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Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems

作者： Bram M. Renting Holger H. Hoos Catholijn M. Jonker Leiden University & Delft University of Technology Leiden Netherlands RWTH Aachen & Leiden University Aachen Germany Delft University of Technology & Leiden University Delft Netherlands

ISBN: (纸本)9781450392136

Bargaining can be used to resolve mixed-motive games in multi-agent systems. Although there is an abundance of negotiation strategies implemented in automated negotiating agents, most agents are based on single fixed strategies, while it is acknowledged that there is no single best-performing strategy for all negotiation *** this paper, we focus on bargaining settings where opponents are repeatedly encountered, but the bargaining problems change. We introduce a novel method that automatically creates and deploys a portfolio of complementary negotiation strategies using a training set and optimise pay-off in never-before-seen bargaining settings through per-setting strategy selection. Our method relies on the following contributions. We introduce a feature representation that captures characteristics for both the opponent and the bargaining problem. We model the behaviour of an opponent during a negotiation based on its actions, which is indicative of its negotiation strategy, in order to be more effective in future *** combination of feature-based methods generalises to new negotiation settings, as in practice, over time, it selects effective counter strategies in future encounters. Our approach is tested in an ANAC-like tournament, and we show that we are capable of winning such a tournament with a (5.6%) increase in pay-off compared to the runner-up agent.

关键词： mixed-motive games bargaining algorithm selection algorithm configuration

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Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems

Predicting CMA-ES Operators as Inductive Biases for Shape Op...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Friess, Stephen Tino, Peter Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham W Midlands England Honda Res Inst Europe Offenbach Germany Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9781728190488

Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying the similarity of different optimization problems and the nature of complementary experiences formed on them. A more rigorously grounded approach can be found alternatively in metalearning. This notion neglects any hurdles on characterizing problem similarity in favor of focusing instead on methodology to form domain-dependent inductive biases and mechanisms to arbitrate between them. In principle, we proposed within our previous research methods for constructing inductive biases and predict these from procedural optimization data. However, while we obtained effective methodology, it does not allow the joint construction of predictive components and biases in a cohesive manner. We therefore show in our following study, that improved configurations can be derived for the CMA-ES algorithm which can serve as inductive biases, and that predictors can be trained to recall them. Particularly noteworthy, this scenario allows the construction of predictive component and bias iteratively in a joint manner. We demonstrate the efficacy of this approach in a shape optimization scenario, in which the inductive bias is predicted through an operator configuration in a problem-specific manner during run-time.

关键词： Inductive Biases algorithm configuration Operator Representation Design Optimization

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Machine Learning Approach to Efficient Hyperparameters Search for Video Streams Semantic Analysis algorithms

Machine Learning Approach to Efficient Hyperparameters Searc...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Krymov, Roman A. Khamukhin, Anatoly, V RnD Ctr ELVEES Moscow Russia

ISBN: (纸本)9781665404761

Computer vision algorithms have complex set of parameters that significantly influence resulting quality. On the one hand, such a big amount of parameters that can be varied results in algorithm ability to fit for particular usage circumstances. On the other hand, fine-tuning of even several parameters may require a lot of human resources unless this task is automated. An approach to such automatization is provided considering a machine learning problem with several peculiarities. First, some parameters are restricted to have integer values. Second, multiple metrics describing algorithm quality are restricted to have integer values as well. Third, there are no analytical formulae describing dependences between parameters and metrics values. Mentioned features make gradient-descent-like methods widely used for neural networks training inapplicable for solving that optimization problem. As result, automatically tuned parameters provides better quality than manually selected ones.

关键词： machine learning hyperparameter optimization video analysis algorithm algorithm configuration

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Reuse-centric k-means configuration

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INFORMATION SYSTEMS 2021年 100卷 101787-101787页

作者： Zhang, Lijun Guan, Hui Ding, Yufei Shen, Xipeng Krim, Hamid Univ Massachusetts Amherst MA 01002 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA North Carolina State Univ Raleigh NC 27606 USA

K-means configuration is to find a configuration of k-means (e.g., the number of clusters, feature sets) that maximize some objectives. It is a time-consuming process due to the iterative nature of k-means. This paper proposes reuse-centric k-means configuration to accelerate k-means configuration. It is based on the observation that the explorations of different configurations share lots of common or similar computations. Effectively reusing the computations from prior trials of different configurations could largely shorten the configuration time. To materialize the idea, the paper presents a set of novel techniques, including reuse-based filtering, center reuse, and a two-phase design to capitalize on the reuse opportunities on three levels: validation, number of clusters, and feature sets. Experiments on k-means-based data classification tasks show that reuse-centric k-means configuration can speed up a heuristic search-based configuration process by a factor of 5.8, and a uniform search-based attainment of classification error surfaces by a factor of 9.1. The paper meanwhile provides some important insights on how to effectively apply the acceleration techniques to tap into a full potential. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： K-means algorithm configuration Computation reuse

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Per-Instance configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis

Per-Instance Configuration of the Modularized CMA-ES by Mean...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Prager, Raphael Patrick Trautmann, Heike Wang, Hao Baeck, Thomas H. W. Kerschke, Pascal Univ Munster Stat & Optimizat Munster Germany Sorbonne Univ LIP6 Paris France Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

ISBN: (纸本)9781728125473

In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4 608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the afrorementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.

关键词： Single-Objective Continuous Optimization algorithm configuration Exploratory Landscape Analysis Modularized CMA-ES Classifier Chains

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Configuring the Perturbation Operations of an Iterated Local Search algorithm for Cross-domain Search: A Probabilistic Learning Approach

Configuring the Perturbation Operations of an Iterated Local...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Adubi, Stephen A. Oladipupo, Olufunke O. Olugbara, Oludayo O. Covenant Univ Dept Comp & Informat Sci Ota Nigeria Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Durban South Africa

ISBN: (纸本)9781728183923

Hyper-heuristics are general-purpose heuristic search methodologies for solving combinatorial optimization problems (COPs). Research findings have revealed that hyper-heuristics still suffer generalization issues as different strategies vary in performance from an instance of a COP to another. In this paper, an approach based on Iterated Local Search (ILS) is proposed to raise the level of generality of hyper-heuristics on the problem domains of the HyFlex framework. The proposed approach utilizes a probabilistic learning technique to automatically configure the behavior of the ILS algorithm during the perturbation stage of the optimization process. In the proposed method, the mutation and ruin-recreate heuristics are treated as distinct entities and the learning layer automatically determines the level of utilization of these heuristic categories depending on the problem domain being solved. The concept of double shaking is also presented where a solution can be perturbed twice before the intensification phase. Experimental results reveal the level of generality achieved by the proposed method as it recorded a minimum formula one score of 30.0 on each tested problem domain. Direct comparison with a state-of-the-art ILS-based approach also establishes the significance of the learning layer added to the perturbation stage of the ILS metaheuristic. Finally, analysis of the perturbation behavior of the hyper-heuristic leads to an interesting conclusion concerning the type of low-level heuristics that are highly beneficial and non-beneficial to a given problem domain.

关键词： iterated local search selection hyper-heuristics probabilistic learning algorithm configuration

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Adaptive Differential Evolution based on Exploration and Exploitation Control

Adaptive Differential Evolution based on Exploration and Exp...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Bai, Hao Huang, Changwu Yao, Xin INSA Rouen Normandy Lab Mech Normandy LMN F-76000 Rouen France Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728183923

Search operator design and parameter tuning are essential parts of algorithm design. However, they often involve trial-and-error and are very time-consuming. A new differential evolution (DE) algorithm with adaptive exploration and exploitation control (AEEC-DE) is proposed in this work to tackle this challenge. The proposed method improves the performance of DE by automatically selecting trial vector generation strategies (both mutation and crossover operators) and dynamically generating the associated control parameter values. A probability-based exploration and exploitation measurement is introduced to estimate whether the state of each newly generated individual is in exploration or exploitation. The state of historical individuals is used to assess the exploration and exploitation capabilities of different generation strategies and parameter values. Then, the strategies and parameters of DE are adapted following the common belief that evolutionary algorithms (EAs) should start with exploration and then gradually change into exploitation. The performance of AEEC-DE is evaluated through experimental studies on a set of test problems and compared with several state-of-the-art adaptive DE variants.

关键词： algorithm configuration Differential Evolution Parameter Control Exploration and Exploitation

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SMAC3: a versatile Bayesian optimization package for hyperparameter optimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 2475-2483页

作者： Marius Lindauer Katharina Eggensperger Matthias Feurer André Biedenkapp Difan Deng Carolin Benjamins Tim Ruhkopf René Sass Frank Hutter Leibniz University Hannover University of Freiburg University of Freiburg and Bosch Center for Artificial Intelligence

algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license.

关键词： Bayesian optimization hyperparameter optimization multi-fidelity optimization automated machine learning algorithm configuration

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ACVIZ: A tool for the visual analysis of the configuration of algorithms with irace

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OPERATIONS RESEARCH PERSPECTIVES 2021年 8卷

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Perez Caceres, Leslie Santa Catarina State Univ Florianopolis SC Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Malaga Malaga Spain Pontificia Univ Catolica Valparaiso Valparaiso Chile

This paper introduces acviz, a tool that helps to analyze the automatic configuration of algorithms with irace. It provides a visual representation of the configuration process, allowing users to extract useful information, e.g. how the configurations evolve over time. When test data is available, acviz also shows the performance of each configuration on the test instances. Using this visualization, users can analyze and compare the quality of the resulting configurations and observe the performance differences on training and test instances.

关键词： algorithm configuration Parameter tuning irace

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Population Step Forward Encoding algorithm: Improving the signal encoding accuracy and efficiency of spike encoding algorithms

Population Step Forward Encoding Algorithm: Improving the si...

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作者： de Gelder, Luuk Delft University of Technology

学位级别：硕士

Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited bandwidth for the spikes that get generated through the encoding algorithm. In this thesis, we propose population Step Forward Encoding algorithm. This algorithm takes the signal encoding accuracy of Step Forward encoding algorithm and makes it into a population, generating multiple spike trains. This allows a higher threshold to encode a large part of the signal, increasing the efficiency. We show that population Step Forward Encoding algorithm doesn't just work good for the signal encoding accuracy, but also for the classification accuracy. Moreover, population Step Forward Encoding algorithm does not only have a high efficiency with a low spike count, it can also achieve higher efficiency with higher spike count. Thus, population Step Forward can make most use of a limited bandwidth of spikes.

关键词： Spiking Neural Networks(SNNs)) Spiking Neural Networks spiking neural networks Encoding algorithms Temporal Encoding algorithms temporal data Encoding algorithm algorithm configuration Population Step Forward Encoding

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