Proceedings of the AAAI Symposium Series最新文献_第9页

Modeling Human-Like Acquisition of Language and Concepts 模拟人类学习语言和概念的过程

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31275

Peter Lindes, Steven Jones

{"title":"Modeling Human-Like Acquisition of Language and Concepts","authors":"Peter Lindes, Steven Jones","doi":"10.1609/aaaiss.v3i1.31275","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31275","url":null,"abstract":"Humans acquire language and related concepts in a trajectory over a lifetime. Concepts for simple interaction with the world are learned before language. Later, words are learned to name these concepts along with structures needed to represent larger meanings. Eventually, language advances to where it can drive the learning of new concepts. Throughout this trajectory a language processing capability uses architectural mechanisms to process language using the knowledge already acquired. We assume that this growing body of knowledge is made up of small units of form-meaning mapping that can be composed in many ways, suggesting that these units are learned incrementally from experience. In prior work we have built a system to comprehend human language within an autonomous robot using knowledge in such units developed by hand. Here we propose a research program to develop the ability of an artificial agent to acquire this knowledge incrementally and autonomously from its experience in a similar trajectory. We then propose a strategy for evaluating this human-like learning system using a large benchmark created as a tool for training deep learning systems. We expect that our human-like learning system will produce better task performance from training on only a small subset of this benchmark.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"81 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121330","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

Personalized Image Generation Through Swiping 通过轻扫生成个性化图像

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31238

Yuto Nakashima

引用次数: 0

Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs 使用知识图谱对机器学习分类器进行基于规则的解释

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31200

Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou

{"title":"Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs","authors":"Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou","doi":"10.1609/aaaiss.v3i1.31200","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31200","url":null,"abstract":"The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that has lately gained a lot of traction. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier escaping the constraints of using the features of raw data as a means to express the explanations, providing a promising solution to the problem of the understandability of explanations. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel theoretical framework for representing explanations of its operation, in the form of query-based rules expressed in the terminology of the knowledge graph. This allows for explaining opaque black-box classifiers, using terminology and information that is independent of the features of the classifier and its domain of application, leading to more understandable explanations but also allowing the creation of different levels of explanations according to the final end-user.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122493","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

Faithful Reasoning over Scientific Claims 对科学主张的忠实推理

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31209

N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock

{"title":"Faithful Reasoning over Scientific Claims","authors":"N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock","doi":"10.1609/aaaiss.v3i1.31209","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31209","url":null,"abstract":"Claim verification in scientific domains requires models that faithfully incorporate relevant knowledge from the ever-growing, vast existing literature. \u0000Unfaithful claim verifications can lead to misinformation such as those observed during the COVID-19 pandemic. Fact-checking systems often fail to capture the complex relationship between claims and evidence, especially with ambiguous claims and implicit assumptions. Relying only on current LLMs poses challenges due to hallucinations and information traceability issues. To address these challenges, our approach considers multiple viewpoints onto the scientific literature, enabling the assessment of contradictory arguments and implicit assumptions. Our proposed inference method adds faithful reasoning to large language models by distilling information from diverse, relevant scientific abstracts. This method provides a verdict label that can be weighted by the reputation of the scientific articles and an explanation that can be traced back to sources. Our findings demonstrate that humans not only perceive our explanation to be significantly superior to the off-the-shelf model, but they also evaluate it as faithfully enabling the tracing of evidence back to its original sources.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120971","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

Toward Autonomy: Metacognitive Learning for Enhanced AI Performance 迈向自主：元认知学习提升人工智能性能

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31270

Brendan Conway-Smith, Robert L. West

引用次数: 1

Toward Human-Like Representation Learning for Cognitive Architectures 面向认知架构的类人表征学习

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31274

Steven Jones, Peter Lindes

引用次数: 0

On Replacing Humans with Large Language Models in Voice-Based Human-in-the-Loop Systems 在基于语音的 "人在回路 "系统中用大型语言模型取代人类

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31178

Shih-Hong Huang, Ting-Hao 'Kenneth' Huang

{"title":"On Replacing Humans with Large Language Models in Voice-Based Human-in-the-Loop Systems","authors":"Shih-Hong Huang, Ting-Hao 'Kenneth' Huang","doi":"10.1609/aaaiss.v3i1.31178","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31178","url":null,"abstract":"It is easy to assume that Large Language Models (LLMs) will seamlessly take over applications, especially those that are largely automated. In the case of conversational voice assistants, commercial systems have been widely deployed and used over the past decade. However, are we indeed on the cusp of the future we envisioned? There exists a social-technical gap between what people want to accomplish and the actual capability of technology. In this paper, we present a case study comparing two voice assistants built on Amazon Alexa: one employing a human-in-the-loop workflow, the other utilizes LLM to engage in conversations with users. In our comparison, we discovered that the issues arising in current human-in-the-loop and LLM systems are not identical. However, the presence of a set of similar issues in both systems leads us to believe that focusing on the interaction between users and systems is crucial, perhaps even more so than focusing solely on the underlying technology itself. Merely enhancing the performance of the workers or the models may not adequately address these issues. This observation prompts our research question: What are the overlooked contributing factors in the effort to improve the capabilities of voice assistants, which might not have been emphasized in prior research?","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"45 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118958","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

Human-Like Learning of Social Reasoning via Analogy 通过类比进行类人社会推理学习

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31284

Irina Rabkina

引用次数: 0

Inclusion Ethics in AI: Use Cases in African Fashion 人工智能中的包容伦理：非洲时尚界的使用案例

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31266

Christelle Scharff, James Brusseau, K. Bathula, Kaleemunnisa Fnu, Samyak Rakesh Meshram, Om Gaikhe

{"title":"Inclusion Ethics in AI: Use Cases in African Fashion","authors":"Christelle Scharff, James Brusseau, K. Bathula, Kaleemunnisa Fnu, Samyak Rakesh Meshram, Om Gaikhe","doi":"10.1609/aaaiss.v3i1.31266","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31266","url":null,"abstract":"This paper addresses the ethics of inclusion in artificial in-telligence in the context of African fashion. Despite the proliferation of fashion-related AI applications and da-tasets global diversity remains limited, and African fash-ion is significantly underrepresented. This paper docu-ments two use-cases that enhance AI's inclusivity by in-corporating sub-Saharan fashion elements. The first case details the creation of a Senegalese fashion dataset and a model for classifying traditional apparel using transfer learning. The second case investigates African wax textile patterns generated through generative adversarial net-works (GANs), specifically StyleGAN architectures, and machine learning diffusion models. Alongside the practi-cal, technological advances, theoretical ethical progress is made in two directions. First, the cases are used to elabo-rate and define the ethics of inclusion, while also contrib-uting to current debates about how inclusion differs from ethical fairness. Second, the cases engage with the ethical debate on whether AI innovation should be slowed to prevent ethical imbalances or accelerated to solve them.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"18 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120817","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-Criterion Client Selection for Efficient Federated Learning 高效联盟学习的多标准客户端选择

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31227

Mehreen Tahir, Muhammad Intizar Ali

{"title":"Multi-Criterion Client Selection for Efficient Federated Learning","authors":"Mehreen Tahir, Muhammad Intizar Ali","doi":"10.1609/aaaiss.v3i1.31227","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31227","url":null,"abstract":"Federated Learning (FL) has received tremendous attention as a decentralized machine learning (ML) framework that allows distributed data owners to collaboratively train a global model without sharing raw data. Since FL trains the model directly on edge devices, the heterogeneity of participating clients in terms of data distribution, hardware capabilities and network connectivity can significantly impact the overall performance of FL systems. Optimizing for model accuracy could extend the training time due to the diverse and resource-constrained nature of edge devices while minimizing training time could compromise the model's accuracy. Effective client selection thus becomes crucial to ensure that the training process is not only efficient but also capitalizes on the diverse data and computational capabilities of different devices. To this end, we propose FedPROM, a novel framework that tackles client selection in FL as a multi-criteria optimization problem. By leveraging the PROMETHEE method, FedPROM ranks clients based on their suitability for a given FL task, considering multiple criteria such as system resources, network conditions, and data quality. This approach allows FedPROM to dynamically select the most appropriate set of clients for each learning round, optimizing both model accuracy and training efficiency. Our evaluations on diverse datasets demonstrate that FedPROM outperforms several state-of-the-art FL client selection protocols in terms of convergence speed, and accuracy, highlighting the framework's effectiveness and the importance of multi-criteria client selection in FL.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"30 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119064","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