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48748 results in Computer Science

Page 7 of 2438

  • 8 - Linear Logic Programming

  • Dale Miller, INRIA Saclay-Île-de-France
  • Book:
    Proof Theory and Logic Programming
    Published online:
    01 December 2025
    Print publication:
    18 December 2025, pp 172-186

  • Summary

    This chapter explores how logic programs can exploit linear logic. It provides several examples of specifying computations using linear logic, such as computing on collections of natural numbers and organizing simple theorem provers. It also illustrates how conventional program tasks, such as reversing a list or generating all permutations of a list, can be achieved in novel ways using linear logic programs. The chapter also demonstrates an alternative approach to specifying sequent calculus proof systems as logic programs. Bibliographic notes offer further references to the specification of logic programs.

  • 13 - Formalizing Operational Semantics

  • Dale Miller, INRIA Saclay-Île-de-France
  • Book:
    Proof Theory and Logic Programming
    Published online:
    01 December 2025
    Print publication:
    18 December 2025, pp 261-279

  • Summary

    This chapter focuses on using logic programming to formalize the operational semantics of programming languages. It illustrates this with examples such as the call-by-value lambda calculus and the pi-calculus. The chapter discusses how to encode transition systems in sequent calculus and abstract machines as binary logic programs. It explores using linear logic to add side-effects to evaluation and specify concurrency primitives. Bibliographic notes direct the reader to relevant literature on formal semantics and logic-based approaches to language specification.

  • Inductive Learning for Possibilistic Logic Programs Under Stable Models

  • HONGBO HU, YISONG WANG, YI HUANG, KEWEN WANG
  • Journal:
    Theory and Practice of Logic Programming , First View
    Published online by Cambridge University Press:
    18 December 2025, pp. 1-50
    • Article
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  • Possibilistic logic programs (poss-programs) under stable models are a major variant of answer set programming. While its semantics (possibilistic stable models) and properties have been well investigated, the problem of inductive reasoning has not been investigated yet. This paper presents an approach to extracting poss-programs from a background program and examples (parts of intended possibilistic stable models). To this end, the notion of induction tasks is first formally defined, its properties are investigated and two algorithms ilpsm and ilpsmmin for computing induction solutions are presented. An implementation of ilpsmmin is also provided and experimental results show that when inputs are ordinary logic programs, the prototype outperforms a major inductive learning system for normal logic programs from stable models on the datasets that are randomly generated.

  • 5 - Two Abstract Logic Programming Languages

  • Dale Miller, INRIA Saclay-Île-de-France
  • Book:
    Proof Theory and Logic Programming
    Published online:
    01 December 2025
    Print publication:
    18 December 2025, pp 61-106

  • Summary

    This chapter introduces two abstract logic programming languages, one based on first-order Horn clauses (fohc) and another on first-order hereditary Harrop formulas (fohh). It shows how these can be understood within the framework of uniform proofs in intuitionistic logic. The chapter discusses the limitations of these languages. It also explores the concept of focused proofs as a way to structure proof search. The chapter proves the completeness of focused proofs for a fragment of intuitionistic logic. Bibliographic notes provide references to related work in logic programming theory.

  • 10 - Specifying Computations Using Multisets

  • Dale Miller, INRIA Saclay-Île-de-France
  • Book:
    Proof Theory and Logic Programming
    Published online:
    01 December 2025
    Print publication:
    18 December 2025, pp 216-228

  • Summary

    This chapter focuses on specifying computations using multisets within a logic programming framework. It illustrates this by encoding numerals, letters, and words as multisets. The chapter provides examples of encoding computational models such as finite automata and pushdown automata using linear logic and multisets. It also touches upon the properties of these encodings. Bibliographic notes point to related work on multiset rewriting and its applications.

Page 7 of 2438