Constraint Answer Set Programming without Grounding

Arias, Joaquín; Carro, Manuel; Salazar, Elmer; Marple, Kyle; Gupta, Gopal

doi:10.1017/s1471068418000285

Cited by 66 publications

(82 citation statements)

References 19 publications

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“…-Availability of well known and mature solvers to be used such as CLASP and DLV. Furthermore, new interesting solvers such as s(CASP) [1,47] have potential interest in our problem. It is worth to mention that Prolog-type solvers of such as XSB and CIAO [1,32] can also be considered.…”

Section: Pre-evaluation Of Ceciliamentioning

confidence: 99%

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Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Galindo

Moreno²

2021

Lecture Notes in Computer Science

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Contribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.

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Section: Pre-evaluation Of Ceciliamentioning

confidence: 99%

“…Furthermore, new interesting solvers such as s(CASP) [1,47] have potential interest in our problem. It is worth to mention that Prolog-type solvers of such as XSB and CIAO [1,32] can also be considered. -ASP can naturally interact with Inductive Logic Programming (ILP) [12].…”

Section: Pre-evaluation Of Ceciliamentioning

confidence: 99%

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Galindo

Moreno²

2021

Lecture Notes in Computer Science

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“…Finally, grounding has been entirely avoided by some systems that aim at query answering. This has been realized by evaluating programs of normal rules in a top-down Prolog-like manner [87], both on the ground level in GALLIWASP [88] and on the first-order level in S(ASP) [10].…”

Section: Lazy-grounding Techniquesmentioning

confidence: 99%

Boosting Answer Set Optimization with Weighted Comparator Networks

Bomanson¹,

Janhunen²

2020

Theory and Practice of Logic Programming

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AbstractAnswer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.

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“…Some works have been carried out to integrate data-driven solutions into declarative systems with the aim of increasing performance; for instance, such solutions are used for inductively choosing configurations, algorithms selection, and proper coupling of subsystems Maratea et al 2014;Fuscà et al 2017). In some proposals, see for instance SMT (Cok et al 2015;Mellarkod et al 2016; Barrett and Tinelli 2018;Barrett et al 2013) or CASP (Baselice et al 2005;Mellarkod et al 2008;Balduccini and Lierler 2017;Lierler and Susman 2017;Shen and Lierler 2018;Arias et al 2018), the logic solver can select statements that should be checked by external theory/numerical solvers, so that the next steps carried out by the logic solver depend on the answers produced by the external ones. Some works mix statistical analysis and ASP (Gelfond 2010;Nickles and Mileo 2014;Beck et al 2015): here, the aim is to extend logic programs with probabilistic reasoning, either by a direct integration or by embedding external predicates.…”

Section: Related Workmentioning

confidence: 99%

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

Calimeri¹,

Cauteruccio²,

Cinelli³

et al. 2019

Theory and Practice of Logic Programming

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Deductive formalisms have been strongly developed in recent years; among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to artificial intelligence; indeed, in several contexts, other approaches result to be more useful. Typical bioinformatics tasks, for instance classification, are currently carried out mostly by machine learning (ML)-based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians Theory and Practice of Logic Programming 81 and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.

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Constraint Answer Set Programming without Grounding

Cited by 66 publications

References 19 publications

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Boosting Answer Set Optimization with Weighted Comparator Networks

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

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