Dynamic control for safety system multi-agent system with case-based reasoning

2017 ◽  
Vol 11 (3/4) ◽  
pp. 238 ◽  
Author(s):  
Nassima Aissani ◽  
Islam Hadj Mohamed Guetarni ◽  
Soraya Zebirate
Keyword(s):  
Dynamic Control ◽  
Safety System ◽  
Case Based Reasoning ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent ◽  
Case Based

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.

HYBRID NEURAL INTELLIGENT SYSTEM TO PREDICT BUSINESS FAILURE IN SMALL-TO-MEDIUM-SIZE ENTERPRISES

2011 ◽  
Vol 21 (04) ◽  
pp. 277-296 ◽  
Author(s):  
M. LOURDES BORRAJO ◽  
BRUNO BARUQUE ◽  
EMILIO CORCHADO ◽  
JAVIER BAJO ◽  
JUAN M. CORCHADO
Keyword(s):  
Intelligent System ◽  
Control Process ◽  
Business Failure ◽  
Medium Size ◽  
Case Based Reasoning ◽  
Multi Agent System ◽  
Agent System ◽  
Reasoning System ◽  
Multi Agent ◽  
Case Based

During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

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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