Constraint acquisition

Bessière, Christian; Koriche, Frédéric; Lazaar, Nadjib; O’Sullivan, Barry

doi:10.1016/j.artint.2015.08.001

Cited by 73 publications

(94 citation statements)

References 28 publications

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“…Synthesis of argumentation frameworks. Broadly speaking, generation of structures from given information, also sometimes called learning, acquisition, or synthesis, has initiated several research directions within AI [20,24,46,100]. Focusing on the construction of argumentative structures [89,96,97], we recall the synthesis operation from [89], where our structural constraints can be applied.…”

Section: Constraints For Further Operationsmentioning

confidence: 99%

Structural constraints for dynamic operators in abstract argumentation

Wallner

2020

AAC

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Many recent studies of dynamics in formal argumentation within AI focus on the well-known formalism of Dung's argumentation frameworks (AFs). Despite the usefulness of AFs in many areas of argumentation, their abstract notion of arguments creates a barrier for operators that modify a given AF, e.g., in the case that dependencies between arguments have been abstracted away that are important for subsequent modifications. In this paper we aim to support development of dynamic operators on formal models in abstract argumentation by providing constraints imposed on the modification of the structure that can be used to incorporate information that has been abstracted away. Towards a broad reach, we base our results on the general formalism of abstract dialectical frameworks (ADFs) in abstract argumentation. To show applicability, we present two cases studies that adapt an existing extension enforcement operator that modifies AFs: in the first case study, we show how to utilize constraints in order to obtain an enforcement operator on ADFs that is allowed to only add support relations between arguments, and in the second case study we show how an enforcement operator on AFs can be defined that respects dependencies between arguments. We show feasibility of our approach by studying the complexity of the proposed structural constraints and the operators arising from the case studies, and by an experimental evaluation of an answer set programming (ASP) implementation of the enforcement operator based on supports.

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Section: Constraints For Further Operationsmentioning

confidence: 99%

Structural constraints for dynamic operators in abstract argumentation

Wallner

2020

AAC

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“…In terms of modelling, constraint acquisition [Bessiere et al, 2017] uses machine learning techniques to learn structural constraints from data, while other works are concerned with finding the most likely parameters of given hard constraints [Picard-Cantin et al, 2017].…”

Section: Other Related Approachesmentioning

confidence: 99%

Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

Demirović

Stuckey

Bailey

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

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“…The SUMO process of compiling a set of constraints with user interactions has also been approached from a querydriven perspective. CONACQ [38] poses specific queries to the user, using them as an oracle to deduce the constraint network. This contrasts to SUMO in that the queries are "membership queries" (e.g., "do x and y belong together?")…”

Section: Constraint Acquisitionmentioning

confidence: 99%

Effectively Incorporating Expert Knowledge in Automated Software Remodularisation

Hall

Walkinshaw

McMinn

2018

IIEEE Trans. Software Eng.

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Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowledge, leading to results that can be nonsensical to developers. Nevertheless, suppling such knowledge is a potentially burdensome task to perform manually. A lot information may need to be specified, particularly for large systems. Addressing these concerns, we propose the SUMO (SUpervised reMOdularisation) approach. SUMO is a technique that aims to leverage a small subset of domain knowledge about a system to produce a remodularisation that will be acceptable to a developer. With SUMO, developers refine a modularisation by iteratively supplying corrections. These corrections constrain the type of remodularisation eventually required, enabling SUMO to dramatically reduce the solution space. This in turn reduces the amount of feedback the developer needs to supply. We perform a comprehensive systematic evaluation using 100 real world subject systems. Our results show that SUMO guarantees convergence on a target remodularisation with a tractable amount of user interaction.

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Constraint acquisition

Cited by 73 publications

References 28 publications

Structural constraints for dynamic operators in abstract argumentation

Structural constraints for dynamic operators in abstract argumentation

Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

Effectively Incorporating Expert Knowledge in Automated Software Remodularisation

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