Annals of Mathematics and Artificial Intelligence最新文献_第2页

Domain independent heuristics for online stochastic contingent planning 在线随机应急规划的领域独立启发式方法

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-07-08 DOI: 10.1007/s10472-024-09947-5

Oded Blumenthal, Guy Shani

引用次数: 0

Solving morphological analogies: from retrieval to generation 解决形态类比问题：从检索到生成

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-06-11 DOI: 10.1007/s10472-024-09945-7

Esteban Marquer, Miguel Couceiro

{"title":"Solving morphological analogies: from retrieval to generation","authors":"Esteban Marquer, Miguel Couceiro","doi":"10.1007/s10472-024-09945-7","DOIUrl":"https://doi.org/10.1007/s10472-024-09945-7","url":null,"abstract":"<p>Analogical inference is a remarkable capability of human reasoning, and has been used to solve hard reasoning tasks. Analogy based reasoning (AR) has gained increasing interest from the artificial intelligence community and has shown its potential in multiple machine learning tasks such as classification, decision making and recommendation with competitive results. We propose a deep learning (DL) framework to address and tackle two key tasks in AR: analogy detection and solving. The framework is thoroughly tested on the Siganalogies dataset of morphological analogical proportions (APs) between words, and shown to outperform symbolic approaches in many languages. Previous work have explored the behavior of the Analogy Neural Network for classification (ANNc) on analogy detection and of the Analogy Neural Network for retrieval (ANNr) on analogy solving by retrieval, as well as the potential of an autoencoder (AE) for analogy solving by generating the solution word. In this article we summarize these findings and we extend them by combining ANNr and the AE embedding model, and checking the performance of ANNc as an retrieval method. The combination of ANNr and AE outperforms the other approaches in almost all cases, and ANNc as a retrieval method achieves competitive or better performance than 3CosMul. We conclude with general guidelines on using our framework to tackle APs with DL.</p>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"33 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introduction to the special issue: selected papers from EMAS 2022 特刊导言：《2022 年教育管理和服务计划》论文选

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-06-10 DOI: 10.1007/s10472-024-09946-6

Amit K. Chopra, Jürgen Dix, Rym Zalila-Wenkstern

引用次数: 0

Optimizing doubly stochastic matrices for average consensus through swarm and evolutionary algorithms 通过蜂群算法和进化算法优化双随机矩阵以达成平均共识

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-06-08 DOI: 10.1007/s10472-023-09912-8

Panos K. Syriopoulos, Konstantinos I. Chatzilygeroudis, Nektarios G. Kalampalikis, Michael N. Vrahatis

{"title":"Optimizing doubly stochastic matrices for average consensus through swarm and evolutionary algorithms","authors":"Panos K. Syriopoulos, Konstantinos I. Chatzilygeroudis, Nektarios G. Kalampalikis, Michael N. Vrahatis","doi":"10.1007/s10472-023-09912-8","DOIUrl":"10.1007/s10472-023-09912-8","url":null,"abstract":"<div><p>Doubly-stochastic matrices play a vital role in modern applications of complex networks such as tracking and decentralized state estimation, coordination and control of autonomous agents. A central theme in all of the above is consensus, that is, nodes reaching agreement about the value of an underlying variable (e.g. the state of the environment). Despite the fact that complex networks have been studied thoroughly, the communication graphs are usually described by symmetric matrices due to their advantageous theoretical properties. We do not yet have methods for optimizing generic doubly-stochastic matrices. In this paper, we propose a novel formulation and framework, EvoDSM, for achieving fast linear distributed averaging by: (a) optimizing the weights of a fixed graph topology, and (b) optimizing for the topology itself. We are concerned with graphs that can be described by positive doubly-stochastic matrices. Our method relies on swarm and evolutionary optimization algorithms and our experimental results and analysis showcase that our method (1) achieves comparable performance with traditional methods for symmetric graphs, (2) is applicable to non-symmetric network structures and edge weights, and (3) is scalable and can operate effectively with moderately large graphs without engineering overhead.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"93 1","pages":"151 - 171"},"PeriodicalIF":1.2,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An extended knowledge compilation map for conditional preference statements-based and generalized additive utilities-based languages 基于条件偏好语句和广义累加功用语言的扩展知识编译图谱

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-06-03 DOI: 10.1007/s10472-024-09935-9

Hélène Fargier, Stefan Mengel, Jérôme Mengin

引用次数: 0

Knowledge compilation 知识汇编

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-05-17 DOI: 10.1007/s10472-024-09942-w

Adnan Darwiche, Pierre Marquis

引用次数: 0

A study of universal morphological analysis using morpheme-based, holistic, and neural approaches under various data size conditions 在不同数据规模条件下使用基于语素、整体和神经方法进行通用形态分析的研究

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-05-11 DOI: 10.1007/s10472-024-09944-8

Rashel Fam, Yves Lepage

{"title":"A study of universal morphological analysis using morpheme-based, holistic, and neural approaches under various data size conditions","authors":"Rashel Fam, Yves Lepage","doi":"10.1007/s10472-024-09944-8","DOIUrl":"https://doi.org/10.1007/s10472-024-09944-8","url":null,"abstract":"<p>We perform a study on the universal morphological analysis task: given a word form, generate the lemma (lemmatisation) and its corresponding morphosyntactic descriptions (MSD analysis). Experiments are carried out on the SIGMORPHON 2018 Shared Task: Morphological Reinflection Task dataset which consists of more than 100 different languages with various morphological richness under three different data size conditions: low, medium and high. We consider three main approaches: morpheme-based (eager learning), holistic (lazy learning), and neural (eager learning). Performance is evaluated on the two subtasks of lemmatisation and MSD analysis. For the lemmatisation subtask, under all three data sizes, experimental results show that the holistic approach predicted more accurate lemmata, while the morpheme-based approach produced lemmata closer to the answers when it produces the wrong answers. For the MSD analysis subtask, under all three data sizes, the holistic approach achieves higher recall, while the morpheme-based approach is more precise. However, the trade-off between precision and recall of the two systems leads to a very similar overall F1 score. On the whole, neural approaches suffer under low resource conditions, but they achieve the best performance in comparison to the other approaches when the size of the training data increases.</p>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"42 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep data density estimation through Donsker-Varadhan representation 通过 Donsker-Varadhan 表示进行深度数据密度估算

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-04-26 DOI: 10.1007/s10472-024-09943-9

Seonho Park, Panos M. Pardalos

引用次数: 0

Signifiers for conveying and exploiting affordances: from human-computer interaction to multi-agent systems 传递和利用负担能力的符号：从人机交互到多代理系统

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-04-17 DOI: 10.1007/s10472-024-09938-6

Jérémy Lemée, Danai Vachtsevanou, Simon Mayer, Andrei Ciortea

{"title":"Signifiers for conveying and exploiting affordances: from human-computer interaction to multi-agent systems","authors":"Jérémy Lemée, Danai Vachtsevanou, Simon Mayer, Andrei Ciortea","doi":"10.1007/s10472-024-09938-6","DOIUrl":"10.1007/s10472-024-09938-6","url":null,"abstract":"<div><p>The ecological psychologist James J. Gibson defined the notion of affordances to refer to what action possibilities environments offer to animals. In this paper, we show how (artificial) agents can discover and exploit affordances in a Multi-Agent System (MAS) environment to achieve their goals. To indicate to agents what affordances are present in their environment and whether it is likely that these may help the agents to achieve their objectives, the environment may expose signifiers while taking into account the current situation of the environment and of the agent. On this basis, we define a Signifier Exposure Mechanism that is used by the environment to compute which signifiers should be exposed to agents in order to permit agents to only perceive information about affordances that are likely to be relevant to them, and thereby increase their interaction efficiency. If this is successful, agents can interact with partially observable environments more efficiently because the signifiers indicate the affordances they can exploit towards given purposes. Signifiers thereby facilitate the exploration and the exploitation of MAS environments. Implementations of signifiers and of the Signifier Exposure Mechanism are presented within the context of a Hypermedia Multi-Agent System, and the utility of this approach is presented through the development of a scenario.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 4","pages":"815 - 835"},"PeriodicalIF":1.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-024-09938-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

引用次数: 0

Near-term advances in quantum natural language processing 量子自然语言处理的近期进展

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-04-11 DOI: 10.1007/s10472-024-09940-y

Dominic Widdows, Aaranya Alexander, Daiwei Zhu, Chase Zimmerman, Arunava Majumder

{"title":"Near-term advances in quantum natural language processing","authors":"Dominic Widdows, Aaranya Alexander, Daiwei Zhu, Chase Zimmerman, Arunava Majumder","doi":"10.1007/s10472-024-09940-y","DOIUrl":"10.1007/s10472-024-09940-y","url":null,"abstract":"<div><p>This paper describes experiments showing that some tasks in natural language processing (NLP) can already be performed using quantum computers, though so far only with small datasets. We demonstrate various approaches to topic classification. The first uses an explicit word-based approach, in which word-topic weights are implemented as fractional rotations of individual qubits, and a phrase is classified based on the accumulation of these weights onto a scoring qubit, using entangling quantum gates. This is compared with more scalable quantum encodings of word embedding vectors, which are used to compute kernel values in a quantum support vector machine: this approach achieved an average of 62% accuracy on classification tasks involving over 10000 words, which is the largest such quantum computing experiment to date. We describe a quantum probability approach to bigram modeling that can be applied to understand sequences of words and formal concepts, investigate a generative approximation to these distributions using a quantum circuit Born machine, and introduce an approach to ambiguity resolution in verb-noun composition using single-qubit rotations for simple nouns and 2-qubit entangling gates for simple verbs. The smaller systems presented have been run successfully on physical quantum computers, and the larger ones have been simulated. We show that statistically meaningful results can be obtained, but the quality of individual results varies much more using real datasets than using artificial language examples from previous quantum NLP research. Related NLP research is compared, partly with respect to contemporary challenges including informal language, fluency, and truthfulness.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 5","pages":"1249 - 1272"},"PeriodicalIF":1.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0