Annals of Mathematics and Artificial Intelligence最新文献_第10页

Automated triangle constructions in hyperbolic geometry 双曲几何中的自动三角形构造

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-12 DOI: 10.1007/s10472-023-09850-5

Vesna Marinković, Tijana Šukilović, Filip Marić

引用次数: 0

A Bayesian reinforcement learning approach in markov games for computing near-optimal policies 计算近似最优策略的马尔可夫对策中的贝叶斯强化学习方法

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-10 DOI: 10.1007/s10472-023-09860-3

Julio B. Clempner

{"title":"A Bayesian reinforcement learning approach in markov games for computing near-optimal policies","authors":"Julio B. Clempner","doi":"10.1007/s10472-023-09860-3","DOIUrl":"10.1007/s10472-023-09860-3","url":null,"abstract":"<div><p>Bayesian Learning is an inference method designed to tackle exploration-exploitation trade-off as a function of the uncertainty of a given probability model from observations within the Reinforcement Learning (RL) paradigm. It allows the incorporation of prior knowledge, as probabilistic distributions, into the algorithms. Finding the resulting Bayes-optimal policies is notorious problem. We focus our attention on RL of a special kind of ergodic and controllable Markov games. We propose a new framework for computing the near-optimal policies for each agent, where it is assumed that the Markov chains are regular and the inverse of the behavior strategy is well defined. A fundamental result of this paper is the development of a theoretical method that, based on the formulation of a non-linear problem, computes the near-optimal adaptive-behavior strategies and policies of the game under some restrictions that maximize the expected reward. We prove that such behavior strategies and the policies satisfy the Bayesian-Nash equilibrium. Another important result is that the RL process learn a model through the interaction of the agents with the environment, and shows how the proposed method can finitely approximate and estimate the elements of the transition matrices and utilities maintaining an efficient long-term learning performance measure. We develop the algorithm for implementing this model. A numerical empirical example shows how to deploy the estimation process as a function of agent experiences.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"91 5","pages":"675 - 690"},"PeriodicalIF":1.2,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-023-09860-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48089164","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

Quantifying the relationship between software design principles and performance in Jason: a case study with simulated mobile robots 在Jason中量化软件设计原则和性能之间的关系:模拟移动机器人的案例研究

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-08 DOI: 10.1007/s10472-023-09844-3

Patrick Gavigan, Babak Esfandiari

引用次数: 0

Anomaly detection with superexperts under delayed feedback 基于延迟反馈的超专家异常检测

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-05 DOI: 10.1007/s10472-023-09845-2

Raisa Dzhamtyrova, C. Maple

引用次数: 0

Conformal Predictive Distribution Trees 保形预测分布树

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-03 DOI: 10.1007/s10472-023-09847-0

U. Johansson, Tuwe Löfström, Henrik Boström

引用次数: 0

Correction to: Preface 更正:前言

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-25 DOI: 10.1007/s10472-023-09870-1

Adel Bouhoula, Bruno Buchberger, Tetsuo Ida, Temur Kutsia

引用次数: 0

Towards a geometry deductive database prover 一个几何演绎数据库证明器

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-24 DOI: 10.1007/s10472-023-09839-0

Nuno Baeta, Pedro Quaresma

{"title":"Towards a geometry deductive database prover","authors":"Nuno Baeta, Pedro Quaresma","doi":"10.1007/s10472-023-09839-0","DOIUrl":"10.1007/s10472-023-09839-0","url":null,"abstract":"<div><p>The Geometry Automated-Theorem-Provers (GATP) based on the deductive database method use a data-based search strategy to improve the efficiency of forward chaining. An implementation of such a method is expected to be able to efficiently prove a large set of geometric conjectures, producing readable proofs. The number of conjectures a given implementation can prove will depend on the set of inference rules chosen, the deductive database method is not a decision procedure. Using an approach based in an <i>SQL</i> database library and using an in-memory database, the implementation described in this paper tries to achieve the following goals. Efficiency in the management of the inference rules, the set of already known facts and the new facts discovered, by the use of the efficient data manipulation techniques of the <i>SQL</i> library. Flexibility, by transforming the inference rules in <i>SQL</i> data manipulation language queries, will open the possibility of meta-development of GATP based on a provided set of rules. Natural language and visual renderings, possible by the use of a synthetic forward chaining method. Implemented as an open source library, that will open its use by third-party programs, e.g. the dynamic geometry systems.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"91 6","pages":"851 - 863"},"PeriodicalIF":1.2,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-023-09839-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44899834","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

Logical aspects of multi-agent systems 多代理系统的逻辑方面

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-19 DOI: 10.1007/s10472-023-09852-3

Bastien Maubert, Aniello Murano, Sasha Rubin

引用次数: 0

Correction to: Preface: Selected revised papers from the LION 15 conference 更正:前言:选自LION 15会议的修订论文

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-17 DOI: 10.1007/s10472-023-09855-0

Ilias S. Kotsireas, Panos M. Pardalos, Dimitris E. Simos

引用次数: 0

A program to create new geometry proof problems 创建新的几何证明问题的程序

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-16 DOI: 10.1007/s10472-023-09854-1

Philip Todd, Danny Aley

{"title":"A program to create new geometry proof problems","authors":"Philip Todd, Danny Aley","doi":"10.1007/s10472-023-09854-1","DOIUrl":"10.1007/s10472-023-09854-1","url":null,"abstract":"<div><p>In a previous paper Todd (Submitted to AMAI, 2022), linear systems corresponding to sets of angle bisector conditions are analyzed. In a system which is not full rank, one bisector condition can be derived from the others. In that paper, we describe methods for finding such rank deficient linear systems. The vector angle bisector relationship may be interpreted geometrically in a number of ways: as an angle bisector, as a reflection, as an isosceles triangle, or as a circle chord. A rank deficient linear system may be interpreted as a geometry theorem by mapping each vector angle bisector relationship onto one of these geometrical representations. In Todd (Submitted to AMAI, 2022) we illustrate the step from linear system to geometry theorem with a number of by-hand constructed examples. In this paper, we present an algorithm which automatically generates a geometry theorem from a starting point of a linear system of the type identified in Todd (Submitted to AMAI, 2022). Both statement and diagram of the new theorem are generated by the algorithm. Our implementation creates a simple text description of the new theorem and utilizes the Mathematica GeometricScene to form a diagram.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"91 6","pages":"779 - 795"},"PeriodicalIF":1.2,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49469437","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