• Suche: "logic programming"

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81

Learning differentiable logic programs for abstract visual reasoning
Hikaru Shindo ; Viktor Pfanschilling ; Devendra Singh Dhami ; et al.
Machine Learning. 113:8533-8584

Machine Learning FOS: Computer and inform... Artificial Intelligence... Differentiable reasoning Artificial Intelligence Computer Vision and Patt... …
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82

Versioning Virtual Reconstruction Hypotheses: Revealing Counterfactual Trajectories of the Fallen Voussoirs of Notre-Dame de Paris Using Reasoning and 2D/3D Visualization
Guillem, Anaïs ; Samuel, John ; Gesquière, Gilles ; et al.
Lecture Notes in Computer Science ISBN: 9783031789540

Virtual reconstruction scientific narrative logic programming Notre-Dame de Paris counterfactual reasoning 3D visualization …
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83

HYBRID MODELS FOR COUPLING DEDUCTIVE AND INDUCTIVE REASONING: REVISED SELECTED PAPERS
INTERNATIONAL WORKSHOP ON HYBRID MODELS FOR COUPLING DEDUCTIVE AND INDUCTIVE REASONING (3RD: 2024: SANTIAGO DE COMPOSTELA, SPAIN) ; PIERANGELA BRUNO, Editor

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84

LOGIC PROGRAMMING AND NONMONOTONIC REASONING
LPNMR (CONFERENCE) (17TH : 2024 : DALLAS, TX) ; CARMINE DODARO, Editor

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85

Inductive Programming Meets the Real World.
GULWANI, SUMIT ; HERNÁNDEZ-ORALLO, JOSÉ ; KITZELMANN, EMANUEL ; et al.
Communications of the ACM. Nov2015, Vol. 58 Issue 11, p90-99. 10p. 1 Color Photograph, 2 Diagrams, 7 Charts.

APPLICATION software ELECTRONIC spreadsheets SCRIPTING languages (Com... INDUCTIVE logic programm... END users (Information t... INTELLIGENT tutoring sys...
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86

Analyzing Long-Term Dynamics of Biological Networks with Answer Set Programming
Ben Abdallah, Emna ; Folschette, Maxime ; Magnin, Morgan ; et al.
Systems Biology Modelling and Analysis: Formal Bioinformatics Methods and Tools, 2022

Answer Set Programming (... logic programming attractors stable states dynamical analysis [INFO.INFO-MO]Computer S... …
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87

Inductive Learning from State Transitions over Continuous Domains
Ribeiro, Tony ; Tourret, Sophie ; Folschette, Maxime ; et al.
27th International Conference on Inductive Logic Programming. 10759(27th International Conference on Inductive Logic Programming, LNCS, volume 10759, Springer, Cham, pp.124-139, 2018, Inductive Logic Programming, ⟨10.1007/978-3-319-78090-0_9⟩):124-139

Continuous Logic Program... Inductive Logic Programm... learning from interpreta... dynamical systems [INFO.INFO-MO]Computer S... Modeling and Simulation …
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88

CHC-COMP 2023: Competition Report
De Angelis Emanuele ; Hari Govind V. K.
Electronic Proceedings in Theoretical Computer Science. 402:83-104

Constraint Solving FOS: Computer and inform... Computer Science - Logic... Satisfiability Modulo Th... Constraint Logic Program... Constrained Horn Clauses …
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89

A Mathematical Conceptualization of Bundle Sets in Defeasible Logic Programming
Yamil Osvaldo Soto ; Cristhian Ariel David Deagustini ; Maria Vanina Martinez ; et al.
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. :779-786

defeasible reasoning argumentation lines model-theoretic semantic... structured argumentation defeasible logic program...
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90

A Proof-Theoretic Approach to Certifying Skolemization
Chaudhuri, Kaustuv ; Manighetti, Matteo ; Miller, Dale ; et al.
CPP 2019 - 8th ACM SIGPLAN International Conference. :78-90

Cascais, Portugal Focusing Foundational proof certi... Keywords Skolemization Constraint and logic pro... Proof theory …
Konferenz
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91

Lisp for Robotics: Implementing Graph Traversal.
Ray, Dipankar
Open Source For You; Aug2025, Vol. 13 Issue 10, p70-73, 4p

GRAPH algorithms ARTIFICIAL intelligence LOGIC programming HEURISTIC algorithms SEARCH algorithms SYMBOLIC computation
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92

Sequent calculi and an efficient theorem prover for conditional logics with selection function semantics
Gliozzi, Valentina ; Pozzato, Gian Luca ; Tessore, Gabriele ; et al.
Journal of Logic and Computation. 34:1430-1468

proof methods, sequent c... 0202 electrical engineer... 0102 computer and inform... 02 engineering and techn... 01 natural sciences
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93

Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
FIEKE HILLERSTRÖM ; GERTJAN BURGHOUTS ; DSS - Defence, Safety & Security ; et al.
Theory and Practice of Logic Programming, 24(4), pp. 628-643.

FOS: Computer and inform... Computer Science - Machi... Artificial Intelligence... Sensory data Computer Science - Artif... Probabilistic background... …
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94

Abstract Environment Trimming
DANIEL JURJO-RIVAS ; JOSE F. MORALES ; PEDRO LÓPEZ-GARCÍA ; et al.
Theory and Practice of Logic Programming, ISSN 1471-0684, 2024-07-01, Vol. 24, No. 4
Archivo Digital UPM
Universidad Politécnica de Madrid

Informática FOS: Computer and inform... Computer Science - Progr... PROLOG Securit Logic programming method... …
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95

Analytical learning and term-rewriting systems / Philip Laird and Evan Gamble.
Laird, Philip

Machine learning. Calculus. Algorithms. Logic programming. Mathematical models.
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96

Optimistic Parallelism Requires Abstractions.
Kulkarni, Milind ; Pingali, Keshav ; Walter, Bruce ; et al.
Communications of the ACM. Sep2009, Vol. 52 Issue 9, p89-97. 9p. 9 Diagrams, 3 Graphs.

DATA structures ALGORITHMS DATA mining PARALLEL logic programmi... ABSTRACT thought MICROPROCESSORS …
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97

Applications of Embedded System Using AI: A Review
Ashna Farheen ; Zoha Fatima ; Muskan Tahura

Attribute grammars, logi... embedded intelligence model compression resource-constrained dev... hard- ware accelerator internet of things
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98

Inductive learning of robot task knowledge from raw data and online expert feedback
Meli, Daniele ; Fiorini, Paolo
Machine Learning. 114(4)

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99

Block structured scheduling using constraint logic programming.
Bădică, Amelia ; Bădică, Costin ; Ivanović, Mirjana
AI Communications. 2020, Vol. 33 Issue 1, p41-57. 17p.

PROCESS optimization ORDER picking systems MATHEMATICAL models CONSTRAINT programming LOGIC programming
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100

Scientific Knowledge Discovery Using Inductive Logic Programming.
Muggleton, Stephen
Communications of the ACM. Nov99, Vol. 42 Issue 11, p42-46. 5p.

DATA mining PHARMACEUTICAL industry... ARTIFICIAL intelligence LOGIC programming MACHINE learning RULES
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