An Agent Based Framework for Multiple, Heterogeneous Case Based Reasoning

2013 ◽  
pp. 314-328 ◽  
Author(s):  
Elena Irena Teodorescu ◽  
Miltos Petridis
Keyword(s):  
Case Based Reasoning ◽  
Agent Based ◽  
Case Based

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

Hybrid Intelligence Agents Architecture Design for Product Return Administration

2011 ◽  
Vol 403-408 ◽  
pp. 3339-3343
Author(s):  
Chen Shu Wang
Keyword(s):  
Intelligent Agents ◽  
Case Based Reasoning ◽  
End User ◽  
Improve Performance ◽  
Information Transparency ◽  
Agent Based ◽  
Intelligent Algorithms ◽  
Product Return ◽  
Hybrid Intelligence ◽  
Case Based

Return is a critical but controversial issue. To deal with such a vague return problem, business must improve information transparency about end users’ return activities. This research proposed an agent-based architecture for return administration. The intelligent return administration expert system (iRAES) architecture consists of two KDD mechanisms and two intelligent agents that can predict the possibility of the end user will return the product (via return diagnosis agent, RDA) and provide return centre staff with recommendations for return administration (via return recommender agent, RRA). iRAES is implemented successfully and adopts hybrid artificial intelligent algorithms, including the following: data mining is employed to implement the RDA agent, and case-based reasoning is adopted to design the RRA agent. A demonstrated 3C-iShop scenario is presented to illustrate the feasibility and efficiency of iRAES architecture. As the experiment results show, iRAES can decrease the 70% effort for return administration evaluation and improve performance with return administration suggestions by 37%. Therefore, return administration and the knowledge management about product return can be accelerated via iRAES.

INCREASING THE AUTONOMY OF DELIBERATIVE AGENTS WITH A CASE-BASED REASONING SYSTEM

2003 ◽  
Vol 03 (01) ◽  
pp. 101-118 ◽  
Author(s):  
J. M. CORCHADO ◽  
R. LAZA ◽  
L. BORRAJO ◽  
J. C. YAÑES ◽  
M. VALIÑO
Keyword(s):  
World Wide Web ◽  
Computer Networks ◽  
World Wide ◽  
Case Based Reasoning ◽  
Agent Based ◽  
Advantages And Disadvantages ◽  
Reasoning System ◽  
The World ◽  
Case Based

This paper shows how deliberative agents can be built by means of a case-based reasoning system. The concept of deliberative agent is introduced and the case-based reasoning model is presented. Once the advantages and disadvantages of such agents have been discussed, it will be shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The World Wide Web has emerged as one of the most popular vehicle for disseminating and sharing information through computer networks; a distributed agent-based solution for e-business, in which such agents have been used, is also presented and evaluated in this paper.

Case-Based Reasoning and Agent Based Job Offer Recommender System

2018 ◽  
pp. 21-33 ◽  
Author(s):  
Alfonso González-Briones ◽  
Alberto Rivas ◽  
Pablo Chamoso ◽  
Roberto Casado-Vara ◽  
Juan Manuel Corchado
Keyword(s):  
Recommender System ◽  
Case Based Reasoning ◽  
Agent Based ◽  
Case Based
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