Artificial Intelligence最新文献_第9页

Coltrane: A domain-independent system for characterizing and planning in novel situations 一个独立于领域的系统，用于描述和规划新情况

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-04-24 DOI: 10.1016/j.artint.2025.104336

Bryan Loyall , Avi Pfeffer , James Niehaus , Michael Harradon , Paola Rizzo , Alex Gee , Joe Campolongo , Tyler Mayer , John Steigerwald

AI systems operating in open-world environments must be able to adapt to impactful changes in the world, immediately when they occur, and be able to do this across the many types of changes that can occur. We are seeking to create methods to extend traditional AI systems so that they can (1) immediately recognize changes in how the world works that are impactful to task accomplishment; (2) rapidly characterize the nature of the change using the limited observations that are available when the change is first detected; (3) adapt to the change as well as feasible to accomplish the system's tasks given the available observations; and (4) continue to improve the characterization and adaptation as additional observations are available. In this paper, we describe Coltrane, a domain-independent system for characterizing and planning in novel situations that uses only natural domain descriptions to generate its novelty-handling behavior, without any domain-specific anticipation of the novelty. Coltrane's characterization method is based on probabilistic program synthesis of perturbations to programs expressed in a traditional programming language describing domain transition models. Its planning method is based on incorporating novel domain models in an MCTS search algorithm and on automatically adapting the heuristics used. Both a formal external evaluation and our own demonstrations show that Coltrane is capable of accurately characterizing interesting forms of novelty and of adapting its behavior to restore its performance to pre-novelty levels and even beyond.

在开放世界环境中运行的人工智能系统必须能够适应世界中有影响力的变化，当它们发生时立即适应，并且能够跨越可能发生的多种变化。我们正在寻求创造方法来扩展传统的人工智能系统，以便它们能够(1)立即识别对任务完成有影响的世界运作方式的变化；(2)利用第一次探测到变化时可获得的有限观测资料，迅速表征变化的性质；(3)在给定观测值的情况下，适应变化并可行地完成系统的任务；(4)随着获得更多观测数据，继续改进特征和适应。在本文中，我们描述了Coltrane，一个独立于领域的系统，用于描述和规划新颖的情况，它只使用自然的领域描述来生成其新颖性处理行为，没有任何特定于领域的新颖性预期。Coltrane的表征方法是基于对用传统编程语言描述域转移模型的程序表示的扰动的概率程序综合。它的规划方法是基于在MCTS搜索算法中加入新的领域模型并自动适应所使用的启发式算法。正式的外部评估和我们自己的演示都表明，Coltrane能够准确地描述新奇的有趣形式，并调整其行为以恢复其表现到新奇之前的水平，甚至更高。

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引用次数: 0

Learning optimal contracts with small action spaces 学习小行动空间的最优契约

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-04-17 DOI: 10.1016/j.artint.2025.104334

Francesco Bacchiocchi, Matteo Castiglioni, Nicola Gatti, Alberto Marchesi

We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme—called contract—in order to induce an agent to take a costly, unobservable action leading to favorable outcomes. We consider a generalization of the classical (single-round) version of the problem in which the principal interacts with the agent by committing to contracts over multiple rounds. The principal has no information about the agent, and they have to learn an optimal contract by only observing the outcome realized at each round. We focus on settings in which the size of the agent's action space is small. We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant. Our algorithm solves an open problem by Zhu et al. [1]. Moreover, it can also be employed to provide a

\tilde{O} (T^{4 / 5})

regret bound in the related online learning setting in which the principal aims at maximizing their cumulative utility over rounds, considerably improving previously-known regret bounds.

我们研究委托代理问题，其中委托人承诺一个结果依赖的支付方案-称为合同-以诱使代理人采取昂贵的，不可观察的行动，导致有利的结果。我们考虑了经典（单轮）版本问题的一般化，其中委托人通过在多轮中承诺合同与代理人进行交互。委托人没有关于代理人的信息，他们只能通过观察每轮实现的结果来学习最优契约。我们关注的是智能体动作空间较小的设置。我们设计了一种算法，该算法在结果空间大小的数轮多项式中以高概率学习近似最优契约，当动作数量恒定时。我们的算法解决了Zhu等人提出的一个开放问题。此外，它还可以用于在相关的在线学习设置中提供O ~ （T4/5）遗憾界限，其中校长的目标是最大化他们在几轮中的累积效用，大大改善了先前已知的遗憾界限。

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引用次数: 0

The incentive guarantees behind Nash welfare in divisible resources allocation 可分割资源分配中纳什福利背后的激励保障

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-04-16 DOI: 10.1016/j.artint.2025.104335

Xiaohui Bei , Biaoshuai Tao , Jiajun Wu , Mingwei Yang

We study the problem of allocating divisible resources among n agents, hopefully in a fair and efficient manner. With the presence of strategic agents, additional incentive guarantees are also necessary, and the problem of designing fair and efficient mechanisms becomes much less tractable. While there are flourishing positive results against strategic agents for homogeneous divisible items, very few of them are known to hold in cake cutting.

We show that the Maximum Nash Welfare (MNW) mechanism, which provides desirable fairness and efficiency guarantees and achieves an incentive ratio of 2 for homogeneous divisible items, also has an incentive ratio of 2 in cake cutting. Remarkably, this result holds even without the free disposal assumption, which is hard to get rid of in the design of truthful cake cutting mechanisms.

Moreover, we show that, for cake cutting, the Partial Allocation (PA) mechanism proposed by Cole et al. [1], which is truthful and

1 / e

-MNW for homogeneous divisible items, has an incentive ratio between

[e^{1 / e}, e]

and when randomization is allowed, can be turned to be truthful in expectation. Given two alternatives for a trade-off between incentive ratio and Nash welfare provided by the MNW and PA mechanisms, we establish an interpolation between them for both cake cutting and homogeneous divisible items.

Finally, we study the optimal incentive ratio achievable by envy-free cake cutting mechanisms. We first give an envy-free mechanism for two agents with an incentive ratio of 4/3. Then, we show that any envy-free cake cutting mechanism with the connected pieces constraint has an incentive ratio of

Θ (n)

.

我们研究在n个代理之间分配可分割资源的问题，希望以公平和有效的方式进行。由于战略代理人的存在，额外的激励保证也是必要的，设计公平有效机制的问题就变得不那么容易处理了。虽然针对同质可分物品的战略代理人的积极结果非常多，但据了解，其中很少有人会切蛋糕。我们发现，最大纳什福利（MNW）机制提供了理想的公平和效率保证，对同质可分物品的激励比为2，对切蛋糕的激励比也为2。值得注意的是，即使没有自由处置假设，这一结果也成立，而自由处置假设在真实切饼机构的设计中是难以摆脱的。此外，我们表明，对于切蛋糕，Cole等人提出的部分分配（PA）机制，对于同质可分物品是真实的和1/e- mnw，其激励比在[e1/e，e]之间，当允许随机化时，可以在期望上变为真实。给定由MNW和PA机制提供的激励比和纳什福利之间的权衡的两种选择，我们在切蛋糕和同质可分项目之间建立了插值。最后，研究了无嫉妒切蛋糕机制所能达到的最优激励比。我们首先给出了两个主体激励比为4/3的无嫉妒机制。然后，我们证明了任何具有连通片约束的无嫉妒切饼机制的激励比为Θ(n)。

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引用次数: 0

Drawing a map of elections 绘制选举地图

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-03-28 DOI: 10.1016/j.artint.2025.104332

Stanisław Szufa , Niclas Boehmer , Robert Bredereck , Piotr Faliszewski , Rolf Niedermeier , Piotr Skowron , Arkadii Slinko , Nimrod Talmon

Our main contribution is the introduction of the map of elections framework. A map of elections consists of three main elements: (1) a dataset of elections (i.e., collections of ordinal votes over given sets of candidates), (2) a way of measuring similarities between these elections, and (3) a representation of the elections in the 2D Euclidean space as points, so that the more similar two elections are, the closer are their points. In our maps, we mostly focus on datasets of synthetic elections, but we also show an example of a map over real-life ones. To measure similarities, we would have preferred to use, e.g., the isomorphic swap distance, but this is infeasible due to its high computational complexity. Hence, we propose polynomial-time computable positionwise distance and use it instead. Regarding the representations in 2D Euclidean space, we mostly use the Kamada-Kawai algorithm, but we also show two alternatives. We develop the necessary theoretical results to form our maps and argue experimentally that they are accurate and credible. Further, we show how coloring the elections in a map according to various criteria helps in analyzing results of a number of experiments. In particular, we show colorings according to the scores of winning candidates or committees, running times of ILP-based winner determination algorithms, and approximation ratios achieved by particular algorithms.

我们的主要贡献是采用了选举框架图。选举地图由三个主要元素组成：(1)选举数据集（即给定候选人集合的顺序选票集合），(2)测量这些选举之间相似性的方法，以及(3)在二维欧几里得空间中以点表示选举，因此两个选举越相似，它们的点就越接近。在我们的地图中，我们主要关注合成选举的数据集，但我们也展示了一个真实选举地图的例子。为了度量相似性，我们更倾向于使用，例如同构交换距离，但由于其高计算复杂度，这是不可行的。因此，我们提出了多项式时间可计算的位置距离，并用它来代替。对于二维欧几里得空间的表示，我们主要使用Kamada-Kawai算法，但我们也给出了两种替代方法。我们发展必要的理论结果来形成我们的地图，并通过实验证明它们是准确和可信的。此外，我们还展示了如何根据各种标准在地图中为选举上色有助于分析许多实验的结果。特别是，我们根据获胜候选人或委员会的分数，基于ilp的获胜者确定算法的运行时间以及特定算法实现的近似比率显示着色。

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引用次数: 0

The value of real-time automated explanations in stochastic planning 实时自动解释在随机规划中的价值

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-03-08 DOI: 10.1016/j.artint.2025.104323

Claudia V. Goldman , Ronit Bustin , Wenyuan Qi , Zhengyu Xing , Rachel McPhearson-White , Sally Rogers

Recently, we are witnessing an increase in computation power and memory, leading to strong AI algorithms becoming applicable in areas affecting our daily lives. We focus on AI planning solutions for complex, real-life decision-making problems under uncertainty, such as autonomous driving. Human trust in such AI-based systems is essential for their acceptance and market penetration. Moreover, users need to establish appropriate levels of trust to benefit the most from these systems. Previous studies have motivated this work, showing that users can benefit from receiving (handcrafted) information about the reasoning of a stochastic AI planner, for example, controlling automated driving maneuvers. Our solution to automating these hand-crafted notifications with explainable AI algorithms, XAI, includes studying: (1) what explanations can be generated from an AI planning system, applied to a real-world problem, in real-time? What is that content that can be processed from a planner's reasoning that can help users understand and trust the system controlling a behavior they are experiencing? (2) when can this information be displayed? and (3) how shall we display this information to an end user? The value of these computed XAI notifications has been assessed through an online user study with 800 participants, experiencing simulated automated driving scenarios. Our results show that real time XAI notifications decrease significantly subjective misunderstanding of participants compared to those that received only a dynamic HMI display. Also, our XAI solution significantly increases the level of understanding of participants with prior ADAS experience and of participants that lack such experience but have non-negative prior trust to ADAS features. The level of trust significantly increases when XAI was provided to a more restricted set of the participants, including those over 60 years old, with prior ADAS experience and non-negative prior trust attitude to automated features.

最近，我们看到计算能力和内存的增加，导致强大的人工智能算法在影响我们日常生活的领域变得适用。我们专注于人工智能规划解决方案，以解决不确定性下复杂的、现实生活中的决策问题，比如自动驾驶。人类对这种基于人工智能的系统的信任对它们的接受和市场渗透至关重要。此外，用户需要建立适当的信任级别，以便从这些系统中获益最多。之前的研究推动了这项工作，表明用户可以从接收（手工制作的）关于随机人工智能规划器推理的信息中受益，例如，控制自动驾驶机动。我们的解决方案是用可解释的人工智能算法XAI自动化这些手工制作的通知，包括研究：(1)应用于现实世界问题的人工智能规划系统可以实时生成哪些解释？什么内容可以从计划者的推理中处理，帮助用户理解和信任控制他们正在经历的行为的系统？(2)这个信息什么时候可以显示？(3)我们如何将这些信息显示给最终用户？通过对800名参与者的在线用户研究，评估了这些计算出来的XAI通知的价值，这些参与者体验了模拟的自动驾驶场景。我们的研究结果表明，与那些只收到动态HMI显示的参与者相比，实时XAI通知显著减少了参与者的主观误解。此外，我们的XAI解决方案显着提高了具有先前ADAS经验的参与者和缺乏此类经验但对ADAS功能具有非负面先前信任的参与者的理解水平。当XAI提供给更有限的一组参与者时，信任水平显着增加，包括那些60岁以上的参与者，具有先前的ADAS经验和对自动化功能的非负面先前信任态度。

{"title":"The value of real-time automated explanations in stochastic planning","authors":"Claudia V. Goldman , Ronit Bustin , Wenyuan Qi , Zhengyu Xing , Rachel McPhearson-White , Sally Rogers","doi":"10.1016/j.artint.2025.104323","DOIUrl":"10.1016/j.artint.2025.104323","url":null,"abstract":"<div><div>Recently, we are witnessing an increase in computation power and memory, leading to strong AI algorithms becoming applicable in areas affecting our daily lives. We focus on AI planning solutions for complex, real-life decision-making problems under uncertainty, such as autonomous driving. Human trust in such AI-based systems is essential for their acceptance and market penetration. Moreover, users need to establish appropriate levels of trust to benefit the most from these systems. Previous studies have motivated this work, showing that users can benefit from receiving (handcrafted) information about the reasoning of a stochastic AI planner, for example, controlling automated driving maneuvers. Our solution to automating these hand-crafted notifications with explainable AI algorithms, XAI, includes studying: (1) what explanations can be generated from an AI planning system, applied to a real-world problem, in real-time? What is that content that can be processed from a planner's reasoning that can help users understand and trust the system controlling a behavior they are experiencing? (2) when can this information be displayed? and (3) how shall we display this information to an end user? The value of these computed XAI notifications has been assessed through an online user study with 800 participants, experiencing simulated automated driving scenarios. Our results show that real time XAI notifications decrease significantly subjective misunderstanding of participants compared to those that received only a dynamic HMI display. Also, our XAI solution significantly increases the level of understanding of participants with prior ADAS experience and of participants that lack such experience but have non-negative prior trust to ADAS features. The level of trust significantly increases when XAI was provided to a more restricted set of the participants, including those over 60 years old, with prior ADAS experience and non-negative prior trust attitude to automated features.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"343 ","pages":"Article 104323"},"PeriodicalIF":5.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of dimensions on the complexity of computing decision trees 维数对决策树计算复杂度的影响

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-03-07 DOI: 10.1016/j.artint.2025.104322

Stephen Kobourov , Maarten Löffler , Fabrizio Montecchiani , Marcin Pilipczuk , Ignaz Rutter , Raimund Seidel , Manuel Sorge , Jules Wulms

A decision tree recursively splits a feature space

R^{d}

and then assigns class labels based on the resulting partition. Decision trees have been part of the basic machine-learning toolkit for decades. A large body of work considers heuristic algorithms that compute a decision tree from training data, usually aiming to minimize in particular the size of the resulting tree. In contrast, little is known about the complexity of the underlying computational problem of computing a minimum-size tree for the given training data. We study this problem with respect to the number d of dimensions of the feature space

R^{d}

, which contains n training examples. We show that it can be solved in

O (n^{2 d + 1})

time, but under reasonable complexity-theoretic assumptions it is not possible to achieve

f (d) \cdot n^{o (d / \log d)}

running time. The problem is solvable in

{(d R)}^{O (d R)} \cdot n^{1 + o (1)}

time if there are exactly two classes and R is an upper bound on the number of tree leaves labeled with the first class.

决策树递归地分割特征空间Rd，然后根据分割结果分配类标签。几十年来，决策树一直是基本机器学习工具包的一部分。大量的工作考虑了从训练数据计算决策树的启发式算法，通常以最小化结果树的大小为目标。相比之下，对于为给定训练数据计算最小大小树的潜在计算问题的复杂性知之甚少。我们根据特征空间Rd的维数d来研究这个问题，其中包含n个训练样本。我们证明了它可以在O（n2d+1）时间内求解，但在合理的复杂性理论假设下，不可能实现f(d)⋅no（d/log (d)）的运行时间。如果恰好有两类且R是标记为第一类的树叶数目的上界，则问题在（dR）O(dR)⋅n1+ O(1)时间内可解。

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引用次数: 0

ICCMA 2023: 5th International Competition on Computational Models of Argumentation ICCMA 2023：第五届国际辩论计算模型竞赛

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-03-03 DOI: 10.1016/j.artint.2025.104311

Matti Järvisalo , Tuomo Lehtonen , Andreas Niskanen

The study of computational models of argumentation and the development of practical automated approaches to reasoning over the models has developed into a vibrant area of artificial intelligence research in recent years. The series of International Competitions on Computational Models of Argumentation (ICCMA) aims at nurturing research and development of practical reasoning algorithms for models of argumentation. Organized biennially, the ICCMA competitions provide a snapshot of the current state of the art in algorithm implementations for central fundamental reasoning tasks over models of argumentation. The year 2023 marked the 5th instantiation of International Competitions on Computational Models of Argumentation, ICCMA 2023. We provide a comprehensive overview of ICCMA 2023, including details on the various new developments introduced in 2023, overview of the participating solvers, extensive details on the competition benchmarks and results, as well as lessons learned.

近年来，对论证计算模型的研究和对模型进行推理的实用自动化方法的开发已发展成为人工智能研究的一个充满活力的领域。国际论辩计算模型竞赛（ICCMA）旨在促进论辩模型实用推理算法的研究和发展。ICCMA竞赛每两年举办一次，为论证模型的核心基本推理任务的算法实现提供了当前艺术状态的快照。2023年是第五届国际辩论计算模型竞赛（ICCMA 2023）。我们提供ICCMA 2023的全面概述，包括2023年推出的各种新发展的详细信息，参与解决方案的概述，关于比赛基准和结果的广泛细节，以及吸取的教训。

引用次数: 0

Lifted inference beyond first-order logic 超越一阶逻辑的提升推理

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-02-24 DOI: 10.1016/j.artint.2025.104310

Sagar Malhotra , Davide Bizzaro , Luciano Serafini

Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general (#P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers (C²) is domain-liftable. However, many properties of real-world data, like acyclicity in citation networks and connectivity in social networks, cannot be modeled in C², or first order logic in general. In this work, we expand the domain liftability of C² with multiple such properties. We show that any C² sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of counting by splitting. Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.

加权一阶模型计数（WFOMC）是统计关系学习模型中概率推断的基础。众所周知，WFOMC 在一般情况下是难以实现的（#P-complete），因此，能够实现多项式时间 WFOMC 的逻辑片段就引起了人们的极大兴趣。这种片段被称为可域提升片段。最近的研究表明，用计数量词扩展的一阶逻辑的双变量片段（C2）是可域提升的。然而，现实世界数据的许多属性，如引文网络中的非循环性和社交网络中的连通性，无法用 C2 或一般一阶逻辑建模。在这项工作中，我们用多个此类属性扩展了 C2 的域可提升性。我们证明，当任何 C2 句子的一个关系被限制为表示有向无环图、连通图、树（有向树）或森林（有向森林）时，它仍然是可域提升的。我们的所有结果都依赖于一种新颖而通用的拆分计数方法。除了应用于概率推理，我们的结果还为组合结构计数提供了一个通用框架。我们扩展了以前离散数学文献中关于有向无环图、系统发育网络等的大量结果。

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引用次数: 0

(Re)Conceptualizing trustworthy AI: A foundation for change （重新）概念化可信赖的人工智能：变革的基础

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.artint.2025.104309

Christopher D. Wirz , Julie L. Demuth , Ann Bostrom , Mariana G. Cains , Imme Ebert-Uphoff , David John Gagne II , Andrea Schumacher , Amy McGovern , Deianna Madlambayan

Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.

开发人员和学者对开发“可信赖的”人工智能（AI）越来越感兴趣。然而，这一目标在实践中很难实现，特别是考虑到信任和可信赖性是复杂的、多方面的概念，不能完全保证也不能完全构建到人工智能系统中。我们借鉴了跨多个学科和领域的信任相关文献的广度，以综合有关人际信任、自动化信任以及风险与信任的知识。在此基础上回顾我们(重新)概念化诚信实际上是(a)的知觉,这意味着用户评估是否时,人工智能模型输出在多大程度上是值得信赖的,即使它在坚持了AI诚信标准,和(b)上下文相关的,也就是说,用户的感知可信度和使用一个AI模型可以根据他们的情况的细节有所不同(例如,时间压力对决策、高风险的决定)。我们提供了我们的重新概念化，以细微差别人工智能社区如何以与过去的理论研究更一致的方式思考、研究和评估可信度。

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引用次数: 0

Stochastic population update can provably be helpful in multi-objective evolutionary algorithms 随机种群更新在多目标进化算法中具有重要的应用价值

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-02-13 DOI: 10.1016/j.artint.2025.104308

Chao Bian , Yawen Zhou , Miqing Li , Chao Qian

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the best solutions from the current population and newly-generated solutions (irrespective of the selection criteria used such as Pareto dominance, crowdedness and indicators). In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.

进化算法由于其基于群体的搜索特性，在多目标优化问题中得到了广泛而成功的应用。种群更新是多目标ea （moea）的关键组成部分，通常以贪婪的、确定性的方式进行。也就是说，下一代群体是通过从当前群体和新产生的解决方案中选择最佳解决方案而形成的（不考虑使用的选择标准，如帕累托优势，拥挤性和指标）。在本文中，我们分析地证明了随机种群更新对寻找moea是有利的。具体而言，我们证明了SMS-EMOA和NSGA-II这两个已建立的moea在解决OneJumpZeroJump和双目标RealRoyalRoad两个双目标问题时，如果用随机种群更新机制取代其确定性种群更新机制，其预期运行时间可以成倍地减少。实证研究也验证了所提出的种群更新方法的有效性。这项工作试图展示在moea种群更新中引入随机性的好处。它的积极成果，可能具有更广泛的意义，应该鼓励探索在该地区发展新的moea。

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引用次数: 0