International Joint Conference on Artificial Intelligence最新文献_第8页

Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges 对可解释的人工智能进行基准测试-对可用工具包和开放挑战的调查

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/747

Phuong Quynh Le, Meike Nauta, Van Bach Nguyen, Shreyasi Pathak, Jörg Schlötterer, Christin Seifert

{"title":"Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges","authors":"Phuong Quynh Le, Meike Nauta, Van Bach Nguyen, Shreyasi Pathak, Jörg Schlötterer, Christin Seifert","doi":"10.24963/ijcai.2023/747","DOIUrl":"https://doi.org/10.24963/ijcai.2023/747","url":null,"abstract":"The goal of Explainable AI (XAI) is to make the reasoning of a machine learning model accessible to humans, such that users of an AI system can evaluate and judge the underlying model. Due to the blackbox nature of XAI methods it is, however, hard to disentangle the contribution of a model and the explanation method to the final output. It might be unclear on whether an unexpected output is caused by the model or the explanation method. Explanation models, therefore, need to be evaluated in technical (e.g. fidelity to the model) and user-facing (correspondence to domain knowledge) terms. A recent survey has identified 29 different automated approaches to quantitatively evaluate explanations. In this work, we take an additional perspective and analyse which toolkits and data sets are available. We investigate which evaluation metrics are implemented in the toolkits and whether they produce the same results. We find that only a few aspects of explanation quality are currently covered, data sets are rare and evaluation results are not comparable across different toolkits. Our survey can serve as a guide for the XAI community for identifying future directions of research, and most notably, standardisation of evaluation.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129246245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fast Algorithms for SAT with Bounded Occurrences of Variables 具有有界变量出现的SAT快速算法

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/223

Junqiang Peng, Mingyu Xiao

引用次数: 0

Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data 超越纯文本:基于文本和表格数据的财务报告总结

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/581

Ziao Wang, Zelin Jiang, Xiaofeng Zhang, Jaehyeon Soon, Jialu Zhang, Xiaoyao Wang, Hongwei Du

{"title":"Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data","authors":"Ziao Wang, Zelin Jiang, Xiaofeng Zhang, Jaehyeon Soon, Jialu Zhang, Xiaoyao Wang, Hongwei Du","doi":"10.24963/ijcai.2023/581","DOIUrl":"https://doi.org/10.24963/ijcai.2023/581","url":null,"abstract":"Abstractive text summarization is to generate concise summaries that well preserve both salient information and the overall semantic meanings of the given documents. However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. Particularly, we first manually construct a “table+text → summary” dataset. Then, the tabular data is respectively embedded in a row-wise and column-wise manner, and the textual data is encoded at the sentence-level via an employed pre-trained model. We propose a salient detector gate respectively performed between each pair of row/column and sentence embeddings. The highly correlated content is considered as salient information that must be summarized. Extensive experiments have been performed on our constructed dataset and the promising results demonstrate the effectiveness of the proposed approach w.r.t. a number of both automatic and human evaluation criteria.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Small Decision Trees with Large Domain 用大域学习小决策树

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/355

E. Eiben, S. Ordyniak, Giacomo Paesani, Stefan Szeider

{"title":"Learning Small Decision Trees with Large Domain","authors":"E. Eiben, S. Ordyniak, Giacomo Paesani, Stefan Szeider","doi":"10.24963/ijcai.2023/355","DOIUrl":"https://doi.org/10.24963/ijcai.2023/355","url":null,"abstract":"One favors decision trees (DTs) of the smallest size or depth to facilitate explainability and interpretability. However, learning such an optimal DT from data is well-known to be NP-hard. To overcome this complexity barrier, Ordyniak and Szeider (AAAI 21) initiated the study of optimal DT learning under the parameterized complexity perspective. They showed that solution size (i.e., number of nodes or depth of the DT) is insufficient to obtain fixed-parameter tractability (FPT). Therefore, they proposed an FPT algorithm that utilizes two auxiliary parameters: the maximum difference (as a structural property of the data set) and maximum domain size. They left it as an open question of whether bounding the maximum domain size is necessary.\u0000\u0000\u0000\u0000The main result of this paper answers this question. We present FPT algorithms for learning a smallest or lowest-depth DT from data, with the only parameters solution size and maximum difference. Thus, our algorithm is significantly more potent than the one by Szeider and Ordyniak as it can handle problem inputs with features that range over unbounded domains. We also close several gaps concerning the quality of approximation one obtains by only considering DTs based on minimum support sets.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125463161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Topological Planning with Post-unique and Unary Actions 具有后唯一和一元动作的拓扑规划

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/603

G. Prévost, Stéphane Cardon, T. Cazenave, C. Guettier, Éric, Jacopin

引用次数: 0

An Experimental Comparison of Multiwinner Voting Rules on Approval Elections 批准选举中多赢家投票规则的实验比较

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/298

P. Faliszewski, M. Lackner, Krzysztof Sornat, Stanislaw Szufa

引用次数: 0

PARTNER: A Persuasive Mental Health and Legal Counselling Dialogue System for Women and Children Crime Victims 合作伙伴:为妇女和儿童犯罪受害者提供有说服力的心理健康和法律咨询对话系统

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/686

Priyanshu Priya, Kshitij Mishra, Palak Totala, Asif Ekbal

{"title":"PARTNER: A Persuasive Mental Health and Legal Counselling Dialogue System for Women and Children Crime Victims","authors":"Priyanshu Priya, Kshitij Mishra, Palak Totala, Asif Ekbal","doi":"10.24963/ijcai.2023/686","DOIUrl":"https://doi.org/10.24963/ijcai.2023/686","url":null,"abstract":"The World Health Organization has underlined the significance of expediting the preventive measures for crime against women and children to attain the United Nations Sustainable Development Goals 2030 (promoting well-being, gender equality, and equal access to justice). The crime victims typically need mental health and legal counselling support for their ultimate well-being and sometimes they need to be persuaded to seek desired support. Further, counselling interactions should adopt correct politeness and empathy strategies so that a warm, amicable, and respectful environment can be built to better understand the victims’ situations. To this end, we propose PARTNER, a Politeness and empAthy strategies-adaptive peRsuasive dialogue sysTem for meNtal health and LEgal counselling of cRime victims. For this, first, we create a novel mental HEalth and legAl counseLling conversational dataset HEAL, annotated with three distinct aspects, viz. counselling act, politeness strategy, and empathy strategy. Then, by formulating a novel reward function, we train a counselling dialogue system in a reinforcement learning setting to ensure correct counselling act, politeness strategy, and empathy strategy in the generated responses. Extensive empirical analysis and experimental results show that the proposed reward function ensures persuasive counselling responses with correct polite and empathetic tone in the generated responses. Further, PARTNER proves its efficacy to engage the victim by generating diverse and natural responses.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121304008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuro-Symbolic Class Expression Learning 神经符号类表达学习

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/403

Caglar Demir, A. N. Ngomo

{"title":"Neuro-Symbolic Class Expression Learning","authors":"Caglar Demir, A. N. Ngomo","doi":"10.24963/ijcai.2023/403","DOIUrl":"https://doi.org/10.24963/ijcai.2023/403","url":null,"abstract":"Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable.\u0000\u0000In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. By learning non-myopic heuristic functions with deep Q-learning, DRILL efficiently steers the standard search procedure in a quasi-ordered search space towards goal states. Our extensive experiments on 4 benchmark datasets and 390 learning problems suggest that DRILL converges to goal states at least 2.7 times faster than state-of-the-art models on all learning problems. The results of our statistical significance test confirms that DRILL converges to goal states significantly faster (p-value <1%) than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of DRILL, including pre-trained models, training and evaluation scripts.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121507620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery 抠图时刻:一个统一的数据驱动的抠图引擎的移动AIGC在照片库

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/845

Yanhao Zhang, Fanyi Wang, Weixuan Sun, Jingwen Su, Peng Liu, Yaqian Li, Xinjie Feng, Zhengxia Zou

{"title":"Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery","authors":"Yanhao Zhang, Fanyi Wang, Weixuan Sun, Jingwen Su, Peng Liu, Yaqian Li, Xinjie Feng, Zhengxia Zou","doi":"10.24963/ijcai.2023/845","DOIUrl":"https://doi.org/10.24963/ijcai.2023/845","url":null,"abstract":"Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called \"Matting Moments\" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122257696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating Unlikely Negative Cues for Distinctive Image Captioning 将不太可能的负面线索纳入独特的图像标题

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/83

Zhengcong Fei, Junshi Huang

{"title":"Incorporating Unlikely Negative Cues for Distinctive Image Captioning","authors":"Zhengcong Fei, Junshi Huang","doi":"10.24963/ijcai.2023/83","DOIUrl":"https://doi.org/10.24963/ijcai.2023/83","url":null,"abstract":"While recent neural image captioning models have shown great promise in terms of automatic metrics, they still struggle with generating generic sentences, which limits their use to only a handful of simple scenarios. On the other hand, negative training has been suggested as an effective way to prevent models from producing frequent yet meaningless sentences. However, when applied to image captioning, this approach may overlook low-frequency but generic and vague sentences, which can be problematic when dealing with diverse and changeable visual scenes. In this paper, we introduce a approach to improve image captioning by integrating negative knowledge that focuses on preventing the model from producing undesirable generic descriptions while addressing previous limitations. We accomplish this by training a negative teacher model that generates image-wise generic sentences with retrieval entropy-filtered data. Subsequently, the student model is required to maximize the distance with multi-level negative knowledge transferring for optimal guiding. Empirical results evaluated on MS COCO benchmark confirm that our plug-and-play framework incorporating unlikely negative knowledge leads to significant improvements in both accuracy and diversity, surpassing previous state-of-the-art methods for distinctive image captioning.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116034454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1