检索结果-内蒙古大学图书馆

Nongovernance rather than governance in a multiagent economic society

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2001年第5期5卷 535-545页

作者： Takadama, K Terano, T Shimohara, K Adv Telecommun Res Inst Int Kyoto 6190288 Japan Univ Tsukuba Grad Sch Syst Management Tokyo 1120012 Japan

This paper explores how to achieve goals at the macro level without controlling self-interested economic agents at the micro level and investigates the effectiveness of our claim suggesting that we make use of properties arising from interaction among economic agents to address the above issue. Intensive experiments on a complex domain problem have found the following implications: 1) as an institution design, it is important not to control economic agents at the micro level, but to promote them to self-activate in order to achieve goals at the macro level and 2) as a role of an administrative party like a government, it is important to have a clear view to determine which results are good because the timing for finding such results depends on the environmental situation and there is no guarantee that these results will converge. Other implications are summarized as follows: 1) it is important to remove evaluation level intervention to find good results, while it is important to introduce this intervention to reduce costs and 2) behavior intervention does not contribute to finding good results nor reducing costs.

关键词： agent-based computational economics (ACE) emergent behavior intervention for control learning classifier system multiagent economic society

来源：评论

学校读者我要写书评

暂无评论

How Should learning classifier systems Cover A State-Action Space?

How Should Learning Classifier Systems Cover A State-Action ...

引用

IEEE Congress on Evolutionary Computation

作者： Masaya Nakata Pier Luca Lanzi Tim Kovacs Will Neil Browne Keiki Takadama The University of Electo-Communications Tokyo Japan Politecnico di Milano Milano Italy University of Bristol Bristol United Kingdom Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781479974931

A learning strategy in learning classifier systems (LCSs) defines how classifiers cover a state-action space in a problem. Previous analyses in classification problems have empirically claimed an adequate learning strategy can be decided depending on the types of noise in the problem. This issue is still arguable from two aspects. First, there lacks comparison of learning strategies in reinforcement learning problems with different types of noise. Second, when we can claim so, a further issue is how should classifiers cover the state-action space in order to improve the stability of LCS performance on as many types of noise as possible? This paper first attempts to empirically conclude these issues on a version of LCSs (i.e., the XCS classifier system). That is, we present a new concept of learning strategy for LCSs, and complement that claim by comparing it with the existing learning strategies on a reinforcement learning problem. Our learning strategy covers all state-action pairs but assigns more classifiers to the highest-return action at each state than other actions. Our results support that claim that existing learning strategies have dependencies on the types of noise in reinforcement learning problems. However, our learning strategy improves the stability of XCS performance compared with the existing strategies on all types of noise employed in this paper.

关键词： learning classifier system XCS complete action map best action map reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

For real! XCS with continuous-valued inputs

引用

EVOLUTIONARY COMPUTATION 2003年第3期11卷 299-336页

作者： Stone, C Bull, L Univ W England Fac Comp Engn & Math Sci Bristol BS16 1QY Avon England

Many real-world problems are not conveniently expressed using the ternary representation typically used by learning classifier systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other learning classifier system architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for learning classifier systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared using both the real multiplexer and checkerboard problems and we find that representational, Operator and sampling bias all affect the performance of XCS in continuous-valued environments.

关键词： checkerboard problem continuous-valued input interval representation learning classifier system real multiplexer problem operator bias real input representational bias sampling bias XCS

来源：评论

学校读者我要写书评

暂无评论

Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study

引用

ARTIFICIAL LIFE 2023年第4期29卷 468-486页

作者： Heider, Michael Stegherr, Helena Nordsieck, Richard Haehner, Joerg Univ Augsburg Organ Comp Grp Augsburg Germany Xitaso GmbH IT & Software Solut Augsburg Germany

In sociotechnical settings, human operators are increasingly assisted by decision support systems. By employing such systems, important properties of sociotechnical systems, such as self-adaptation and self-optimization, are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanations regarding the reasoning behind specific decisions. In this article, we propose the use of learning classifier systems (LCSs), a family of rule-based machine learning methods, to facilitate and highlight techniques to improve transparent decision-making. Furthermore, we present a novel approach to assessing application-specific explainability needs for the design of LCS models. For this, we propose an application-independent template of seven questions. We demonstrate the approach's use in an interview-based case study for a manufacturing scenario. We find that the answers received do yield useful insights for a well-designed LCS model and requirements for stakeholders to engage actively with an intelligent agent.

关键词： Rule-based learning self-explaining decision support sociotechnical system learning classifier system explainable AI

来源：评论

学校读者我要写书评

暂无评论

Comparing extended classifier system and genetic programming for financial forecasting: an empirical study

引用

SOFT COMPUTING 2007年第12期11卷 1173-1183页

作者： Chen, Mu-Yen Chen, Kuang-Ku Chiang, Heien-Kun Huang, Hwa-Shan Huang, Mu-Jung Natl Changhua Univ Educ Dept Accounting Changhua 50058 Taiwan Natl Changhua Univ Educ Dept Informat Management Changhua 50058 Taiwan

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules;however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population;(2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP.

关键词： learning classifier system extended classifier system genetic programming machine learning

来源：评论

学校读者我要写书评

暂无评论

Strategy of global asset allocation using extended classifier system

引用

EXPERT systemS WITH APPLICATIONS 2010年第9期37卷 6611-6617页

作者： Tsai, Wen-Chih Chen, An-Pin Natl Chiao Tung Univ Inst Informat Management Hsinchu 30050 Taiwan

There are several studies about extended classification system (XCS) in past years. XCS model can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation to apply XCS to global asset allocation in the country-specific exchanged traded funds (ETFs). Since international stock price trend is influenced by unknown and unpredictable surroundings, using XCS to model the fluctuations on global financial market allows for the discovery of the patterns of the future trends. As such, the benefits of international asset diversification can be achieved in a tax-efficient way with country-specific ETFs at a low transaction cost with minimized tracking error. These empirical results indicate that XCS is capable of evolving over time, and thus XCS can provide a good indicator for future global asset allocation decision-making aiming at maximized profit. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Extended classification system learning classifier system Exchanged traded funds Finance predication

来源：评论

学校读者我要写书评

暂无评论

Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach

引用

EXPERT systemS WITH APPLICATIONS 2018年 114卷 374-387页

作者： Li, Xianneng Yang, Meihua Wu, Shizhe Dalian Univ Technol Fac Management & Econ 2 Linggong Rd Dalian 116024 Peoples R China

As one of the most important research branches of evolutionary computation (EC), learning classifier system (LCS) is dedicated to discover decision making classifiers ("IF-THEN" type rules) via evolution and learning. Recent advances in LCS have shown distinguished generalization property over traditional approaches. In this paper, a novel LCS named niching genetic network programming with rule accumulation (nGNP-RA) is proposed. The unique features of the proposal arise from the following three points: First, it utilizes an advanced graph-based EC named GNP as the rule generator, resulting higher knowledge representation ability than traditional genetic algorithm (GA)-based LCSs;Second, a novel niching mechanism is developed in GNP to encourage the discovery of high-quality diverse rules;Third, a novel reinforcement learning (RL)-based mechanism is embedded to assign accurate credits to the discovered rules. To verify the effectiveness and robustness of nGNP-RA over traditional systems, two decision making testbeds are applied, including the benchmark tileworld problem and the real mobile robot control application. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Decision making Genetic network programming learning classifier system Niching Reinforcement learning Rule accumulation

来源：评论

学校读者我要写书评

暂无评论

Rule reduction by selection strategy in XCS with adaptive action map

引用

EVOLUTIONARY INTELLIGENCE 2015年第2-3期8卷 71-87页

作者： Nakata, Masaya Lanzi, Pier Luca Takadama, Keiki Univ Elect Commun Grad Sch Informat Chofu Tokyo 1820021 Japan Japan Soc Promot Sci Chiyoda Ku Tokyo Japan Politecn Milan Dept Elect & Informat I-20133 Milan Italy

The XCS classifier system is a rule-based evolutionary machine learning system. XCS evolves classifiers in order to learn generalized solutions. The XCS with adaptive action mapping (XCSAM) is inherited from XCS, which evolves a best action map where it evolves classifiers that advocate the best action in every state. Accordingly, XCSAM can potentially evolve solutions that are more compact than XCS, which in contrast focuses on a complete action map. Previous experimental results however have shown that, in some problems, XCSAM may produce solutions with more classifiers than XCS. In this paper, we initially show that the original fitness-based selection strategy of XCS produces non effective classifiers which are not likely to be in the best action map (i.e., they are inaccurate ones or do not have best actions) in XCSAM. Then, we introduce a new selection strategy for XCSAM that promotes the evolution of classifiers advocating the best action map and thus produces more compact solutions. The new strategy selects classifiers based both on their fitness (like XCS) and on the parameter optimality of action of XCSAM. The result is a pressure towards classifiers that are accurate and advocate the best actions. We present analyses showing that the new selection strategy successfully enables XCSAM to focus on classifiers having best actions. Our experimental results show that XCSAM with the new selection strategy (called XCSAM-SS) can evolve smaller solutions than XCS (and the original XCSAM) both in single-step and multi-step problems. As a consequence, XCSAM can also learn with smaller iterations than XCS in the single-step problem. Our conclusion is that, as the best action map potentially has a compact solution, XCSAM evolves a much compact solution than XCS by adding an adequate selection strategy.

关键词： learning classifier system XCS XCSAM Rule-reduction

来源：评论

学校读者我要写书评

暂无评论

Approach to Clustering with Variance-Based XCS

引用

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第5期21卷 885-894页

作者： Zhang, Caili Tatsumi, Takato Nakata, Masaya Takadama, Keiki Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan Yokohama Natl Univ Hodogaya Ku 79-1 Tokiwadai Yokohama Kanagawa Japan

This paper presents an approach to clustering that extends the variance-based learning classifier system (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a benchmark problem to examine whether XCS-VRc can cluster both the generalized and more generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCS-VR.

关键词： machine learning learning classifier system

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Rule Deletion Scheme in XCS on Reinforcement learning Problem

引用

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第5期21卷 876-884页

作者： Nakata, Masaya Hamagami, Tomoki Yokohama Natl Univ Hodogaya Ku 79-5 Tokiwadai Yokohama Kanagawa Japan

The XCS classifier system is an evolutionary rule-based learning technique powered by a Q-learning like learning mechanism. It employs a global deletion scheme to delete rules from all rules covering all state-action pairs. However, the optimality of this scheme remains unclear owing to the lack of intensive analysis. We here introduce two deletion schemes: 1) local deletion, which can be applied to a subset of rules covering each state (a match set), and 2) stronger local deletion, which can be applied to a more specific subset covering each state-action pair (an action set). The aim of this paper is to reveal how the above three deletion schemes affect the performance of XCS. Our analysis shows that the local deletion schemes promote the elimination of inaccurate rules compared with the global deletion scheme. However, the stronger local deletion scheme occasionally deletes a good rule. We further show that the two local deletion schemes greatly improve the performance of XCS on a set of noisy maze problems. Although the localization strength of the proposed deletion schemes may require consideration, they can be adequate for XCS rather than the original global deletion scheme.

关键词： learning classifier system evolutionary reinforcement learning deletion scheme

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：