technology that can match the newest challenges in the xAI field. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： logic programming

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A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure 15th

A Comparison of ASP-Based and SAT-Based Algorithms for th...

引用

15th International Conference on Scalable Uncertainty Management, SUM 2022

作者： Kuhlmann, Isabelle Gessler, Anna Laszlo, Vivien Thimm, Matthias Artificial Intelligence Group University of Hagen Hagen Germany

ISBN: (纸本)9783031188428

We propose an algorithm based on satisfiability problem (SAT) solving for determining the contension inconsistency degree in propositional knowledge bases. In addition, we present a revised version of an algorithm based on answer set programming (ASP), which serves the same purpose. In an experimental analysis, we compare the two algorithms to each other, as well as to a naive baseline method. Our results demonstrate that both the SAT and the ASP approach expectedly outperform the baseline algorithm. Further, the revised ASP method is not only superior to the SAT approach, but also to its predecessors from the literature. Hence, it poses a new state of the art. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： logic programming

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Abduction in (Probabilistic) Answer Set programming 37

Abduction in (Probabilistic) Answer Set Programming

引用

37th Italian Conference on Computational logic, CILC 2022

作者： Azzolini, Damiano Bellodi, Elena Riguzzi, Fabrizio Dipartimento di Scienze dell'Ambiente e della Prevenzione University of Ferrara Ferrara Italy Dipartimento di Ingegneria University of Ferrara Ferrara Italy Dipartimento di Matematica e Informatica University of Ferrara Ferrara Italy

Answer Set programming (ASP) is a branch of logic programming particularly useful for representing complex domains. logic abduction, the reasoning strategy that deals with incomplete data, is tightly related to ASP, and encodes incompleteness through abducibles. The goal of logic abduction is to find the minimal set of abducibles (where minimality is usually considered in terms of set inclusion) that explains a query. Recently, abductive reasoning has been introduced in the context of Probabilistic logic programming, but no solutions are available for Probabilistic Answer Set programming (PASP). In this paper, we close this gap and propose an algorithm to perform abduction both in ASP and in PASP. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： logic programming

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An Application of ASP for Procedural Content Generation in Video Games 37

An Application of ASP for Procedural Content Generation in V...

引用

37th Italian Conference on Computational logic, CILC 2022

作者： De Seta, Andrea Alviano, Mario Rende87036 Italy

Procedural content generation eases and accelerates the development of video games by creating data algorithmically through a combination of human-generated assets and algorithms usually coupled with computer-generated randomness. This paper presents a use case of Answer Set programming (ASP) for procedural content generation of levels in a rougelike video game powered by the Godot Engine. The main elements of a set of human-generated rooms are represented by ASP facts, among them positions of doors, presence of treasures and power-ups. Within this knowledge, ASP is asked to generate dungeons satisfying a few conditions, among them the correct positioning of rooms, the absence of unreachable rooms and constraints on the occurrences of rooms. Scalability of ASP in this context is evaluated empirically, showing that it can generate in few seconds levels that comprise thousands of rooms. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： logic programming

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BEWARE-22: Bringing together researchers to address the logical, ethical, and epistemological challenges of AI 1

BEWARE-22: Bringing together researchers to address the logi...

引用

1st Workshop on Bias, Ethical AI, Explainability and the Role of logic and logic programming, BEWARE 2022

作者： Boella, Guido D’Asaro, Fabio Aurelio Dyoub, Abeer Primiero, Giuseppe Department of Computer Science University of Turin Turin Italy Ethos Group Department of Human Sciences University of Verona Verona Italy DISIM University of L’Aquila L’Aquila Italy LUCI Group Department of Philosophy University of Milan Milan Italy

The BEWARE-22 workshop, held on December 2, 2022 in Udine, Italy, focused on emerging ethical aspects of artificial intelligence, with a particular emphasis on bias, risk, explainability, and the role of logic and logic programming. The invited speaker, Francesca Alessandra Lisi, gave a talk on "Ethics & Gender for a Responsible Research and Innovation in AI," exploring the intersection of ethics and gender in the context of responsible research and innovation in artificial intelligence. The workshop program consisted of three sessions: "logic for AI", "Technical Approaches to XAI", and "Conceptual Views," which this short preface aims to describe. In total, 13 papers were accepted for the workshop, with 5 accepted as long papers and 8 as short papers. The proceedings include 12 papers out of the 13 from the workshop, plus an invited abstract, and will hopefully serve as a valuable resource for researchers and practitioners working on the ethical aspects of AI, inspiring further discussions and collaborations in this critical area of research. © 2022 Copyright for this paper by its authors.

关键词： logic programming

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Care Robots Learning Rules of Ethical Behavior Under the Supervision of an Ethical Teacher 1

Care Robots Learning Rules of Ethical Behavior Under the Sup...

引用

1st International Workshop on HYbrid Models for Coupling Deductive and Inductive ReAsoning and the 29th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, HYDRA-RCRA 2022

作者： Dyoub, Abeer Costantini, Stefania Letteri, Ivan DISIM University of L’Aquila Italy

Care robots are viewed as promising technological development that has the potential to mitigate the increasing imbalance between the number of older adults needing care and a decreasing number of caregivers. However, there are growing concerns about the ethical behavior of these robots. In this work, we show how care robots can learn logical ethical rules of behavior from experience under the supervision of human teacher. © 2022 Copyright for this paper by its authors.

关键词： logic programming

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CROSSBEAM: LEARNING TO SEARCH IN BOTTOM-UP PROGRAM SYNTHESIS 10

CROSSBEAM: LEARNING TO SEARCH IN BOTTOM-UP PROGRAM SYNTHESIS

引用

10th International Conference on Learning Representations, ICLR 2022

作者： Shi, Kensen Dai, Hanjun Ellis, Kevin Sutton, Charles Google Research Cornell University United States

Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable as the size of the desired program increases. To tame the search space blowup, we propose training a neural model to learn a hands-on search policy for bottom-up synthesis, instead of relying on a combinatorial search algorithm. Our approach, called CROSSBEAM, uses the neural model to choose how to combine previously-explored programs into new programs, taking into account the search history and partial program executions. Motivated by work in structured prediction on learning to search, CROSSBEAM is trained on-policy using data extracted from its own bottom-up searches on training tasks. We evaluate CROSSBEAM in two very different domains, string manipulation and logic programming. We observe that CROSSBEAM learns to search efficiently, exploring much smaller portions of the program space compared to the state-of-the-art. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： logic programming

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