Artificial Intelligence最新文献

{"title":"ASQ-IT: Interactive explanations for reinforcement-learning agents","authors":"Yotam Amitai ,&nbsp;Ofra Amir ,&nbsp;Guy Avni","doi":"10.1016/j.artint.2024.104182","DOIUrl":"10.1016/j.artint.2024.104182","url":null,"abstract":"<div><p>As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers' intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive explanation system that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT's user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT and that using ASQ-IT assists users in identifying faulty agent behaviors.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104182"},"PeriodicalIF":5.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952324","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":"Planning with mental models – Balancing explanations and explicability","authors":"Sarath Sreedharan ,&nbsp;Tathagata Chakraborti ,&nbsp;Christian Muise ,&nbsp;Subbarao Kambhampati","doi":"10.1016/j.artint.2024.104181","DOIUrl":"10.1016/j.artint.2024.104181","url":null,"abstract":"<div><p>Human-aware planning involves generating plans that are explicable, i.e. conform to user expectations, as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. To achieve this, we conceive a first-of-its-kind planner <span>MEGA</span> that can reason about the possibility of explaining a plan <em>in the plan generation process itself</em>. We will also explore how solutions to such problems can be expressed as “self-explaining plans” – and show how this representation allows us to leverage classical planning compilations of epistemic planning to reason about this trade-off at plan generation time without having to incur the computational burden of having to search in the space of differences between the agent model and the mental model of the human in the loop in order to come up with the optimal trade-off. We will illustrate these concepts in two well-known planning domains, as well as with a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104181"},"PeriodicalIF":5.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842294","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 note on incorrect inferences in non-binary qualitative probabilistic networks","authors":"Jack Storror Carter","doi":"10.1016/j.artint.2024.104180","DOIUrl":"10.1016/j.artint.2024.104180","url":null,"abstract":"<div><p>Qualitative probabilistic networks (QPNs) combine the conditional independence assumptions of Bayesian networks with the qualitative properties of positive and negative dependence. They formalise various intuitive properties of positive dependence to allow inferences over a large network of variables. However, we will demonstrate in this paper that, due to an incorrect symmetry property, many inferences obtained in non-binary QPNs are not mathematically true. We will provide examples of such incorrect inferences and briefly discuss possible resolutions.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104180"},"PeriodicalIF":5.1,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729395","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":"Assessing fidelity in XAI post-hoc techniques: A comparative study with ground truth explanations datasets","authors":"Miquel Miró-Nicolau ,&nbsp;Antoni Jaume-i-Capó ,&nbsp;Gabriel Moyà-Alcover","doi":"10.1016/j.artint.2024.104179","DOIUrl":"10.1016/j.artint.2024.104179","url":null,"abstract":"<div><p>The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI) methods to their underlying models is a challenging task, primarily due to the absence of a ground truth for explanations. However, assessing fidelity is a necessary step for ensuring a correct XAI methodology. In this study, we conduct a fair and objective comparison of the current state-of-the-art XAI methods by introducing three novel image datasets with reliable ground truth for explanations. The primary objective of this comparison is to identify methods with low fidelity and eliminate them from further research, thereby promoting the development of more trustworthy and effective XAI techniques. Our results demonstrate that XAI methods based on the direct gradient calculation and the backpropagation of output information to input yield higher accuracy and reliability compared to methods relying on perturbation based or Class Activation Maps (CAM). However, these methods tend to generate more noisy saliency maps. These findings have significant implications for the advancement of XAI methods, enabling the elimination of erroneous explanations and fostering the development of more robust and reliable XAI.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104179"},"PeriodicalIF":5.1,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001152/pdfft?md5=aa7feedd4216bc7f0fc0893dee291096&pid=1-s2.0-S0004370224001152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704429","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":"Class fairness in online matching","authors":"Hadi Hosseini ,&nbsp;Zhiyi Huang ,&nbsp;Ayumi Igarashi ,&nbsp;Nisarg Shah","doi":"10.1016/j.artint.2024.104177","DOIUrl":"10.1016/j.artint.2024.104177","url":null,"abstract":"<div><p>We initiate the study of fairness among <em>classes</em> of agents in online bipartite matching where there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival. In this setting, agents are partitioned into classes and the matching is required to be fair with respect to the classes. We adapt popular fairness notions (e.g. envy-freeness, proportionality, and maximin share) and their relaxations to this setting and study deterministic algorithms for matching indivisible items (leading to integral matchings) and for matching divisible items (leading to fractional matchings). For matching indivisible items, we propose an adaptive-priority-based algorithm, <span>Match-and-Shift</span>, prove that it achieves <span><math><mfrac><mrow><mn>1</mn></mrow><mrow><mn>2</mn></mrow></mfrac></math></span>-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. For matching divisible items, we design a water-filling-based algorithm, <span>Equal-Filling</span>, that achieves <span><math><mo>(</mo><mn>1</mn><mo>−</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>e</mi></mrow></mfrac><mo>)</mo></math></span>-approximation of class envy-freeness and class proportionality; we prove <span><math><mn>1</mn><mo>−</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>e</mi></mrow></mfrac></math></span> to be tight for class proportionality and establish a <span><math><mfrac><mrow><mn>3</mn></mrow><mrow><mn>4</mn></mrow></mfrac></math></span> upper bound on class envy-freeness. Finally, we discuss several challenges in designing randomized algorithms that achieve reasonable fairness approximation ratios. Nonetheless, we build upon <span>Equal-Filling</span> to design a randomized algorithm for matching indivisible items, <span>Equal-Filling-OCS</span>, which achieves 0.593-approximation of class proportionality.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104177"},"PeriodicalIF":5.1,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638644","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":"Incremental measurement of structural entropy for dynamic graphs","authors":"Runze Yang ,&nbsp;Hao Peng ,&nbsp;Chunyang Liu ,&nbsp;Angsheng Li","doi":"10.1016/j.artint.2024.104175","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104175","url":null,"abstract":"<div><p>Structural entropy is a metric that measures the amount of information embedded in graph structure data under a strategy of hierarchical abstracting. To measure the structural entropy of a dynamic graph, we need to decode the optimal encoding tree corresponding to the best community partitioning for each snapshot. However, the current methods do not support dynamic encoding tree updating and incremental structural entropy computation. To address this issue, we propose <em>Incre-2dSE</em>, a novel incremental measurement framework that dynamically adjusts the community partitioning and efficiently computes the updated structural entropy for each updated graph. Specifically, <em>Incre-2dSE</em> includes incremental algorithms based on two dynamic adjustment strategies for two-dimensional encoding trees, i.e., <em>the naive adjustment strategy</em> and <em>the node-shifting adjustment strategy</em>, which support theoretical analysis of updated structural entropy and incrementally optimize community partitioning towards a lower structural entropy. We conduct extensive experiments on 3 artificial datasets generated by <em>Hawkes Process</em> and 3 real-world datasets. Experimental results confirm that our incremental algorithms effectively capture the dynamic evolution of the communities, reduce time consumption, and provide great interpretability.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104175"},"PeriodicalIF":5.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542832","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":"Controlled query evaluation in description logics through consistent query answering","authors":"Gianluca Cima ,&nbsp;Domenico Lembo ,&nbsp;Riccardo Rosati ,&nbsp;Domenico Fabio Savo","doi":"10.1016/j.artint.2024.104176","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104176","url":null,"abstract":"<div><p>Controlled Query Evaluation (CQE) is a framework for the protection of confidential data, where a <em>policy</em> given in terms of logic formulae indicates which information must be kept private. Functions called <em>censors</em> filter query answering so that no answers are returned that may lead a user to infer data protected by the policy. The preferred censors, called <em>optimal</em> censors, are the ones that conceal only what is necessary, thus maximizing the returned answers. Typically, given a policy over a data or knowledge base, several optimal censors exist.</p><p>Our research on CQE is based on the following intuition: confidential data are those that violate the logical assertions specifying the policy, and thus censoring them in query answering is similar to processing queries in the presence of inconsistent data as studied in Consistent Query Answering (CQA). In this paper, we investigate the relationship between CQE and CQA in the context of Description Logic ontologies. We borrow the idea from CQA that query answering is a form of skeptical reasoning that takes into account all possible optimal censors. This approach leads to a revised notion of CQE, which allows us to avoid making an arbitrary choice on the censor to be selected, as done by previous research on the topic.</p><p>We then study the data complexity of query answering in our CQE framework, for conjunctive queries issued over ontologies specified in the popular Description Logics <span><math><msub><mrow><mtext>DL-Lite</mtext></mrow><mrow><mi>R</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>EL</mi></mrow><mrow><mo>⊥</mo></mrow></msub></math></span>. In our analysis, we consider some variants of the censor language, which is the language used by the censor to enforce the policy. Whereas the problem is in general intractable for simple censor languages, we show that for <span><math><msub><mrow><mtext>DL-Lite</mtext></mrow><mrow><mi>R</mi></mrow></msub></math></span> ontologies it is first-order rewritable, and thus in AC⁰ in data complexity, for the most expressive censor language we propose.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104176"},"PeriodicalIF":5.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001127/pdfft?md5=ee177d55b636c08d6ce8c57b16674343&pid=1-s2.0-S0004370224001127-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594823","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":"Adversarial analysis of similarity-based sign prediction","authors":"Michał T. Godziszewski ,&nbsp;Marcin Waniek ,&nbsp;Yulin Zhu ,&nbsp;Kai Zhou ,&nbsp;Talal Rahwan ,&nbsp;Tomasz P. Michalak","doi":"10.1016/j.artint.2024.104173","DOIUrl":"10.1016/j.artint.2024.104173","url":null,"abstract":"<div><p>Adversarial social network analysis explores how social links can be altered or otherwise manipulated to hinder unwanted information collection. To date, however, problems of this kind have not been studied in the context of signed networks in which links have positive and negative labels. Such formalism is often used to model social networks with positive links indicating friendship or support and negative links indicating antagonism or opposition.</p><p>In this work, we present a computational analysis of the problem of attacking sign prediction in signed networks, whereby the aim of the attacker (a network member) is to hide from the defender (an analyst) the signs of a target set of links by removing the signs of some other, non-target, links. While the problem turns out to be NP-hard if either local or global similarity measures are used for sign prediction, we provide a number of positive computational results, including an FPT-algorithm for eliminating common signed neighborhood and heuristic algorithms for evading local similarity-based link prediction in signed networks.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104173"},"PeriodicalIF":5.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638643","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":"Hyper-heuristics for personnel scheduling domains","authors":"","doi":"10.1016/j.artint.2024.104172","DOIUrl":"10.1016/j.artint.2024.104172","url":null,"abstract":"<div><p>In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Rotating Workforce Scheduling, Minimum Shift Design, and Bus Driver Scheduling. Instead of designing very specific solution methods, we propose to use the more general approach based on hyper-heuristics which take a set of simpler low-level heuristics and combine them to automatically create a fitting heuristic for the problem at hand. This paper presents a major study on applying hyper-heuristics to these domains, which contributes in four different ways: First, it defines new low-level heuristics for these scheduling domains, allowing to apply hyper-heuristics to them for the first time. Second, it provides a comparison of several state-of-the-art hyper-heuristics on those domains. Third, new best solutions for several instances of the different problem domains are found. Finally, a detailed investigation of the use of low-level heuristics by the hyper-heuristics gives insights in the way hyper-heuristics apply to different domains and the importance of different low-level heuristics. The results show that hyper-heuristics are able to perform well even on very complex practical problem domains in the area of scheduling and, while being more general and requiring less problem-specific adaptation, can in several cases compete with specialized algorithms for the specific problems. Several hyper-heuristics with very good performance across different real-life domains are identified. They can efficiently select low-level heuristics to apply for each domain, but for repeated application they benefit from evaluating and selecting the most useful subset of these heuristics. These results help to improve industrial systems in use for solving different scheduling scenarios by allowing faster and easier adaptation to new problem variants.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104172"},"PeriodicalIF":5.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001085/pdfft?md5=4b18a79ac0a3f1adc46a5f873b25eac7&pid=1-s2.0-S0004370224001085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574902","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":"Boosting optimal symbolic planning: Operator-potential heuristics","authors":"","doi":"10.1016/j.artint.2024.104174","DOIUrl":"10.1016/j.artint.2024.104174","url":null,"abstract":"<div><p>Heuristic search guides the exploration of states via heuristic functions <em>h</em> estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both approaches together constitute the state of the art. Yet combinations of the two have so far not been an unqualified success, because (i) <em>h</em> must be applicable to sets of states rather than individual ones, and (ii) the different state partitioning induced by <em>h</em> may be detrimental for BDD size. Many competitive heuristic functions in planning do not qualify for (i), and it has been shown that even extremely informed heuristics can deteriorate search performance due to (ii).</p><p>Here we show how to achieve (i) for a state-of-the-art family of heuristic functions, namely potential heuristics. These assign a fixed potential value to each state-variable/value pair, ensuring by LP constraints that the sum over these values, for any state, yields an admissible and consistent heuristic function. Our key observation is that we can express potential heuristics through fixed potential values for operators instead, capturing the change of heuristic value induced by each operator. These reformulated heuristics satisfy (i) because we can express the heuristic value change as part of the BDD transition relation in symbolic search steps. We run exhaustive experiments on IPC benchmarks, evaluating several different instantiations of potential heuristics in forward, backward, and bi-directional symbolic search. Our operator-potential heuristics turn out to be highly beneficial, in particular they hardly ever suffer from (ii). Our best configurations soundly beat previous optimal symbolic planning algorithms, bringing them on par with the state of the art in optimal heuristic explicit search planning in overall performance.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"334 ","pages":"Article 104174"},"PeriodicalIF":5.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001103/pdfft?md5=e96bd05f57c63e29d7f7ad8ddd65c0e0&pid=1-s2.0-S0004370224001103-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574904","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}