Multi-Agent, Multi-Case-Based Reasoning

2013 ◽  
pp. 74-88 ◽  
Author(s):  
Susan L. Epstein ◽  
Xi Yun ◽  
Lei Xie
Keyword(s):  
Case Based Reasoning ◽  
Multi Agent ◽  
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.

Planning Agent for Geriatric Residences

2011 ◽  
pp. 1316-1322 ◽  
Author(s):  
Javier Bajo ◽  
Dante I. Tapia ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Health Care ◽  
Radio Frequency Identification ◽  
Adaptive Systems ◽  
Autonomous Agent ◽  
Case Based Reasoning ◽  
Multi Agent System ◽  
Agent System ◽  
Learning And Adaptation ◽  
Multi Agent ◽  
Case Based

Agents and Multi-Agent Systems (MAS) have become increasingly relevant for developing distributed and dynamic intelligent environments. The ability of software agents to act somewhat autonomously links them with living animals and humans, so they seem appropriate for discussion under nature-inspired computing (Marrow, 2000). This paper presents AGALZ (Autonomous aGent for monitoring ALZheimer patients), and explains how this deliberative planning agent has been designed and implemented. A case study is then presented, with AGALZ working with complementary agents into a prototype environment-aware multi-agent system (ALZ-MAS: ALZheimer Multi-Agent System) (Bajo, Tapia, De Luis, Rodríguez & Corchado, 2007). The elderly health care problem is studied, and the possibilities of Radio Frequency Identification (RFID) (Sokymat, 2006) as a technology for constructing an intelligent environment and ascertaining patient location to generate plans and maximize safety are examined. This paper focuses in the development of natureinspired deliberative agents using a Case-Based Reasoning (CBR) (Aamodt & Plaza, 1994) architecture, as a way to implement sensitive and adaptive systems to improve assistance and health care support for elderly and people with disabilities, in particular with Alzheimer. Agents in this context must be able to respond to events, take the initiative according to their goals, communicate with other agents, interact with users, and make use of past experiences to find the best plans to achieve goals, so we propose the development of an autonomous deliberative agent that incorporates a Case-Based Planning (CBP) mechanism, derivative from Case-Based Reasoning (CBR) (Bajo, Corchado & Castillo, 2006), specially designed for planning construction. CBP-BDI facilitates learning and adaptation, and therefore a greater degree of autonomy than that found in pure BDI (Believe, Desire, Intention) architecture (Bratman, 1987). BDI agents can be implemented by using different tools, such as Jadex (Pokahr, Braubach & Lamersdorf, 2003), dealing with the concepts of beliefs, goals and plans, as java objects that can be created and handled within the agent at execution time.

A Multi-Agent System Approach to Mobile Negotiation Support Mechanism by Integrating Case-Based Reasoning and Fuzzy Cognitive Map

2011 ◽  
pp. 218-238
Author(s):  
Kun Chang Lee ◽  
Lee Namho
Keyword(s):  
Support System ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Case Based Reasoning ◽  
Negotiation Support ◽  
Support Mechanism ◽  
Multi Agent ◽  
Effective Decision Support ◽  
Primary Advantage ◽  
Case Based

This paper proposes a new type of multi-agent mobile negotiation support system named MAM-NSS in which both buyers and sellers are seeking for best deal given limited resources. Mobile commerce or m-commerce is now on the verge of explosion in many countries, triggering the need to develop more effective decision support system capable of suggesting timely and relevant action strategies for both buyers and sellers. To fulfill research purpose like this, two AI methods such as CBR (case-based reasoning) and FCM (fuzzy cognitive map) are integrated, and named MAM-NSS. Primary advantage of the proposed approach is that those decision makers involved in m-commerce regardless of buyers and sellers can benefit from the negotiation support functions that are derived from referring to past instances via CBR and investigating interrelated factors simultaneously through FCM. To prove the validity of the proposed approach, a hypothetical m-commerce problem is developed in which theaters (sellers) seek to maximize profit by selling its vacant seats to potential customers (buyers) walking around within reasonable distance. For experimental design and implementation, a multi-agent environment Netlogo is adopted. Simulation reveals that the proposed MAM-NSS could produce more robust and promising results that fit the characteristics of m-commerce.

scholarly journals Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons

2016 ◽  
Vol 231 (2) ◽  
pp. 110-124 ◽  
Author(s):  
Julien Henriet
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Ordered Set ◽  
Case Based Reasoning ◽  
Adaptation Process ◽  
Multi Agent System ◽  
Multi Agent ◽  
Different Parts ◽  
Case Based ◽  
Underlying Theme

AI-Virtual Trainer is an educative system using Artificial Intelligence to propose varied lessons to trainers. The agents of this multi-agent system apply case-based reasoning to build solutions by analogy. However, as required by the field, Artificial Intelligence-Virtual Trainer never proposes the same lesson twice, whereas the same objective may be set many times consecutively. The adaptation process of Artificial Intelligence-Virtual Trainer delivers an ordered set of exercises adapted to the objectives and sub-objectives chosen by trainers. This process has been enriched by including the notion of distance between exercises: the proposed tasks are not only appropriate but are hierarchically ordered. With this new version of the system, students are guided towards their objectives via an underlying theme. Finally, the agents responsible for the different parts of lessons collaborate with each other according to a dedicated protocol and decision-making policy since no exercise must appear more than once in the same lesson. The results prove that Artificial Intelligence-Virtual Trainer, however perfectible, meets the requirements of this field.

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