Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31248
Hiroshi Sogabe, Masayuki Numao
Currently, MRI examinations and neuropsychological tests by physicians and clinical psychologists are used to screen for dementia, but they are problematic because they overwhelm medical resources and are highly invasive to patients. If automatic detection of dementia from conversations becomes feasible, it will reduce the burden on medical institutions and realize a less invasive screening method. In this paper, we constructed a machine learning model to identify dementia by extracting linguistic features and time features from the elderly corpus with a control group. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were used in the model. We compared the AUC of the single topic model and the general topic model in three cases: (I) All Features, (II) Gini Impurity, and (III) PCA + Gini Impurity. The AUC of the model constructed using RF in (III) for a single topic was 0.91, showing higher AUC than in the previous study. Furthermore, topic analysis showed that topics with high similarity in utterance content are effective in identifying MCI. In the case of the general topic, the model with AUC of 0.8 showed a high identification performance for unknown topics by cross validation on a topic-by-topic basis, indicating that the general topic model developed in this study can be applied to general conversation.
{"title":"Toward Application to General Conversation Detection of Dementia Tendency from Conversation Based on Linguistic and Time Features of Speech","authors":"Hiroshi Sogabe, Masayuki Numao","doi":"10.1609/aaaiss.v3i1.31248","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31248","url":null,"abstract":"Currently, MRI examinations and neuropsychological tests by physicians and clinical psychologists are used to screen for dementia, but they are problematic because they overwhelm medical resources and are highly invasive to patients. If automatic detection of dementia from conversations becomes feasible, it will reduce the burden on medical institutions and realize a less invasive screening method. In this paper, we constructed a machine learning model to identify dementia by extracting linguistic features and time features from the elderly corpus with a control group. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were used in the model. We compared the AUC of the single topic model and the general topic model in three cases: (I) All Features, (II) Gini Impurity, and (III) PCA + Gini Impurity. The AUC of the model constructed using RF in (III) for a single topic was 0.91, showing higher AUC than in the previous study. Furthermore, topic analysis showed that topics with high similarity in utterance content are effective in identifying MCI.\u0000In the case of the general topic, the model with AUC of 0.8 showed a high identification performance for unknown topics by cross validation on a topic-by-topic basis, indicating that the general topic model developed in this study can be applied to general conversation.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"93 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122830","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31176
Boris Galitsky
In spite of the great progress of large language models (LLMs) in recent years, there is a popular belief that their limitations need to be addressed “from outside”, by building hybrid neurosymbolic systems which add robustness, explainability, perplexity and verification done at a symbolic level. We propose shape-charged learning in the form of Meta-learning/DNN - kNN that enables the above features by integrating LMM with explainable nearest neighbor learning (kNN) to form the object-level, having deductive reasoning-based metalevel control learning processes, performing validation and correction of predictions in a way that is more interpretable by humans.
{"title":"Shaped-Charge Architecture for Neuro-Symbolic Systems","authors":"Boris Galitsky","doi":"10.1609/aaaiss.v3i1.31176","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31176","url":null,"abstract":"In spite of the great progress of large language models (LLMs) in recent years, there is a popular belief that their limitations need to be addressed “from outside”, by building hybrid neurosymbolic systems which add robustness, explainability, perplexity and verification done at a symbolic level. We propose shape-charged learning in the form of Meta-learning/DNN - kNN that enables the above features by integrating LMM with explainable nearest neighbor learning (kNN) to form the object-level, having deductive reasoning-based metalevel control learning processes, performing validation and correction of predictions in a way that is more interpretable by humans.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122324","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31258
Sambit Bhattacharya, Bogdan Czejdo, Rebecca Zulli, Adrienne A. Smith
While students are often passionate about their chosen fields, they often have limited awareness of the profound impact of AI technologies on their professions. In order to advance efforts in building subject-relevant AI literacy among undergraduate students studying Computer Science and non-Computer Science (Criminal Justice and Forensic Science) it is imperative to engage in rigorous efforts to develop and study curricular infusion of Artificial Intelligence topics. Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation. Using data collected through surveys, focus groups, critical review, and reflection exercises the external evaluation team produced findings that informed the project team in revising and improving their materials and approach for the second iteration. These efforts can help educators and the AI module developers tailor their AI curriculum to address these specific areas, ensuring that students develop a more accurate understanding of applications of AI in their future career field.
{"title":"Enhancing AI Education at an MSI: A Design-Based Research Approach","authors":"Sambit Bhattacharya, Bogdan Czejdo, Rebecca Zulli, Adrienne A. Smith","doi":"10.1609/aaaiss.v3i1.31258","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31258","url":null,"abstract":"While students are often passionate about their chosen fields, they often have limited awareness of the profound impact of AI technologies on their professions. In order to advance efforts in building subject-relevant AI literacy among undergraduate students studying Computer Science and non-Computer Science (Criminal Justice and Forensic Science) it is imperative to engage in rigorous efforts to develop and study curricular infusion of Artificial Intelligence topics. Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation. Using data collected through surveys, focus groups, critical review, and reflection exercises the external evaluation team produced findings that informed the project team in revising and improving their materials and approach for the second iteration. These efforts can help educators and the AI module developers tailor their AI curriculum to address these specific areas, ensuring that students develop a more accurate understanding of applications of AI in their future career field.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"80 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122915","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31237
Iko Nakari, K. Takadama
As a first step towards realizing an AI sleep counselor capable of generating personalized advice, this paper proposes a method for monitoring daily sleep conditions with a mattress sensor. To improve the accuracy of sleep stage estimation and to get accurate sleep structure, this paper introduced sleep domain knowledge to machine learning for improving the accuracy of sleep stage estimation. Concretely, the proposed method estimates ultradian rhythm based on the body movement density, updates prediction probabilities of each sleep stage by ML model and applies WAKE/NR3 detection based on the large/small body movement. Through the human subject experiment, the following implications have been revealed: (1) the proposed method improved the percentage of Accuracy by 65.0% from 61.5% and the QWK score by 0.196 from 0.297 by the conventional machine learning method; (2) the proposed method prevents over-NR12 estimating and is useful for understanding sleep structure by estimating REM sleep and NR3 sleep correctly. (3) the correct estimation of ultradian rhythms significantly improved the sleep stage estimation, with an Accuracy of 77.6% and a QWK score of 0.52 when all subjects' ultradian rhythms were estimated correctly.
作为实现能够生成个性化建议的人工智能睡眠顾问的第一步，本文提出了一种利用床垫传感器监测日常睡眠状况的方法。为了提高睡眠阶段估计的准确性，获得准确的睡眠结构，本文将睡眠领域知识引入机器学习，以提高睡眠阶段估计的准确性。具体来说，本文提出的方法基于身体运动密度估算超昼夜节律，通过 ML 模型更新各睡眠阶段的预测概率，并基于大/小身体运动进行 WAKE/NR3 检测。通过人体实验，揭示了以下意义：（1）拟议方法的准确率从传统机器学习方法的 61.5%提高了 65.0%，QWK 分数从 0.297 提高了 0.196；（2）拟议方法通过正确估计 REM 睡眠和 NR3 睡眠，防止了过度 NR12 估计，有助于了解睡眠结构。(3）对超昼夜节律的正确估计显著改善了睡眠阶段的估计，当所有受试者的超昼夜节律都被正确估计时，准确率为 77.6%，QWK 得分为 0.52。
{"title":"Sleep Stage Estimation by Introduction of Sleep Domain Knowledge to AI: Towards Personalized Sleep Counseling System with GenAI","authors":"Iko Nakari, K. Takadama","doi":"10.1609/aaaiss.v3i1.31237","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31237","url":null,"abstract":"As a first step towards realizing an AI sleep counselor capable of generating personalized advice, this paper proposes a method for monitoring daily sleep conditions with a mattress sensor. To improve the accuracy of sleep stage estimation and to get accurate sleep structure, this paper introduced sleep domain knowledge to machine learning for improving the accuracy of sleep stage estimation. Concretely, the proposed method estimates ultradian rhythm based on the body movement density, updates prediction probabilities of each sleep stage by ML model and applies WAKE/NR3 detection based on the large/small body movement. Through the human subject experiment, the following implications have been revealed: (1) the proposed method improved the percentage of Accuracy by 65.0% from 61.5% and the QWK score by 0.196 from 0.297 by the conventional machine learning method; (2) the proposed method prevents over-NR12 estimating and is useful for understanding sleep structure by estimating REM sleep and NR3 sleep correctly. (3) the correct estimation of ultradian rhythms significantly improved the sleep stage estimation, with an Accuracy of 77.6% and a QWK score of 0.52 when all subjects' ultradian rhythms were estimated correctly.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"24 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120531","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31175
Yaliang Chuang, Poyang David Huang
Aesthetics is a crucial aspect of design that plays a critical role in the creation process and customers' perception of outcomes. However, aesthetic expressions are highly subjective and nuanced. It often relies on designers' experiences and many trials and errors to get it right. Our research first investigated how designers and artists curated aesthetic materials and utilized them in their daily practice. Based on the result, we applied Langley's human-like learning framework to develop an interactive Style Agent system. It aims to learn designers' aesthetic expertise and utilize AI's capability to empower practitioner's creativity. In this paper, we used typographic posters as examples and conducted a preliminary evaluation of our prototype. The results showed that our system provided a modular structure for effortlessly annotating users' subjective perceptions and making the visualizations easy to interpret through performance. Overall, it acts as a facilitator to help enhance their own aesthetic awareness and empowers them to expand their design space.
{"title":"Learning Subjective Knowledge with Designer-Like Thinking and Interactive Machine Teaching","authors":"Yaliang Chuang, Poyang David Huang","doi":"10.1609/aaaiss.v3i1.31175","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31175","url":null,"abstract":"Aesthetics is a crucial aspect of design that plays a critical role in the creation process and customers' perception of outcomes. However, aesthetic expressions are highly subjective and nuanced. It often relies on designers' experiences and many trials and errors to get it right. Our research first investigated how designers and artists curated aesthetic materials and utilized them in their daily practice. Based on the result, we applied Langley's human-like learning framework to develop an interactive Style Agent system. It aims to learn designers' aesthetic expertise and utilize AI's capability to empower practitioner's creativity. In this paper, we used typographic posters as examples and conducted a preliminary evaluation of our prototype. The results showed that our system provided a modular structure for effortlessly annotating users' subjective perceptions and making the visualizations easy to interpret through performance. Overall, it acts as a facilitator to help enhance their own aesthetic awareness and empowers them to expand their design space.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"60 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121768","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31287
Jesse Roberts
Human-like learning refers to the learning done in the lifetime of the individual. However, the architecture of the human brain has been developed over millennia and represents a long process of evolutionary learning which could be viewed as a form of pre-training. Large language models (LLMs), after pre-training on large amounts of data, exhibit a form of learning referred to as in-context learning (ICL). Consistent with human-like learning, LLMs are able to use ICL to perform novel tasks with few examples and to interpret the examples through the lens of their prior experience. I examine the constraints which typify human-like learning and propose that LLMs may learn to exhibit human-like learning simply by training on human generated text.
{"title":"Do Large Language Models Learn to Human-Like Learn?","authors":"Jesse Roberts","doi":"10.1609/aaaiss.v3i1.31287","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31287","url":null,"abstract":"Human-like learning refers to the learning done in the lifetime of the individual. However, the architecture of the human brain has been developed over millennia and represents a long process of evolutionary learning which could be viewed as a form of pre-training. Large language models (LLMs), after pre-training on large amounts of data, exhibit a form of learning referred to as in-context learning (ICL). Consistent with human-like learning, LLMs are able to use ICL to perform novel tasks with few examples and to interpret the examples through the lens of their prior experience. I examine the constraints which typify human-like learning and propose that LLMs may learn to exhibit human-like learning simply by training on human generated text.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"1 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121178","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31198
Andreas Martin, Charuta Pande, Hans Friedrich Witschel, Judith Mathez
We propose conversational agents as a means to simulate expert interviews, integrated into a simulational learning environment: ChEdventure. Designing and developing conversational agents using the existing tools and frameworks requires technical knowledge and a considerable learning curve. Recently, LLMs are being leveraged for their adaptability to different domains and their ability to perform various tasks in a natural, human-like conversational style. In this work, we explore if LLMs can help educators easily create conversational agents for their individual teaching goals. We propose a generalized template-based approach using LLMs that can instantiate conversational agents as an integrable component of teaching and learning activities. We evaluate our approach using prototypes generated from this template and identify guidelines to improve the experience of educators.
{"title":"ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment Using LLMs","authors":"Andreas Martin, Charuta Pande, Hans Friedrich Witschel, Judith Mathez","doi":"10.1609/aaaiss.v3i1.31198","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31198","url":null,"abstract":"We propose conversational agents as a means to simulate expert interviews, integrated into a simulational learning environment: ChEdventure. Designing and developing conversational agents using the existing tools and frameworks requires technical knowledge and a considerable learning curve. Recently, LLMs are being leveraged for their adaptability to different domains and their ability to perform various tasks in a natural, human-like conversational style. In this work, we explore if LLMs can help educators easily create conversational agents for their individual teaching goals. We propose a generalized template-based approach using LLMs that can instantiate conversational agents as an integrable component of teaching and learning activities. We evaluate our approach using prototypes generated from this template and identify guidelines to improve the experience of educators.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119731","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31208
Lucas L. Snijder, Quirine T. S. Smit, Maaike H. T. de Boer
One of the approaches to help the demand and supply problem in the labor market domain is to change from degree-based hiring to skill-based hiring. The link between occupations, degrees and skills is captured in domain ontologies such as ESCO in Europe and O*NET in the US. Several countries are also building or extending these ontologies. The alignment of the ontologies is important, as it should be clear how they all relate. Aligning two ontologies by creating a mapping between them is a tedious task to do manually, and with the rise of generative large language models like GPT-4, we explore how language models and domain knowledge can be combined in the matching of the instances in the ontologies and in finding the specific relation between the instances (mapping refinement). We specifically focus on the process of updating a mapping, but the methods could also be used to create a first-time mapping. We compare the performance of several state-of-the-art methods such as GPT-4 and fine-tuned BERT models on the mapping between ESCO and O*NET and ESCO and CompetentNL (the Dutch variant) for both ontology matching and mapping refinement. Our findings indicate that: 1) Match-BERT-GPT, an integration of BERT and GPT, performs best in ontology matching, while 2) TaSeR outperforms GPT-4, albeit marginally, in the task of mapping refinement. These results show that domain knowledge is still important in ontology alignment, especially in the updating of a mapping in our use cases in the labor domain.
{"title":"Advancing Ontology Alignment in the Labor Market: Combining Large Language Models with Domain Knowledge","authors":"Lucas L. Snijder, Quirine T. S. Smit, Maaike H. T. de Boer","doi":"10.1609/aaaiss.v3i1.31208","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31208","url":null,"abstract":"One of the approaches to help the demand and supply problem in the labor market domain is to change from degree-based hiring to skill-based hiring. The link between occupations, degrees and skills is captured in domain ontologies such as ESCO in Europe and O*NET in the US. Several countries are also building or extending these ontologies. The alignment of the ontologies is important, as it should be clear how they all relate. Aligning two ontologies by creating a mapping between them is a tedious task to do manually, and with the rise of generative large language models like GPT-4, we explore how language models and domain knowledge can be combined in the matching of the instances in the ontologies and in finding the specific relation between the instances (mapping refinement). We specifically focus on the process of updating a mapping, but the methods could also be used to create a first-time mapping. We compare the performance of several state-of-the-art methods such as GPT-4 and fine-tuned BERT models on the mapping between ESCO and O*NET and ESCO and CompetentNL (the Dutch variant) for both ontology matching and mapping refinement. Our findings indicate that: 1) Match-BERT-GPT, an integration of BERT and GPT, performs best in ontology matching, while 2) TaSeR outperforms GPT-4, albeit marginally, in the task of mapping refinement. These results show that domain knowledge is still important in ontology alignment, especially in the updating of a mapping in our use cases in the labor domain.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"39 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120447","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31280
Kanad Pardeshi, Itai Shapira, Ariel D. Procaccia, Aarti Singh
Decision-making and reaching consensus are an integral part of everyday life, and studying how individuals reach these decisions is an important problem in psychology, economics, and social choice theory. Our work develops methods and theory for learning the nature of decisions reached upon by individual decision makers or groups of individuals using data. We consider two tasks, where we have access to data on: 1) Cardinal utilities for d individuals with cardinal consensus values that the group or decision maker arrives at, 2) Cardinal utilities for d individuals for pairs of actions, with ordinal information about the consensus, i.e., which action is better according to the consensus. Under some axioms of social choice theory, the set of possible decision functions reduces to the set of weighted power means, M(u, w, p) = (∑ᵢ₌₁ᵈ wᵢ uᵢᵖ)¹ᐟᵖ, where uᵢ indicate the d utilities, w ∈ ∆_{d - 1} denotes the weights assigned to the d individuals, and p ∈ ℝ (Cousins 2023). For instance, p = 1 corresponds to a weighted utilitiarian function, and p = -∞ is the egalitarian welfare function. Our goal is to learn w ∈ ∆_{d - 1} and p ∈ ℝ for the two tasks given data. The first task is analogous to regression, and we show that owing to the monotonicity in w and p (Qi 2000}, learning these parameters given cardinal utilities and social welfare values is a PAC-learnable task. For the second task, we wish to learn w, p such that, given pairs of actions u, v ∈ ℝ₊ᵈ, the preference is given as C((u, v), w, p) = sign(ln(M(u, w, p)) - ln(M(v, w, p))). This is analogous to classification; however, convexity of the loss function in w and p is not guaranteed. We analyze two related cases - one in which the weights w are known, and another in which the weights are unknown. We prove that both cases are PAC-learnable given positive u, v by giving an O(log d) bound on the VC dimension for the known weights case, and an O(d log d) bound for the unknown weights case. We also establish PAC-learnability for noisy data under IID (Natarajan 2013) and logistic noise models for this task. Finally, we demonstrate how simple algorithms can be useful to learn w and p up to moderately high d through experiments on simulated data.
决策和达成共识是日常生活中不可或缺的一部分，而研究个人如何达成这些决策是心理学、经济学和社会选择理论中的一个重要问题。我们的工作开发了利用数据学习个体决策者或群体决策性质的方法和理论。我们考虑了两个任务，在这两个任务中，我们可以获得以下数据：1) d 个个体的基本效用，以及群体或决策者达成的基本共识值；2) d 个个体对行动的基本效用，以及关于共识的序数信息，即根据共识哪种行动更好。根据社会选择理论的一些公理，可能的决策函数集合可以简化为加权平均值集合，M(u, w, p) = (∑ᵢ₌₁ᵈ wᵢ uᵢᵖ)¹ᐟᵖ、其中，uᵢ 表示 d 个效用，w ∈ ∆_{d - 1} 表示分配给 d 个个体的权重，p ∈ ℝ （Cousins，2023 年）。例如，p = 1 对应于加权功利主义函数，而 p = -∞ 则是平等主义福利函数。我们的目标是针对给定数据的两项任务，学习 w∈ ∆_{d - 1} 和 p∈ ℝ。第一个任务类似于回归，我们将证明由于 w 和 p 的单调性（Qi 2000}，在给定心效用和社会福利值的情况下学习这些参数是一个 PAC 可学习的任务。对于第二项任务，我们希望学习 w、p，以便在给定一对行动 u、v ∈ℝ₊ᵈ的情况下，偏好值为 C((u, v), w, p) = sign(ln(M(u, w, p)) - ln(M(v, w, p))。这类似于分类；但是，在 w 和 p 中损失函数的凸性得不到保证。我们分析了两种相关情况--一种是权重 w 已知，另一种是权重未知。通过给出已知权重情况下 VC 维度的 O(log d) 约束和未知权重情况下 VC 维度的 O(d log d) 约束，我们证明了这两种情况在给定正 u、v 时都是可 PAC 学习的。我们还建立了在 IID（Natarajan，2013 年）和逻辑噪声模型下该任务的高噪声数据的 PAC 可学习性。最后，我们通过对模拟数据的实验，展示了简单算法如何有助于学习 w 和 p，直至达到中等高度的 d。
{"title":"Learning Decision-Making Functions Given Cardinal and Ordinal Consensus Data","authors":"Kanad Pardeshi, Itai Shapira, Ariel D. Procaccia, Aarti Singh","doi":"10.1609/aaaiss.v3i1.31280","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31280","url":null,"abstract":"Decision-making and reaching consensus are an integral part of everyday life, and studying how individuals reach these decisions is an important problem in psychology, economics, and social choice theory. Our work develops methods and theory for learning the nature of decisions reached upon by individual decision makers or groups of individuals using data. \u0000\u0000We consider two tasks, where we have access to data on: 1) Cardinal utilities for d individuals with cardinal consensus values that the group or decision maker arrives at, 2) Cardinal utilities for d individuals for pairs of actions, with ordinal information about the consensus, i.e., which action is better according to the consensus. Under some axioms of social choice theory, the set of possible decision functions reduces to the set of weighted power means, M(u, w, p) = (∑ᵢ₌₁ᵈ wᵢ uᵢᵖ)¹ᐟᵖ, where uᵢ indicate the d utilities, w ∈ ∆_{d - 1} denotes the weights assigned to the d individuals, and p ∈ ℝ (Cousins 2023). For instance, p = 1 corresponds to a weighted utilitiarian function, and p = -∞ is the egalitarian welfare function. \u0000\u0000Our goal is to learn w ∈ ∆_{d - 1} and p ∈ ℝ for the two tasks given data. The first task is analogous to regression, and we show that owing to the monotonicity in w and p (Qi 2000}, learning these parameters given cardinal utilities and social welfare values is a PAC-learnable task. For the second task, we wish to learn w, p such that, given pairs of actions u, v ∈ ℝ₊ᵈ, the preference is given as C((u, v), w, p) = sign(ln(M(u, w, p)) - ln(M(v, w, p))). This is analogous to classification; however, convexity of the loss function in w and p is not guaranteed. \u0000\u0000We analyze two related cases - one in which the weights w are known, and another in which the weights are unknown. We prove that both cases are PAC-learnable given positive u, v by giving an O(log d) bound on the VC dimension for the known weights case, and an O(d log d) bound for the unknown weights case. We also establish PAC-learnability for noisy data under IID (Natarajan 2013) and logistic noise models for this task. Finally, we demonstrate how simple algorithms can be useful to learn w and p up to moderately high d through experiments on simulated data.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"34 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120560","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}
Pub Date : 2024-05-20DOI: 10.1609/aaaiss.v3i1.31246
Daiki Shintani, Iko Nakari, Satomi Washizaki, K. Takadama
This paper proposes the method by physiological knowledge to improve the estimation performance of the NREM3 sleep based on the waist-attached accelerometer. Specifically, this paper proposes the hybrid method that combines the method based on body movement counts and the method based on biological rhythms of sleep. Through the human subject experiment, the following implications were revealed: (1) the proposed method can outperform famous machine learning models (Random Forest and LSTM) trained with automatically generated features that do not sufficiently incorporate domain knowledge; (2) when the input features are based on domain knowledge, the estimator explicitly designed by humans can outperform the machine learning method; and (3) combining the body movement counting method and the biological rhythm-based method can suppress the error of the body movement counting method and reduce false positives.
{"title":"NREM3 Sleep Stage Estimation Based on Accelerometer by Body Movement Count and Biological Rhythms","authors":"Daiki Shintani, Iko Nakari, Satomi Washizaki, K. Takadama","doi":"10.1609/aaaiss.v3i1.31246","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31246","url":null,"abstract":"This paper proposes the method by physiological knowledge to improve the estimation performance of the NREM3 sleep based on the waist-attached accelerometer. Specifically, this paper proposes the hybrid method that combines the method based on body movement counts and the method based on biological rhythms of sleep. Through the human subject experiment, the following implications were revealed: (1) the proposed method can outperform famous machine learning models (Random Forest and LSTM) trained with automatically generated features that do not sufficiently incorporate domain knowledge; (2) when the input features are based on domain knowledge, the estimator explicitly designed by humans can outperform the machine learning method; and (3) combining the body movement counting method and the biological rhythm-based method can suppress the error of the body movement counting method and reduce false positives.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"33 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120723","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}
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