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Annals of Operations Research 1985年第3期5卷 539-556页

作者： Fisher, M.L. Department of Decision Sciences The Wharton School University of Pennsylvania Philadelphia 19104 PA United States

Optimization algorithms or heuristics in which the user interacts significantly either during the solution process or as part of post-optimality analysis are becoming increasingly popular. An important underlying premise of such man/machine systems is that there are some steps in solving a problem in which the computer has an advantage and other steps in which a human has an advantage. This paper first discusses how man/machine systems can be useful in facilitating model specification and revision, coping with aspects of a problem that are difficult to quantify and assisting in the solution process. We then survey successful systems that have been developed in the areas of vehicle scheduling, location problems, job shop scheduling, course scheduling, and planning language-based optimization. © 1985 J.C. Baltzer A.G., Scientific Publishing Company.

关键词： graphics interactive algorithms Programming scheduling

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On the elicitation of indirect preferences in interactive evolutionary multiple objective optimization

On the elicitation of indirect preferences in interactive ev...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Tomczyk, Michal K. Kadzinski, Milosz Poznan Univ Tech Inst Comp Sci Poznan Poland

ISBN: (纸本)9781450371285

We consider essential challenges related to the elicitation of indirect preference information in interactive evolutionary algorithms for multiple objective optimization. The methods in this stream use holistic judgments provided by the Decision Maker (DM) to progressively bias the evolutionary search toward his/her most preferred region in the Pareto front. We enhance such an interactive process using three targeted developments and illustrate their efficiency in the context of a decomposition-based evolutionary framework. Firstly, we present some active learning strategies for selecting solutions from the current population that should be critically compared by the DM. These strategies implement the paradigm of maximizing the potential information gain derived from the DM's answer. Secondly, we discuss the procedures for deciding when the DM should be questioned for preference information. In this way, we refer to a more general problem of distributing the DM's interactions with the method in a way that ensures sufficient evolutionary pressure. Thirdly, we couple the evolutionary schemes with different types of indirect preferences, including pairwise comparisons, preference intensities, best-of-k. judgments, and complete orders of a small subset of solutions. A thorough experimental analysis indicates that the three introduced advancements have a positive impact on the DM-perceived quality of constructed solutions.

关键词： Evolutionary algorithms multiple objective optimization preference learning decomposition-based algorithms interactive algorithms pairwise comparisons

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Bayesian Preference Learning for interactive Multi-objective Optimisation

Bayesian Preference Learning for Interactive Multi-objective...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Taylor, Kendall Ha, Huong Li, Minyi Chan, Jeffrey Li, Xiaodong RMIT Univ Sch Comp Technol Melbourne Vic Australia

ISBN: (纸本)9781450383509

This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted periodically during optimisation of the problem and asked to provide their preference over a series of pairwise comparisons of candidate solutions. After each consultation, the most preferred solution is used as the reference point in an appropriate multiobjective optimisation evolutionary algorithm (MOEA). The rationale for using Bayesian optimisation is to identify the most preferred location in the decision search space with the least number of DM queries, thereby minimising DM cognitive burden and fatigue. This enables non-expert DMs to be involved in the optimisation process and make more informed decisions. We further reduce the number of preference queries required, by progressively redefining the Bayesian search space to reflect the MOEA's decision bounds as it converges toward the Pareto Front. We demonstrate how this approach can locate a reference point close to an unknown preferred location on the Pareto Front, of both benchmark and real-world problems with relatively few pairwise comparisons.

关键词： Multi-objective optimisation preferences evolutionary algorithms interactive algorithms Bayesian optimisation preference learning pairwise comparisons active learning

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Design and Implementation of interactive Learning System for Art Teaching 1

Design and Implementation of Interactive Learning System for...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Fang, ZhengLan Yunnan College of Business Management KunMing China

ISBN: (纸本)9798350316469

This article proposes an interactive algorithm to analyze the teaching and learning objectives. Firstly, use instructional design theory to evaluate learners and divide indicators based on teaching and learning requirements to reduce interference factors in teaching. Then, based on the theory of instructional design, a teaching research plan is formed for the teaching and research of art teaching, and the teaching and research results are comprehensively analyzed. MATLAB simulation shows that under certain evaluation criteria, interactive algorithms have better teaching and research accuracy in art teaching, teaching, and learning time than exploratory teaching mode. © 2023 IEEE.

关键词： art teaching chromatic aberration learning Instructional Design Theory interactive algorithms styling desion

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Propositional computability logic I

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2006年第2期7卷 302-330页

作者： Japaridze, Giorgi Villanova Univ Dept Comp Sci Villanova PA 19085 USA

In the same sense as classical logic is a formal theory of truth, the recently initiated approach called computability logic is a formal theory of computability. It understands ( interactive) computational problems as games played by a machine against the environment, their computability as existence of a machine that always wins the game, logical operators as operations on computational problems, and validity of a logical formula as being a scheme of "always computable" problems. Computability logic has been introduced semantically, and now among its main technical goals is to axiomatize the set of valid formulas or various natural fragments of that set. The present contribution signifies a first step towards this goal. It gives a detailed exposition of a soundness and completeness proof for the rather new type of a deductive propositional system CL1, the logical vocabulary of which contains operators for the so called parallel and choice operations, and the atoms of which represent elementary problems, that is, predicates in the standard sense. This article is self-contained as it explains all relevant concepts. While not technically necessary, familiarity with the foundational paper "Introduction to Computability Logic" [Annals of Pure and Applied Logic 123 (2003), pp. 1-99] would greatly help the reader in understanding the philosophy, underlying motivations, potential and utility of computability logic - the context that determines the value of the present results.

关键词： theory computability logic game semantics interactive algorithms linear logic computational resources

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Propositional computability logic II

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2006年第2期7卷 331-362页

作者： Japaridze, Giorgi Villanova Univ Dept Comp Sci Villanova PA 19085 USA

Computability logic is a formal theory of computational tasks and resources. Its formulas represent interactive computational problems, logical operators stand for operations on computational problems, and validity of a formula is understood as its being a scheme of problems that always have algorithmic solutions. The earlier article "Propositional computability logic I" proved soundness and completeness for the (in a sense) minimal nontrivial fragment CL1 of computability logic. The present article extends that result to the significantly more expressive propositional system CL2. What makes CL2 more expressive than CL1 is the presence of two sorts of atoms in its language: elementary atoms, representing elementary computational problems (i.e. predicates), and general atoms, representing arbitrary computational problems. CL2 conservatively extends CL1, with the latter being nothing but the general-atom-free fragment of the former.

关键词： theory computability logic game semantics interactive algorithms linear logic computational resources

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On some multiobjective optimization problems arising in biology

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2011年第6期88卷 1103-1119页

作者： Soleimani-damaneh, M. Univ Tehran Sch Math Stat & Comp Sci Ctr Excellence Biomath Tehran *** Iran Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran *** Iran

In this paper, we discuss modelling and solving some multiobjective optimization problems arising in biology. A class of comparison problems for string selection in molecular biology and a relocation problem in conservation biology are modelled as multiobjective optimization programmes. Some discussions about applications, solvability and different variants of the obtained models are given, as well. A crucial part of the study is based upon the Pareto optimization which refers to the Pareto solutions of multiobjective optimization problems. For such solution, improvement of some objective function can only be obtained at the expense of the deterioration of at least one other objective function.

关键词： multiobjective optimization (weak/proper) Pareto optimization string selection relocation problem integer programming interactive algorithms transportation

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COMPLETE EFFICIENCY AND THE INITIALIZATION OF algorithms FOR MULTIPLE OBJECTIVE PROGRAMMING

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OPERATIONS RESEARCH LETTERS 1991年第8期10卷 481-487页

作者： BENSON, HP UNIV FLORIDA COLL BUSINESS ADMGAINESVILLEFL 32611 USA

Complete efficiency in multiple objective programming may be more common than has been thought, especially for problems whose feasible regions have no interior. In view of this, we present methods for testing for complete efficiency in multiple objective programming, with special attention to the linear case. These tests do not require the feasible region to have a nonempty interior. For typical linear problems, including those with compact feasible regions, the test not only checks for complete efficiency, but also generates both an initial efficient and an initial extreme point efficient solution for use in vector maximum or interactive algorithms.

关键词： MULTIPLE CRITERIA PROGRAMMING EFFICIENT POINTS VECTOR MAXIMUM algorithms interactive algorithms

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Hierarchical layouts for photo libraries

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IEEE MULTIMEDIA 2006年第4期13卷 62-72页

作者： Kustanowitz, Jack Shneiderman, Ben Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Human Comp Interact Lab College Pk MD 20742 USA

We use an annotated digital photo collection to demonstrate a two-level auto-layout technique consisting of a central primary region with secondary regions surrounding it. Because the object sizes within regions can only be changed in discrete units, we refer to them as quantum content. Our real-time algorithms enable a compelling interactive display as users resize the canvas, or move and resize the primary region.

关键词： data visualisation document image processing photography annotated digital photo collection auto-layout technique interactive displays photo library bi-level hierarchies digital libraries information visualization interactive algorithms photo collections document image processing digital libraries photography information visualization Data visualisation interactive display

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Human-in-the-loop active learning for goal-oriented molecule generation

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JOURNAL OF CHEMINFORMATICS 2024年第1期16卷 1-24页

作者： Nahal, Yasmine Menke, Janosch Martinelli, Julien Heinonen, Markus Kabeshov, Mikhail Janet, Jon Paul Nittinger, Eva Engkvist, Ola Kaski, Samuel Aalto Univ Dept Comp Sci Espoo 02150 Finland AstraZeneca Mol AI Discovery Sci R&D S-43183 Gothenburg Sweden Chalmers Univ Technol Dept Comp Sci & Engn S-41296 Gothenburg Sweden Univ Bordeaux Vaccine Res Inst Inserm Bordeaux Populat Hlth Inria Bordeaux Sud ouest F-33405 Talence France AstraZeneca Med Chem Res & Early Dev Resp & Immunol R&IR&D S-41296 Gothenburg Sweden UNIV MANCHESTER Dept Comp Sci MANCHESTER M13 9PL England

Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules. Scientific contribution. We present an adaptable framework that integrates AL and human expertise to refine property predict

关键词： Goal-oriented molecule generation Human-in-the-loop Machine learning Active learning interactive algorithms

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