Artificial Intelligence最新文献

{"title":"Lifted action models learning from partial traces","authors":"Leonardo Lamanna ,&nbsp;Luciano Serafini ,&nbsp;Alessandro Saetti ,&nbsp;Alfonso Emilio Gerevini ,&nbsp;Paolo Traverso","doi":"10.1016/j.artint.2024.104256","DOIUrl":"10.1016/j.artint.2024.104256","url":null,"abstract":"<div><div>For applying symbolic planning, there is the necessity of providing the specification of a symbolic action model, which is usually manually specified by a domain expert. However, such an encoding may be faulty due to either human errors or lack of domain knowledge. Therefore, learning the symbolic action model in an automated way has been widely adopted as an alternative to its manual specification. In this paper, we focus on the problem of learning action models offline, from an input set of partially observable plan traces. In particular, we propose an approach to: <em>(i)</em> augment the observability of a given plan trace by applying predefined logical rules; <em>(ii)</em> learn the preconditions and effects of each action in a plan trace from partial observations before and after the action execution. We formally prove that our approach learns action models with fundamental theoretical properties, not provided by other methods. We experimentally show that our approach outperforms a state-of-the-art method on a large set of existing benchmark domains. Furthermore, we compare the effectiveness of the learned action models for solving planning problems and show that the action models learned by our approach are much more effective w.r.t. a state-of-the-art method.<span><span>¹</span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104256"},"PeriodicalIF":5.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643211","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":"Human-AI coevolution","authors":"Dino Pedreschi ,&nbsp;Luca Pappalardo ,&nbsp;Emanuele Ferragina ,&nbsp;Ricardo Baeza-Yates ,&nbsp;Albert-László Barabási ,&nbsp;Frank Dignum ,&nbsp;Virginia Dignum ,&nbsp;Tina Eliassi-Rad ,&nbsp;Fosca Giannotti ,&nbsp;János Kertész ,&nbsp;Alistair Knott ,&nbsp;Yannis Ioannidis ,&nbsp;Paul Lukowicz ,&nbsp;Andrea Passarella ,&nbsp;Alex Sandy Pentland ,&nbsp;John Shawe-Taylor ,&nbsp;Alessandro Vespignani","doi":"10.1016/j.artint.2024.104244","DOIUrl":"10.1016/j.artint.2024.104244","url":null,"abstract":"<div><div>Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: <em>(i)</em> outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; <em>(ii)</em> propose a reflection at the intersection between complexity science, AI and society; <em>(iii)</em> provide real-world examples for different human-AI ecosystems; and <em>(iv)</em> illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104244"},"PeriodicalIF":5.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643212","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":"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":"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":"Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning","authors":"Rex G. Liu,&nbsp;Michael J. Frank","doi":"10.1016/j.artint.2022.103770","DOIUrl":"10.1016/j.artint.2022.103770","url":null,"abstract":"<div><p>A hallmark of human intelligence, but challenging for reinforcement learning<span> (RL) agents, is the ability to compositionally generalise, that is, to recompose familiar knowledge components in novel ways to solve new problems. For instance, when navigating in a city, one needs to know the location of the destination and how to operate a vehicle to get there, whether it be pedalling a bike or operating a car. In RL, these correspond to the reward function and transition function, respectively. To compositionally generalize, these two components need to be transferable independently of each other: multiple modes of transport can reach the same goal, and any given mode can be used to reach multiple destinations. Yet there are also instances where it can be helpful to learn and transfer entire structures, jointly representing goals and transitions, particularly whenever these recur in natural tasks (e.g., given a suggestion to get ice cream, one might prefer to bike, even in new towns). Prior theoretical work has explored how, in model-based RL, agents can learn and generalize task components (transition and reward functions). But a satisfactory account for how a single agent can simultaneously satisfy the two competing demands is still lacking. Here, we propose a hierarchical RL agent that learns and transfers individual task components as well as entire structures (particular compositions of components) by inferring both through a non-parametric Bayesian model<span><span> of the task. It maintains a factorised representation of task components through a hierarchical Dirichlet<span> process, but it also represents different possible covariances between these components through a standard Dirichlet process. We validate our approach on a variety of navigation tasks covering a wide range of statistical correlations between task components and show that it can also improve generalisation and transfer in more complex, hierarchical tasks with goal/subgoal structures. Finally, we end with a discussion of our work including how this </span></span>clustering algorithm could conceivably be implemented by cortico-striatal gating circuits in the brain.</span></span></p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"312 ","pages":"Article 103770"},"PeriodicalIF":14.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10279716","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":"Modeling the complex dynamics and changing correlations of epileptic events","authors":"Drausin F. Wulsin ,&nbsp;Emily B. Fox ,&nbsp;Brian Litt","doi":"10.1016/j.artint.2014.05.006","DOIUrl":"10.1016/j.artint.2014.05.006","url":null,"abstract":"<div><p>Patients with epilepsy can manifest short, sub-clinical epileptic “bursts” in addition to full-blown clinical seizures. We believe the relationship between these two classes of events—something not previously studied quantitatively—could yield important insights into the nature and intrinsic dynamics of seizures. A goal of our work is to parse these complex epileptic events into distinct dynamic regimes. A challenge posed by the intracranial EEG (iEEG) data we study is the fact that the number and placement of electrodes can vary between patients. We develop a Bayesian nonparametric Markov switching process that allows for (i) shared dynamic regimes between a variable number of channels, (ii) asynchronous regime-switching, and (iii) an unknown dictionary of dynamic regimes. We encode a sparse and changing set of dependencies between the channels using a Markov-switching Gaussian graphical model for the innovations process driving the channel dynamics and demonstrate the importance of this model in parsing and out-of-sample predictions of iEEG data. We show that our model produces intuitive state assignments that can help automate clinical analysis of seizures and enable the comparison of sub-clinical bursts and full clinical seizures.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"216 ","pages":"Pages 55-75"},"PeriodicalIF":14.4,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.artint.2014.05.006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9368620","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}