Dynamic Programming Algorithms as Products of Weighted Logic Programs

Cohen, Shay B.; Simmons, Robert J.; Smith, Noah A.

doi:10.1007/978-3-540-89982-2_18

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Similar to the CNS and hormonal system, hormones cannot reach the high speed and complex role of the CNS, but they generate changes in the whole body and affect the CNS. The use of weighted logic can allow the implementation of DAG models that facilitate dynamic algorithms ( Devienne and Lebegue, 1986 ; Cohen et al, 2008 ). The weighted way of operating can capture the way by which hormones act in biological systems (bodies): creating a dynamical remapping of the values being computable, activating or silencing signals which activate responses or modular performances.…”

Section: High-level Design Of the Approachmentioning

confidence: 99%

Hormonal computing: a conceptual approach

Vallverdú,

Talanov,

Leukhin

et al. 2023

Front. Chem.

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This paper provides a conceptual roadmap for the use of hormonal bioinspired models in a broad range of AI, neuroengineering, or computational systems. The functional signaling nature of hormones provides an example of a reliable multidimensional information management system that can solve parallel multitasks. Two existing examples of hormonal computing bioinspired possibilities are shortly reviewed, and two novel approaches are introduced, with a special emphasis on what researchers propose as hormonal computing for neurorehabilitation in patients with complete spinal cord injuries. They extend the use of epidural electrical stimulation (EES) by applying sequential stimulations to limbs through prostheses. The prostheses include various limb models and are connected to a neurostimulation bus called the central pattern generator (CPG). The CPG bus utilizes hormonal computing principles to coordinate the stimulation of the spinal cord and muscles.

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Section: High-level Design Of the Approachmentioning

confidence: 99%

Hormonal computing: a conceptual approach

Vallverdú,

Talanov,

Leukhin

et al. 2023

Front. Chem.

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“…Therefore, non-local parameterizations break semiringweighted deduction, because we can no longer use the same logic under all semirings. We need new logics; for this we will use a logic programming transform called the PRODUCT transform (Cohen et al, 2008). We first define a logic for the non-local parameterization.…”

Section: Adding Non-local Parameterizations With the Product Transformmentioning

confidence: 99%

“…We can also compute the Viterbi derivation or the sum over all derivations of a training example, needed for some parameter estimation methods. Cohen et al (2008) derive an alignment logic for ITG from the product of two CKY logics.…”

Section: Alignmentmentioning

confidence: 99%

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Translation as weighted deduction

Lopez¹

2009

Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

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We present a unified view of many translation algorithms that synthesizes work on deductive parsing, semiring parsing, and efficient approximate search algorithms. This gives rise to clean analyses and compact descriptions that can serve as the basis for modular implementations. We illustrate this with several examples, showing how to build search spaces for several disparate phrase-based search strategies, integrate non-local features, and devise novel models. Although the framework is drawn from parsing and applied to translation, it is applicable to many dynamic programming problems arising in natural language processing and other areas.

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Probabilistic (logic) programming concepts

2015

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A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages.To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation.While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years.

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Dynamic Programming Algorithms as Products of Weighted Logic Programs

Cited by 6 publications

References 18 publications

Hormonal computing: a conceptual approach

Hormonal computing: a conceptual approach

Translation as weighted deduction

Probabilistic (logic) programming concepts

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