Artificial Intelligence最新文献

{"title":"Delegated online search","authors":"Pirmin Braun ,&nbsp;Niklas Hahn ,&nbsp;Martin Hoefer ,&nbsp;Conrad Schecker","doi":"10.1016/j.artint.2024.104171","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104171","url":null,"abstract":"<div><p>In a delegation problem, a <em>principal</em> <span><math><mi>P</mi></math></span> with commitment power tries to pick one out of <em>n</em> options. Each option is drawn independently from a known distribution. Instead of inspecting the options herself, <span><math><mi>P</mi></math></span> delegates the information acquisition to a rational and self-interested <em>agent</em> <span><math><mi>A</mi></math></span>. After inspection, <span><math><mi>A</mi></math></span> proposes one of the options, and <span><math><mi>P</mi></math></span> can accept or reject.</p><p>Delegation is a classic setting in economic information design with many prominent applications, but the computational problems are only poorly understood. In this paper, we study a natural <em>online</em> variant of delegation, in which the agent searches through the options in an online fashion. For each option, he has to irrevocably decide if he wants to propose the current option or discard it, before seeing information on the next option(s). How can we design algorithms for <span><math><mi>P</mi></math></span> that approximate the utility of her best option in hindsight?</p><p>We show that in general <span><math><mi>P</mi></math></span> can obtain a <span><math><mi>Θ</mi><mo>(</mo><mn>1</mn><mo>/</mo><mi>n</mi><mo>)</mo></math></span>-approximation and extend this result to ratios of <span><math><mi>Θ</mi><mo>(</mo><mi>k</mi><mo>/</mo><mi>n</mi><mo>)</mo></math></span> in case (1) <span><math><mi>A</mi></math></span> has a lookahead of <em>k</em> rounds, or (2) <span><math><mi>A</mi></math></span> can propose up to <em>k</em> different options. We provide fine-grained bounds independent of <em>n</em> based on three parameters. If the ratio of maximum and minimum utility for <span><math><mi>A</mi></math></span> is bounded by a factor <em>α</em>, we obtain an <span><math><mi>Ω</mi><mo>(</mo><mi>log</mi><mo>⁡</mo><mi>log</mi><mo>⁡</mo><mi>α</mi><mo>/</mo><mi>log</mi><mo>⁡</mo><mi>α</mi><mo>)</mo></math></span>-approximation algorithm, and we show that this is best possible. Additionally, if <span><math><mi>P</mi></math></span> cannot distinguish options with the same value for herself, we show that ratios polynomial in <span><math><mn>1</mn><mo>/</mo><mi>α</mi></math></span> cannot be avoided. If there are at most <em>β</em> different utility values for <span><math><mi>A</mi></math></span>, we show a <span><math><mi>Θ</mi><mo>(</mo><mn>1</mn><mo>/</mo><mi>β</mi><mo>)</mo></math></span>-approximation. If the utilities of <span><math><mi>P</mi></math></span> and <span><math><mi>A</mi></math></span> for each option are related by a factor <em>γ</em>, we obtain an <span><math><mi>Ω</mi><mo>(</mo><mn>1</mn><mo>/</mo><mi>log</mi><mo>⁡</mo><mi>γ</mi><mo>)</mo></math></span>-approximation, where <span><math><mi>O</mi><mo>(</mo><mi>log</mi><mo>⁡</mo><mi>log</mi><mo>⁡</mo><mi>γ</mi><mo>/</mo><mi>log</mi><mo>⁡</mo><mi>γ</mi><mo>)</mo></math></span> is best possible.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104171"},"PeriodicalIF":5.1,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001073/pdfft?md5=2d7a00808c733af9db17db5a21fc73fe&pid=1-s2.0-S0004370224001073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487482","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":"An extensive study of security games with strategic informants","authors":"Weiran Shen ,&nbsp;Minbiao Han ,&nbsp;Weizhe Chen ,&nbsp;Taoan Huang ,&nbsp;Rohit Singh ,&nbsp;Haifeng Xu ,&nbsp;Fei Fang","doi":"10.1016/j.artint.2024.104162","DOIUrl":"10.1016/j.artint.2024.104162","url":null,"abstract":"<div><p>Over the past years, game-theoretic modeling for security and public safety issues (also known as <em>security games</em>) have attracted intensive research attention and have been successfully deployed in many real-world applications for fighting, e.g., illegal poaching, fishing and urban crimes. However, few existing works consider how information from local communities would affect the structure of these games. In this paper, we systematically investigate how a new type of players – <em>strategic informants</em> who are from local communities and may observe and report upcoming attacks – affects the classic defender-attacker security interactions. Characterized by a private type, each informant has a utility structure that drives their strategic behaviors.</p><p>For situations with a single informant, we capture the problem as a 3-player extensive-form game and develop a novel solution concept, Strong Stackelberg-perfect Bayesian equilibrium, for the game. To find an optimal defender strategy, we establish that though the informant can have infinitely many types in general, there always exists an optimal defense plan using only a linear number of patrol strategies; this succinct characterization then enables us to efficiently solve the game via linear programming. For situations with multiple informants, we show that there is also an optimal defense plan with only a linear number of patrol strategies that admits a simple structure based on plurality voting among multiple informants.</p><p>Finally, we conduct extensive experiments to study the effect of the strategic informants and demonstrate the efficiency of our algorithm. Our experiments show that the existence of such informants significantly increases the defender's utility. Even though the informants exhibit strategic behaviors, the information they supply holds great value as defensive resources. Compared to existing works, our study leads to a deeper understanding on the role of informants in such defender-attacker interactions.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104162"},"PeriodicalIF":14.4,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141410791","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":"A domain-independent agent architecture for adaptive operation in evolving open worlds","authors":"Shiwali Mohan ,&nbsp;Wiktor Piotrowski ,&nbsp;Roni Stern ,&nbsp;Sachin Grover ,&nbsp;Sookyung Kim ,&nbsp;Jacob Le ,&nbsp;Yoni Sher ,&nbsp;Johan de Kleer","doi":"10.1016/j.artint.2024.104161","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104161","url":null,"abstract":"<div><p> Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement <em>novelty-aware</em> agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem<span>¹</span>), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104161"},"PeriodicalIF":14.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141303042","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":"Functional Relation Field: A Model-Agnostic Framework for Multivariate Time Series Forecasting","authors":"Ting Li ,&nbsp;Bing Yu ,&nbsp;Jianguo Li ,&nbsp;Zhanxing Zhu","doi":"10.1016/j.artint.2024.104158","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104158","url":null,"abstract":"<div><p>In multivariate time series forecasting, the most popular strategy for modeling the relationship between multiple time series is the construction of graph, where each time series is represented as a node and related nodes are connected by edges. However, the relationship between multiple time series is typically complicated, e.g. the sum of outflows from upstream nodes may be equal to the inflows of downstream nodes. Such relations widely exist in many real-world scenarios for multivariate time series forecasting, yet are far from well studied. In these cases, graph might be insufficient for modeling the complex dependency between nodes. To this end, we explore a new framework to model the inter-node relationship in a more precise way based our proposed inductive bias, <em>Functional Relation Field</em>, where a group of functions parameterized by neural networks are learned to characterize the dependency between multiple time series. Essentially, these learned functions then form a “field”, i.e. a particular set of constraints, to regularize the training loss of the backbone prediction network and enforce the inference process to satisfy these constraints. Since our framework introduces the relationship bias in a data-driven manner, it is flexible and model-agnostic such that it can be applied to any existing multivariate time series prediction networks for boosting performance. The experiment is conducted on one toy dataset to show our approach can well recover the true constraint relationship between nodes. And various real-world datasets are also considered with different backbone prediction networks. Results show that the prediction error can be reduced remarkably with the aid of the proposed framework.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104158"},"PeriodicalIF":14.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000948/pdfft?md5=1e0e8c2dca5cc80e5c38837feded9d5f&pid=1-s2.0-S0004370224000948-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308373","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":"Stability based on single-agent deviations in additively separable hedonic games","authors":"Felix Brandt ,&nbsp;Martin Bullinger ,&nbsp;Leo Tappe","doi":"10.1016/j.artint.2024.104160","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104160","url":null,"abstract":"<div><p>Coalition formation is a central concern in multiagent systems. A common desideratum for coalition structures is stability, defined by the absence of beneficial deviations of single agents. Such deviations require an agent to improve her utility by joining another coalition. On top of that, the feasibility of deviations may also be restricted by demanding consent of agents in the welcoming and/or the abandoned coalition. While most of the literature focuses on deviations constrained by unanimous consent, we also study consent decided by majority vote and introduce two new stability notions that can be seen as local variants of another solution concept called popularity. We investigate stability in additively separable hedonic games by pinpointing boundaries to computational complexity depending on the type of consent and friend-oriented utility restrictions. The latter restrictions shed new light on well-studied classes of games based on the appreciation of friends or the aversion to enemies. Many of our positive results follow from a new combinatorial observation that we call the <em>Deviation Lemma</em> and that we leverage to prove the convergence of simple and natural single-agent dynamics under fairly general conditions. Our negative results, in particular, resolve the complexity of contractual Nash stability in additively separable hedonic games.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104160"},"PeriodicalIF":14.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000961/pdfft?md5=e987438fd09ba66fd8cb7e8db197482a&pid=1-s2.0-S0004370224000961-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244960","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":"Joint learning of reward machines and policies in environments with partially known semantics","authors":"Christos K. Verginis ,&nbsp;Cevahir Koprulu ,&nbsp;Sandeep Chinchali ,&nbsp;Ufuk Topcu","doi":"10.1016/j.artint.2024.104146","DOIUrl":"10.1016/j.artint.2024.104146","url":null,"abstract":"<div><p>We study the problem of reinforcement learning for a task encoded by a reward machine. The task is defined over a set of properties in the environment, called atomic propositions, and represented by Boolean variables. One unrealistic assumption commonly used in the literature is that the truth values of these propositions are accurately known. In real situations, however, these truth values are uncertain since they come from sensors that suffer from imperfections. At the same time, reward machines can be difficult to model explicitly, especially when they encode complicated tasks. We develop a reinforcement-learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values. In order to address such uncertainties, the algorithm maintains a probabilistic estimate about the truth value of the atomic propositions; it updates this estimate according to new sensory measurements that arrive from exploration of the environment. Additionally, the algorithm maintains a hypothesis reward machine, which acts as an estimate of the reward machine that encodes the task to be learned. As the agent explores the environment, the algorithm updates the hypothesis reward machine according to the obtained rewards and the estimate of the atomic propositions' truth value. Finally, the algorithm uses a Q-learning procedure for the states of the hypothesis reward machine to determine an optimal policy that accomplishes the task. We prove that the algorithm successfully infers the reward machine and asymptotically learns a policy that accomplishes the respective task.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"333 ","pages":"Article 104146"},"PeriodicalIF":14.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000821/pdfft?md5=00403f012b025daac195daf945ec2715&pid=1-s2.0-S0004370224000821-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141178018","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":"Credulous acceptance in high-order argumentation frameworks with necessities: An incremental approach","authors":"Gianvincenzo Alfano ,&nbsp;Andrea Cohen ,&nbsp;Sebastian Gottifredi ,&nbsp;Sergio Greco ,&nbsp;Francesco Parisi ,&nbsp;Guillermo R. Simari","doi":"10.1016/j.artint.2024.104159","DOIUrl":"10.1016/j.artint.2024.104159","url":null,"abstract":"<div><p>Argumentation is an important research area in the field of AI. There is a substantial amount of work on different aspects of Dung's abstract Argumentation Framework (AF). Two relevant aspects considered separately so far are: <em>i</em>) extending the framework to account for recursive attacks and supports, and <span><math><mi>i</mi><mi>i</mi><mo>)</mo></math></span> considering dynamics, <em>i.e.</em>, AFs evolving over time. In this paper, we jointly deal with these two aspects. We focus on High-Order Argumentation Frameworks with Necessities (HOAFNs) which allow for attack and support relations (interpreted as <em>necessity</em>) not only between arguments but also targeting attacks and supports at any level. We propose an approach for the incremental evaluation of the credulous acceptance problem in HOAFNs, by “incrementally” computing an extension (a set of accepted arguments, attacks and supports), if it exists, containing a given goal element in an updated HOAFN. In particular, we are interested in monitoring the credulous acceptance of a given argument, attack or support (goal) in an evolving HOAFN. Thus, our approach assumes to have a HOAFN Δ, a goal <em>ϱ</em> occurring in Δ, an extension <em>E</em> for Δ containing <em>ϱ</em>, and an update <em>u</em> establishing some changes in the original HOAFN, and uses the extension for first checking whether the update is relevant; for relevant updates, an extension of the updated HOAFN containing the goal is computed by translating the problem to the AF domain and leveraging on AF solvers. We provide formal results for our incremental approach and empirically show that it outperforms the evaluation from scratch of the credulous acceptance problem for an updated HOAFN.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"333 ","pages":"Article 104159"},"PeriodicalIF":14.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141136689","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":"Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environments","authors":"Sara Bernardini ,&nbsp;Fabio Fagnani ,&nbsp;Alexandra Neacsu ,&nbsp;Santiago Franco","doi":"10.1016/j.artint.2024.104148","DOIUrl":"10.1016/j.artint.2024.104148","url":null,"abstract":"<div><p>In this paper, we perform a joint design of goal legibility and recognition in a cooperative, multi-agent pathfinding setting with partial observability. More specifically, we consider a set of identical agents (the actors) that move in an environment only partially observable to an observer in the loop. The actors are tasked with reaching a set of locations that need to be serviced in a timely fashion. The observer monitors the actors' behavior from a distance and needs to identify each actor's destination based on the actor's observable movements. Our approach generates legible paths for the actors; namely, it constructs one path from the origin to each destination so that these paths overlap as little as possible while satisfying budget constraints. It also equips the observer with a goal-recognition mapping between unique sequences of observations and destinations, ensuring that the observer can infer an actor's destination by making the minimum number of observations (legibility delay). Our method substantially extends previous work, which is limited to an observer with full observability, showing that optimizing pathfinding for goal legibility and recognition can be performed via a reformulation into a classical minimum cost flow problem in the partially observable case when the algorithms for the fully observable case are appropriately modified. Our empirical evaluation shows that our techniques are as effective in partially observable settings as in fully observable ones.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"333 ","pages":"Article 104148"},"PeriodicalIF":14.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000845/pdfft?md5=66bd75617c41f8c0d650bfa7aefc5bfd&pid=1-s2.0-S0004370224000845-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141136365","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":"Acquiring and modeling abstract commonsense knowledge via conceptualization","authors":"Mutian He,&nbsp;Tianqing Fang,&nbsp;Weiqi Wang,&nbsp;Yangqiu Song","doi":"10.1016/j.artint.2024.104149","DOIUrl":"10.1016/j.artint.2024.104149","url":null,"abstract":"<div><p>Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning. Despite recent progress in artificial intelligence to acquire and model commonsense attributed to neural language models and commonsense knowledge graphs (CKGs), conceptualization is yet to be introduced thoroughly, making current approaches ineffective to cover knowledge about countless diverse entities and situations in the real world. To address the problem, we thoroughly study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction by acquiring abstract knowledge about events regarding abstract concepts, as well as higher-level triples or inferences upon them. We then apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase. We annotate a dataset on the validity of contextualized conceptualizations from ATOMIC on both event and triple levels, develop a series of heuristic rules based on linguistic features, and train a set of neural models to generate and verify abstract knowledge. Based on these components, a pipeline to acquire abstract knowledge is built. A large abstract CKG upon ATOMIC is then induced, ready to be instantiated to infer about unseen entities or situations. Finally, we empirically show the benefits of augmenting CKGs with abstract knowledge in downstream tasks like commonsense inference and zero-shot commonsense QA.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"333 ","pages":"Article 104149"},"PeriodicalIF":14.4,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027260","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":"Knowledge is power: Open-world knowledge representation learning for knowledge-based visual reasoning","authors":"Wenbo Zheng ,&nbsp;Lan Yan ,&nbsp;Fei-Yue Wang","doi":"10.1016/j.artint.2024.104147","DOIUrl":"10.1016/j.artint.2024.104147","url":null,"abstract":"<div><p>Knowledge-based visual reasoning requires the ability to associate outside knowledge that is not present in a given image for cross-modal visual understanding. Two deficiencies of the existing approaches are that (1) they only employ or construct elementary and <em>explicit</em> but superficial knowledge graphs while lacking complex and <em>implicit</em> but indispensable cross-modal knowledge for visual reasoning, and (2) they also cannot reason new/<em>unseen</em> images or questions in open environments and are often violated in real-world applications. How to represent and leverage tacit multimodal knowledge for open-world visual reasoning scenarios has been less studied. In this paper, we propose a novel open-world knowledge representation learning method to not only construct implicit knowledge representations from the given images and their questions but also enable knowledge transfer from a <em>known</em> given scene to an <em>unknown</em> scene for answer prediction. Extensive experiments conducted on six benchmarks demonstrate the superiority of our approach over other state-of-the-art methods. We apply our approach to other visual reasoning tasks, and the experimental results show that our approach, with its good performance, can support related reasoning applications.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"333 ","pages":"Article 104147"},"PeriodicalIF":14.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140949791","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}