Efficient Multiple Constraint Acquisition

• URL: http://arxiv.org/abs/2109.05920v1
• Date: Mon, 13 Sep 2021 12:42:16 GMT
• Title: Efficient Multiple Constraint Acquisition
• Authors: Dimosthenis C. Tsouros and Kostas Stergiou
• Abstract summary: Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks. We present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly. We then turn our attention to query generation which is a significant but rather overlooked part of the acquisition process.
• Score: 1.3706331473063877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. In this paper we propose algorithmic and heuristic methods to deal with both these issues. We first describe an algorithm, called MQuAcq, that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. A detailed theoretical analysis of the proposed algorithm is also presented. %We also present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly. Then we turn our attention to query generation which is a significant but rather overlooked part of the acquisition process. We describe %in detail how query generation in a typical constraint acquisition system operates, and we propose heuristics for improving its efficiency. Experiments from various domains demonstrate that our resulting algorithm that integrates all the new techniques does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, in average query generation time as well as in total run time, and also largely alleviates the premature convergence problem.

Related papers

• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Query-Efficient Correlation Clustering with Noisy Oracle [17.11782578276788]
  We introduce two novel formulations of online learning problems rooted in the paradigm of Pure Exploration in Combinatorial Multi-Armed Bandits (PE-CMAB) We design algorithms that combine a sampling strategy with a classic approximation algorithm for correlation and study their theoretical guarantees. Our results are the first examples of clustering-time algorithms that work for the case of PE-CMAB in which the underlying offline optimization problem is NP-hard.
  arXiv  Detail & Related papers  (2024-02-02T13:31:24Z)
• Learning to Learn in Interactive Constraint Acquisition [7.741303298648302]
  In Constraint Acquisition (CA), the goal is to assist the user by automatically learning the model. In (inter)active CA, this is done by interactively posting queries to the user. We propose to use probabilistic classification models to guide interactive CA to generate more promising queries.
  arXiv  Detail & Related papers  (2023-12-17T19:12:33Z)
• Guided Bottom-Up Interactive Constraint Acquisition [10.552990258277434]
  Constraint Acquisition (CA) systems can be used to assist in the modeling of constraint satisfaction problems. Current interactive CA algorithms suffer from at least two major bottlenecks. We present two novel methods that improve the efficiency of CA.
  arXiv  Detail & Related papers  (2023-07-12T12:25:37Z)
• Lifting Symmetry Breaking Constraints with Inductive Logic Programming [2.036811219647753]
  We introduce a new model-oriented approach for Answer Set Programming that lifts the Symmetry Breaking Constraints into a set of interpretable first-order constraints. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs.
  arXiv  Detail & Related papers  (2021-12-22T11:27:48Z)
• Global Optimization of Objective Functions Represented by ReLU Networks [77.55969359556032]
  Neural networks can learn complex, non- adversarial functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversarial examples), but these cannot guarantee the absence of failures. We propose an approach that integrates the optimization process into the verification procedure, achieving better performance than the naive approach.
  arXiv  Detail & Related papers  (2020-10-07T08:19:48Z)
• An Efficient Algorithm for Cooperative Semi-Bandits [0.0]
  We introduce Coop-FTPL, a cooperative version of the well-known Follow The Perturbed Leader algorithm. We show that the expected regret of our algorithm after T time steps is of order QT log(k)(k$alpha$ 1 /Q + m), where Q is the total activation probability mass.
  arXiv  Detail & Related papers  (2020-10-05T07:08:26Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)
• Optimal Clustering from Noisy Binary Feedback [75.17453757892152]
  We study the problem of clustering a set of items from binary user feedback. We devise an algorithm with a minimal cluster recovery error rate. For adaptive selection, we develop an algorithm inspired by the derivation of the information-theoretical error lower bounds.
  arXiv  Detail & Related papers  (2019-10-14T09:18:26Z)

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.