International Joint Conference on Artificial Intelligence最新文献_第10页

SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations SeRO:自监督强化学习从非分布情况中恢复

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/432

Chan Kim, JaeKyung Cho, C. Bobda, Seung-Woo Seo, Seong-Woo Kim

{"title":"SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations","authors":"Chan Kim, JaeKyung Cho, C. Bobda, Seung-Woo Seo, Seong-Woo Kim","doi":"10.24963/ijcai.2023/432","DOIUrl":"https://doi.org/10.24963/ijcai.2023/432","url":null,"abstract":"Robotic agents trained using reinforcement learning have the problem of taking unreliable actions in an out-of-distribution (OOD) state. Agents can easily become OOD in real-world environments because it is almost impossible for them to visit and learn the entire state space during training. Unfortunately, unreliable actions do not ensure that agents perform their original tasks successfully. Therefore, agents should be able to recognize whether they are in OOD states and learn how to return to the learned state distribution rather than continue to take unreliable actions. In this study, we propose a novel method for retraining agents to recover from OOD situations in a self-supervised manner when they fall into OOD states. Our in-depth experimental results demonstrate that our method substantially improves the agent’s ability to recover from OOD situations in terms of sample efficiency and restoration of the performance for the original tasks. Moreover, we show that our method can retrain the agent to recover from OOD situations even when in-distribution states are difficult to visit through exploration. Code and supplementary materials are available at https://github.com/SNUChanKim/SeRO.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"44 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":"128078158","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

Bayesian Optimization with Switching Cost: Regret Analysis and Lookahead Variants 具有切换代价的贝叶斯优化:后悔分析和前瞻变量

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/446

Peng Liu, Haowei Wang, Wei Qiyu

{"title":"Bayesian Optimization with Switching Cost: Regret Analysis and Lookahead Variants","authors":"Peng Liu, Haowei Wang, Wei Qiyu","doi":"10.24963/ijcai.2023/446","DOIUrl":"https://doi.org/10.24963/ijcai.2023/446","url":null,"abstract":"Bayesian Optimization (BO) has recently received increasing attention due to its efficiency in optimizing expensive-to-evaluate functions. For some practical problems, it is essential to consider the path-dependent switching cost between consecutive sampling locations given a total traveling budget. For example, when using a drone to locate cracks in a building wall or search for lost survivors in the wild, the search path needs to be efficiently planned given the limited battery power of the drone. Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as a constrained Markov Decision Process (MDP) and solve it by proposing a new distance-adjusted multi-step look-ahead acquisition function, the distUCB, and using rollout approximation. We also provide a theoretical regret analysis of the distUCB-based Bayesian optimization algorithm. In addition, the empirical performance of the proposed algorithm is tested based on both synthetic and real data experiments, and it shows that our cost-aware non-myopic algorithm performs better than other popular alternatives.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"39 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":"132525800","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

Front-to-End Bidirectional Heuristic Search with Consistent Heuristics: Enumerating and Evaluating Algorithms and Bounds 具有一致启发式的前端到端双向启发式搜索:枚举和评估算法和边界

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/625

Lior Siag, Shahaf S. Shperberg, Ariel Felner, Nathan R Sturtevant

引用次数: 0

Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers 变压器工具:示范生成的符号计划使用变压器

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/839

Vishal Pallagani, Bharath Muppasani, Biplav Srivastava, F. Rossi, L. Horesh, K. Murugesan, Andrea Loreggia, F. Fabiano, Rony Joseph, Yathin Kethepalli

{"title":"Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers","authors":"Vishal Pallagani, Bharath Muppasani, Biplav Srivastava, F. Rossi, L. Horesh, K. Murugesan, Andrea Loreggia, F. Fabiano, Rony Joseph, Yathin Kethepalli","doi":"10.24963/ijcai.2023/839","DOIUrl":"https://doi.org/10.24963/ijcai.2023/839","url":null,"abstract":"Plansformer is a novel tool that utilizes a fine-tuned language model based on transformer architecture to generate symbolic plans. Transformers are a type of neural network architecture that have been shown to be highly effective in a range of natural language processing tasks. Unlike traditional planning systems that use heuristic-based search strategies, Plansformer is fine-tuned on specific classical planning domains to generate high-quality plans that are both fluent and feasible. Plansformer takes the domain and problem files as input (in PDDL) and outputs a sequence of actions that can be executed to solve the problem. We demonstrate the effectiveness of Plansformer on a variety of benchmark problems and provide both qualitative and quantitative results obtained during our evaluation, including its limitations. Plansformer has the potential to significantly improve the efficiency and effectiveness of planning in various domains, from logistics and scheduling to natural language processing and human-computer interaction. In addition, we provide public access to Plansformer via a website as well as an API endpoint; this enables other researchers to utilize our tool for planning and execution. The demo video is available at https://youtu.be/_1rlctCGsrk","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"5 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132287389","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

Annealing Genetic-based Preposition Substitution for Text Rubbish Example Generation 基于退火遗传的介词替换文本垃圾样例生成

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/569

Chen Li, Xinghao Yang, Baodi Liu, Weifeng Liu, Honglong Chen

{"title":"Annealing Genetic-based Preposition Substitution for Text Rubbish Example Generation","authors":"Chen Li, Xinghao Yang, Baodi Liu, Weifeng Liu, Honglong Chen","doi":"10.24963/ijcai.2023/569","DOIUrl":"https://doi.org/10.24963/ijcai.2023/569","url":null,"abstract":"Modern Natural Language Processing (NLP) models expose under-sensitivity towards text rubbish examples. The text rubbish example is the heavily modified input text which is nonsensical to humans but does not change the model’s prediction. Prior work crafts rubbish examples by iteratively deleting words and determining the deletion order with beam search. However, the produced rubbish examples usually cause a reduction in model confidence and sometimes deliver human-readable text. To address these problems, we propose an Annealing Genetic based Preposition Substitution (AGPS) algorithm for text rubbish sample generation with two major merits. Firstly, the AGPS crafts rubbish text examples by substituting input words with meaningless prepositions instead of directly removing them, which brings less degradation to the model’s confidence. Secondly, we design an Annealing Genetic algorithm to optimize the word replacement priority, which allows the Genetic Algorithm (GA) to jump out the local optima with probabilities. This is significant in achieving better objectives, i.e., a high word modification rate and a high model confidence. Experimental results on five popular datasets manifest the superiority of AGPS compared with the baseline and expose the fact: the NLP models can not really understand the semantics of sentences, as they give the same prediction with even higher confidence for the nonsensical preposition sequences.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"75 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":"134532812","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

Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract) ISO 24617-2对话动作注释标准中通用交际功能的自动识别(扩展摘要)

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/788

Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos

{"title":"Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract)","authors":"Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos","doi":"10.24963/ijcai.2023/788","DOIUrl":"https://doi.org/10.24963/ijcai.2023/788","url":null,"abstract":"From the perspective of a dialog system, the identification of the intention behind the segments in a dialog is important, as it provides cues regarding the information present in the segments and how they should be interpreted. The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. To do so, we propose to adapt existing approaches to dialog act recognition, so that they can deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Additionally, we rely on transfer learning processes to address the data scarcity problem. Our experiments on the DialogBank show that this approach outperforms both flat and hierarchical approaches based on multiple classifiers and that each of its components plays an important role in the recognition of general-purpose communicative functions.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"38 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":"134072340","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

Multi-level Graph Contrastive Prototypical Clustering 多层次图对比原型聚类

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/513

Yuchao Zhang, Yuan Yuan, Qi Wang

{"title":"Multi-level Graph Contrastive Prototypical Clustering","authors":"Yuchao Zhang, Yuan Yuan, Qi Wang","doi":"10.24963/ijcai.2023/513","DOIUrl":"https://doi.org/10.24963/ijcai.2023/513","url":null,"abstract":"Recently, graph neural networks (GNNs) have drawn a surge of investigations in deep graph clustering. Nevertheless, existing approaches predominantly are inclined to semantic-agnostic since GNNs exhibit inherent limitations in capturing global underlying semantic structures. Meanwhile, multiple objectives are imposed within one latent space, whereas representations from different granularities may presumably conflict with each other, yielding severe performance degradation for clustering. To this end, we propose a novel Multi-Level Graph Contrastive Prototypical Clustering (MLG-CPC) framework for end-to-end clustering. Specifically, a Prototype Discrimination (ProDisc) objective function is proposed to explicitly capture semantic information via cluster assignments. Moreover, to alleviate the issue of objectives conflict, we introduce to perceive representations of different granularities within individual feature-, prototypical-, and cluster-level spaces by the feature decorrelation, prototype contrast, and cluster space consistency respectively. Extensive experiments on four benchmarks demonstrate the superiority of the proposed MLG-CPC against the state-of-the-art graph clustering approaches.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"22 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":"114229520","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

VecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data VecoCare:访问序列-临床记录联合学习在医疗保健数据中的诊断预测

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/547

Yongxin Xu, Kai Yang, Chaohe Zhang, Peinie Zou, Zhiyuan Wang, Hongxin Ding, Junfeng Zhao, Yasha Wang, Bing Xie

{"title":"VecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data","authors":"Yongxin Xu, Kai Yang, Chaohe Zhang, Peinie Zou, Zhiyuan Wang, Hongxin Ding, Junfeng Zhao, Yasha Wang, Bing Xie","doi":"10.24963/ijcai.2023/547","DOIUrl":"https://doi.org/10.24963/ijcai.2023/547","url":null,"abstract":"Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis prediction scenarios, most works are devoted to learning powerful patient representations either from structured EHR data (e.g., temporal medical events, lab test results, etc.) or unstructured data (e.g., clinical notes, etc.). However, synthesizing rich information from both of them still needs to be explored. Firstly, the heterogeneous semantic biases across them heavily hinder the synthesis of representation spaces, which is critical for diagnosis prediction. Secondly, the intermingled quality of partial clinical notes leads to inadequate representations of to-be-predicted patients. Thirdly, typical attention mechanisms mainly focus on aggregating information from similar patients, ignoring important auxiliary information from others. To tackle these challenges, we propose a novel visit sequences-clinical notes joint learning approach, dubbed VecoCare. It performs a Gromov-Wasserstein Distance (GWD)-based contrastive learning task and an adaptive masked language model task in a sequential pre-training manner to reduce heterogeneous semantic biases. After pre-training, VecoCare further aggregates information from both similar and dissimilar patients through a dual-channel retrieval mechanism. We conduct diagnosis prediction experiments on two real-world datasets, which indicates that VecoCare outperforms state-of-the-art approaches. Moreover, the findings discovered by VecoCare are consistent with the medical researches.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"194 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":"114737476","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

Context-Aware Feature Selection and Classification 上下文感知特征选择和分类

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/480

Juanyan Wang, M. Bilgic

引用次数: 0

Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning 多标签互补标签学习的无偏风险估计

International Joint Conference on Artificial Intelligence Pub Date : 2023-08-01 DOI: 10.24963/ijcai.2023/415

Yi Gao, Miao Xu, Min-Ling Zhang

{"title":"Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning","authors":"Yi Gao, Miao Xu, Min-Ling Zhang","doi":"10.24963/ijcai.2023/415","DOIUrl":"https://doi.org/10.24963/ijcai.2023/415","url":null,"abstract":"Multi-label learning (MLL) usually requires assigning multiple relevant labels to each instance. While a fully supervised MLL dataset needs a large amount of labeling effort, using complementary labels can help alleviate this burden. However, current approaches to learning from complementary labels are mainly designed for multi-class learning and assume that each instance has a single relevant label. This means that these approaches cannot be easily applied to MLL when only complementary labels are provided, where the number of relevant labels is unknown and can vary across instances. In this paper, we first propose the unbiased risk estimator for the multi-labeled complementary label learning (MLCLL) problem. We also provide an estimation error bound to ensure the convergence of the empirical risk estimator. In some cases, the unbiased estimator may give unbounded gradients for certain loss functions and result in overfitting. To mitigate this problem, we improve the risk estimator by minimizing a proper loss function, which has been shown to improve gradient updates. Our experimental results demonstrate the effectiveness of the proposed approach on various datasets.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"86 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":"115881047","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