Artificial Intelligence最新文献

{"title":"Separate but equal: Equality in belief propagation for single-cycle graphs","authors":"Erel Cohen,&nbsp;Ben Rachmut,&nbsp;Omer Lev,&nbsp;Roie Zivan","doi":"10.1016/j.artint.2024.104243","DOIUrl":"10.1016/j.artint.2024.104243","url":null,"abstract":"<div><div>Belief propagation is a widely used, incomplete optimization algorithm whose main theoretical properties hold only under the assumption that beliefs are not equal. Nevertheless, there is substantial evidence to suggest that equality between beliefs does occur. A published method to overcome belief equality, which is based on the use of unary function-nodes, is commonly assumed to resolve the problem.</div><div>In this study, we focus on min-sum, the version of belief propagation that is used to solve constraint optimization problems. We prove that for the case of a single-cycle graph, belief equality can only be avoided when the algorithm converges to the optimal solution. Under any other circumstances, the unary function method will <em>not</em> prevent equality, indicating that some of the existing results presented in the literature are in need of reassessment. We differentiate between belief equality, which refers to equal beliefs in a single message, and assignment equality, which prevents the coherent assignment of values to the variables, and we provide conditions for both.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104243"},"PeriodicalIF":5.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643213","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}

{"title":"Generative models for grid-based and image-based pathfinding","authors":"Daniil Kirilenko ,&nbsp;Anton Andreychuk ,&nbsp;Aleksandr I. Panov ,&nbsp;Konstantin Yakovlev","doi":"10.1016/j.artint.2024.104238","DOIUrl":"10.1016/j.artint.2024.104238","url":null,"abstract":"<div><div>Pathfinding is a challenging problem which generally asks to find a sequence of valid moves for an agent provided with a representation of the environment, i.e. a map, in which it operates. In this work, we consider pathfinding on binary grids and on image representations of the digital elevation models. In the former case, the transition costs are known, while in latter scenario, they are not. A widespread method to solve the first problem is to utilize a search algorithm that systematically explores the search space to obtain a solution. Ideally, the search should also be complemented with an informative heuristic to focus on the goal and prune the unpromising regions of the search space, thus decreasing the number of search iterations. Unfortunately, the widespread heuristic functions for grid-based pathfinding, such as Manhattan distance or Chebyshev distance, do not take the obstacles into account and in obstacle-rich environments demonstrate inefficient performance. As for pathfinding with image inputs, the heuristic search cannot be applied straightforwardly as the transition costs, i.e. the costs of moving from one pixel to the other, are not known. To tackle both challenges, we suggest utilizing modern deep neural networks to infer the instance-dependent heuristic functions at the pre-processing step and further use them for pathfinding with standard heuristic search algorithms. The principal heuristic function that we suggest learning is the path probability, which indicates how likely the grid cell (pixel) is lying on the shortest path (for binary grids with known transition costs, we also suggest another variant of the heuristic function that can speed up the search). Learning is performed in a supervised fashion (while we have also explored the possibilities of end-to-end learning that includes a planner in the learning pipeline). At the test time, path probability is used as the secondary heuristic for the Focal Search, a specific heuristic search algorithm that provides the theoretical guarantees on the cost bound of the resultant solution. Empirically, we show that the suggested approach significantly outperforms state-of-the-art competitors in a variety of different tasks (including out-of-the distribution instances).</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104238"},"PeriodicalIF":5.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643214","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}

{"title":"Online learning in sequential Bayesian persuasion: Handling unknown priors","authors":"Martino Bernasconi,&nbsp;Matteo Castiglioni,&nbsp;Alberto Marchesi,&nbsp;Nicola Gatti,&nbsp;Francesco Trovò","doi":"10.1016/j.artint.2024.104245","DOIUrl":"10.1016/j.artint.2024.104245","url":null,"abstract":"<div><div>We study a repeated <em>information design</em> problem faced by an informed <em>sender</em> who tries to influence the behavior of a self-interested <em>receiver</em>, through the provision of payoff-relevant information. We consider settings where the receiver repeatedly faces a <em>sequential decision making</em> (SDM) problem. At each round, the sender observes the realizations of random events in the SDM problem, which are only partially observable by the receiver. This begets the challenge of how to incrementally disclose such information to the receiver to <em>persuade</em> them to follow (desirable) action recommendations. We study the case in which the sender does <em>not</em> know random events probabilities, and, thus, they have to gradually learn them while persuading the receiver. We start by providing a non-trivial polytopal approximation of the set of the sender's persuasive information-revelation structures. This is crucial to design efficient learning algorithms. Next, we prove a negative result which also applies to the non-sequential case: <em>no learning algorithm can be persuasive in high probability</em>. Thus, we relax the persuasiveness requirement, studying algorithms that guarantee that the receiver's <em>regret</em> in following recommendations <em>grows sub-linearly</em>. In the <em>full-feedback</em> setting—where the sender observes the realizations of <em>all</em> the possible random events—, we provide an algorithm with <span><math><mover><mrow><mi>O</mi></mrow><mrow><mo>˜</mo></mrow></mover><mo>(</mo><msqrt><mrow><mi>T</mi></mrow></msqrt><mo>)</mo></math></span> regret for both the sender and the receiver. Instead, in the <em>bandit-feedback</em> setting—where the sender only observes the realizations of random events actually occurring in the SDM problem—, we design an algorithm that, given an <span><math><mi>α</mi><mo>∈</mo><mo>[</mo><mn>1</mn><mo>/</mo><mn>2</mn><mo>,</mo><mn>1</mn><mo>]</mo></math></span> as input, guarantees <span><math><mover><mrow><mi>O</mi></mrow><mrow><mo>˜</mo></mrow></mover><mo>(</mo><msup><mrow><mi>T</mi></mrow><mrow><mi>α</mi></mrow></msup><mo>)</mo></math></span> and <span><math><mover><mrow><mi>O</mi></mrow><mrow><mo>˜</mo></mrow></mover><mo>(</mo><msup><mrow><mi>T</mi></mrow><mrow><mi>max</mi><mo>⁡</mo><mo>{</mo><mi>α</mi><mo>,</mo><mn>1</mn><mo>−</mo><mfrac><mrow><mi>α</mi></mrow><mrow><mn>2</mn></mrow></mfrac><mo>}</mo></mrow></msup><mo>)</mo></math></span> regrets, for the sender and the receiver respectively. This result is complemented by a lower bound showing that such a regret trade-off is tight for <span><math><mi>α</mi><mo>∈</mo><mo>[</mo><mn>1</mn><mo>/</mo><mn>2</mn><mo>,</mo><mn>2</mn><mo>/</mo><mn>3</mn><mo>]</mo></math></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104245"},"PeriodicalIF":5.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655204","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}

{"title":"Open-world continual learning: Unifying novelty detection and continual learning","authors":"Gyuhak Kim ,&nbsp;Changnan Xiao ,&nbsp;Tatsuya Konishi ,&nbsp;Zixuan Ke ,&nbsp;Bing Liu","doi":"10.1016/j.artint.2024.104237","DOIUrl":"10.1016/j.artint.2024.104237","url":null,"abstract":"<div><div>As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detect items that they have never seen or learned, and (2) learn the new items incrementally to become more and more knowledgeable and powerful. (1) is called <em>novelty detection</em> or <em>out-of-distribution</em> (OOD) <em>detection</em> and (2) is called <em>class incremental learning</em> (CIL), which is a setting of <em>continual learning</em> (CL). In existing research, OOD detection and CIL are regarded as two completely different problems. This paper first provides a theoretical proof that good OOD detection for each task within the set of learned tasks (called <em>closed-world OOD detection</em>) is <em>necessary</em> for successful CIL. We show this by decomposing CIL into two sub-problems: <em>within-task prediction</em> (WP) and <em>task-id prediction</em> (TP), and proving that TP is correlated with closed-world OOD detection. The <em>key theoretical result</em> is that regardless of whether WP and OOD detection (or TP) are defined explicitly or implicitly by a CIL algorithm, good WP and good closed-world OOD detection are <em>necessary</em> and <em>sufficient</em> conditions for good CIL, which unifies novelty or OOD detection and continual learning (CIL, in particular). We call this traditional CIL the <em>closed-world CIL</em> as it does not detect future OOD data in the open world. The paper then proves that the theory can be generalized or extended to <em>open-world CIL</em>, which is the proposed <em>open-world continual learning</em>, that can perform CIL in the open world and detect future or open-world OOD data. Based on the theoretical results, new CIL methods are also designed, which outperform strong baselines in CIL accuracy and in continual OOD detection by a large margin.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104237"},"PeriodicalIF":5.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chimeric U-Net – Modifying the standard U-Net towards explainability","authors":"Kenrick Schulze ,&nbsp;Felix Peppert ,&nbsp;Christof Schütte ,&nbsp;Vikram Sunkara","doi":"10.1016/j.artint.2024.104240","DOIUrl":"10.1016/j.artint.2024.104240","url":null,"abstract":"<div><div>Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net, encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under:</div><div><span><span>https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch</span><svg><path></path></svg></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104240"},"PeriodicalIF":5.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TeachText: CrossModal text-video retrieval through generalized distillation","authors":"Ioana Croitoru ,&nbsp;Simion-Vlad Bogolin ,&nbsp;Marius Leordeanu ,&nbsp;Hailin Jin ,&nbsp;Andrew Zisserman ,&nbsp;Yang Liu ,&nbsp;Samuel Albanie","doi":"10.1016/j.artint.2024.104235","DOIUrl":"10.1016/j.artint.2024.104235","url":null,"abstract":"<div><div>In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we investigate the design of such algorithms and propose a novel generalized distillation method, <span>TeachText</span>, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the retrieval model. <span>TeachText</span> yields significant gains on a number of video retrieval benchmarks without incurring additional computational overhead during inference and was used to produce the winning entry in the Condensed Movie Challenge at ICCV 2021. We show how <span>TeachText</span> can be extended to include multiple video modalities, reducing computational cost at inference without compromising performance. Finally, we demonstrate the application of our method to the task of removing noisy descriptions from the training partitions of retrieval datasets to improve performance. Code and data can be found at <span><span>https://www.robots.ox.ac.uk/~vgg/research/teachtext/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104235"},"PeriodicalIF":5.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655203","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}

{"title":"Integrating multi-armed bandit with local search for MaxSAT","authors":"Jiongzhi Zheng ,&nbsp;Kun He ,&nbsp;Jianrong Zhou ,&nbsp;Yan Jin ,&nbsp;Chu-Min Li ,&nbsp;Felip Manyà","doi":"10.1016/j.artint.2024.104242","DOIUrl":"10.1016/j.artint.2024.104242","url":null,"abstract":"<div><div>Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we introduce a new local search algorithm for these problems, named BandHS. It applies two multi-armed bandit (MAB) models to guide the search directions when escaping local optima. One MAB model is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, while the other MAB model is combined with all the literals in hard clauses to help the algorithm select suitable literals to satisfy the hard clauses. These two models enhance the algorithm's search ability in both feasible and infeasible solution spaces. BandHS also incorporates a novel initialization method that prioritizes both unit and binary clauses when generating the initial solutions. Moreover, we apply our MAB approach to the state-of-the-art local search algorithm NuWLS and to the local search component of the incomplete solver NuWLS-c-2023. The extensive experiments conducted demonstrate the excellent performance and generalization capability of the proposed method. Additionally, we provide analyses on the type of problems where our MAB method works well or not, aiming to offer insights and suggestions for its application. Encouragingly, our MAB method has been successfully applied in core local search components in the winner of the WPMS complete track of MaxSAT Evaluation 2023, as well as the runners-up of the incomplete track of MaxSAT Evaluations 2022 and 2023.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104242"},"PeriodicalIF":5.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577758","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}

{"title":"Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments","authors":"Liesbeth Allein,&nbsp;Maria Mihaela Truşcǎ,&nbsp;Marie-Francine Moens","doi":"10.1016/j.artint.2024.104234","DOIUrl":"10.1016/j.artint.2024.104234","url":null,"abstract":"<div><div>The social and implicit nature of human communication ramifies readers' understandings of written sentences. Single gold-standard interpretations rarely exist, challenging conventional assumptions in natural language processing. This work introduces the interpretation modeling (IM) task which involves modeling several interpretations of a sentence's underlying semantics to unearth layers of implicit meaning. To obtain these, IM is guided by multiple annotations of social relation and common ground - in this work approximated by reader attitudes towards the author and their understanding of moral judgments subtly embedded in the sentence. We propose a number of modeling strategies that rely on one-to-one and one-to-many generation methods that take inspiration from the philosophical study of interpretation. A first-of-its-kind IM dataset is curated to support experiments and analyses. The modeling results, coupled with scrutiny of the dataset, underline the challenges of IM as conflicting and complex interpretations are socially plausible. This interplay of diverse readings is affirmed by automated and human evaluations on the generated interpretations. Finally, toxicity analyses in the generated interpretations demonstrate the importance of IM for refining filters of content and assisting content moderators in safeguarding the safety in online discourse.<span><span>¹</span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104234"},"PeriodicalIF":5.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The complexity of optimizing atomic congestion","authors":"Cornelius Brand ,&nbsp;Robert Ganian ,&nbsp;Subrahmanyam Kalyanasundaram ,&nbsp;Fionn Mc Inerney","doi":"10.1016/j.artint.2024.104241","DOIUrl":"10.1016/j.artint.2024.104241","url":null,"abstract":"<div><div>Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a <em>system-optimal</em> set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104241"},"PeriodicalIF":5.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven transcriptome profile-guided hit molecule generation","authors":"Chen Li,&nbsp;Yoshihiro Yamanishi","doi":"10.1016/j.artint.2024.104239","DOIUrl":"10.1016/j.artint.2024.104239","url":null,"abstract":"<div><div><span><math><mi>D</mi><mi>e</mi><mspace></mspace><mi>n</mi><mi>o</mi><mi>v</mi><mi>o</mi></math></span> generation of bioactive and drug-like hit molecules is a pivotal goal in computer-aided drug discovery. While artificial intelligence (AI) has proven adept at generating molecules with desired chemical properties, previous studies often overlook the influence of disease-specific cellular environments. This study introduces GxVAEs, a novel AI-driven deep generative model designed to produce hit molecules from transcriptome profiles using dual variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from transcriptome profiles to guide the second VAE, MolVAE, in generating hit molecules. GxVAEs aim to bridge the gap between molecule generation and the biological context of disease, producing molecules that are biologically relevant within specific cellular environments or pathological conditions. Experimental results and case studies focused on hit molecule generation demonstrate that GxVAEs surpass current state-of-the-art methods, in terms of reproducibility of known ligands. This approach is expected to effectively find potential molecular structures with bioactivities across diverse disease contexts.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104239"},"PeriodicalIF":5.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}