Applying Incremental Answer Set Solving to Product Configuration
- URL: http://arxiv.org/abs/2207.08599v1
- Date: Mon, 18 Jul 2022 13:38:12 GMT
- Title: Applying Incremental Answer Set Solving to Product Configuration
- Authors: Richard Comploi-Taupe and Giulia Francescutto and Gottfried Schenner
- Abstract summary: We show how to use incremental answer set solving to solve product problems incrementally.
Using complex domain-specific configuration actions makes it possible to tightly control the level of non-determinism.
We show applications of this technique for reasoning about product configuration, like simulating the behavior of a deterministic configuration algorithm.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we apply incremental answer set solving to product
configuration. Incremental answer set solving is a step-wise incremental
approach to Answer Set Programming (ASP). We demonstrate how to use this
technique to solve product configurations problems incrementally. Every step of
the incremental solving process corresponds to a predefined configuration
action. Using complex domain-specific configuration actions makes it possible
to tightly control the level of non-determinism and performance of the solving
process. We show applications of this technique for reasoning about product
configuration, like simulating the behavior of a deterministic configuration
algorithm and describing user actions.
Related papers
- Dominating Set Reconfiguration with Answer Set Programming [0.5242869847419832]
We develop an approach to solve the dominating set reconfiguration problem based on Answer Set Programming (ASP)
Our approach relies on a high-level ASP encoding, and both the grounding and solving tasks are delegated to an ASP-based solver.
arXiv Detail & Related papers (2024-08-14T12:38:12Z) - A learning-based mathematical programming formulation for the automatic
configuration of optimization solvers [0.8075866265341176]
We employ a set of solved instances and configurations in order to learn a performance function of the solver.
We formulate a mixed-integer nonlinear program where the objective/constraints explicitly encode the learnt information.
arXiv Detail & Related papers (2024-01-08T21:10:56Z) - Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Outfit Completion via Conditional Set Transformation [10.075094678260625]
We formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem.
The proposal includes a conditional set transformation architecture with deep neural networks and a compatibility-based regularization method.
Experimental results on real data reveal that the proposed method outperforms existing approaches in terms of accuracy of the outfit completion task, condition satisfaction, and compatibility of completion results.
arXiv Detail & Related papers (2023-11-28T09:30:52Z) - AbCD: A Component-wise Adjustable Framework for Dynamic Optimization
Problems [49.1574468325115]
Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications.
We develop a component-oriented framework for DOPs called Adjustable Components for Dynamic Problems (AbCD)
Our results highlight existing problems in the DOP field that need to be addressed in the future development of algorithms and components.
arXiv Detail & Related papers (2023-10-09T08:11:31Z) - Solving Multi-Configuration Problems: A Performance Analysis with Choco
Solver [49.712444772173775]
In this paper, we exemplify the application of multi-configuration for generating individualized exams.
We also provide a constraint solver performance analysis which helps to gain some insights into corresponding performance issues.
arXiv Detail & Related papers (2023-10-04T08:34:32Z) - Conjunctive Query Based Constraint Solving For Feature Model
Configuration [79.14348940034351]
We show how to apply conjunctive queries to solve constraint satisfaction problems.
This approach allows the application of a wide-spread database technology to solve configuration tasks.
arXiv Detail & Related papers (2023-04-26T10:08:07Z) - Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver.
This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
arXiv Detail & Related papers (2023-01-28T01:50:42Z) - Counterfactual Explanations in Sequential Decision Making Under
Uncertainty [27.763369810430653]
We develop methods to find counterfactual explanations for sequential decision making processes.
In our problem formulation, the counterfactual explanation specifies an alternative sequence of actions differing in at most k actions.
We show that our algorithm finds can provide valuable insights to enhance decision making under uncertainty.
arXiv Detail & Related papers (2021-07-06T17:38:19Z) - An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets [68.8204255655161]
We introduce a divide-and-conquer based diagnosis algorithm (FastDiag) which identifies minimal sets of faulty constraints in an over-constrained problem.
We compare FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis.
arXiv Detail & Related papers (2021-02-17T19:55:42Z) - Constrained Combinatorial Optimization with Reinforcement Learning [0.30938904602244344]
This paper presents a framework to tackle constrained optimization problems using deep Reinforcement Learning (RL)
We extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation.
In that context, the solution is iteratively constructed based on interactions with the environment.
arXiv Detail & Related papers (2020-06-22T03:13:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.