Integration of Case Based Reasoning in Multi-agent System for the Real-Time Container Stacking in Seaport Terminals

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.

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Multi-Agent System of an Adaptive Learning Hypermedia Based on Incremental Hybrid Case-Based Reasoning

Innovations in Smart Cities Applications Edition 3 - Lecture Notes in Intelligent Transportation and Infrastructure ◽

10.1007/978-3-030-37629-1_12 ◽

2020 ◽

pp. 143-156

Author(s):

Nihad El Ghouch ◽

Mohamed Kouissi ◽

El Mokhtar En-Naimi

Keyword(s):

Adaptive Learning ◽

Case Based Reasoning ◽

Multi Agent System ◽

Agent System ◽

Multi Agent ◽

Case Based

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A Multi-Agent System Architecture for Coordination of the Real-Time Control Functions in Complex Industrial Systems

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.4.2108 ◽

2011 ◽

Vol 6 (4) ◽

pp. 761

Author(s):

Jiang Wu ◽

Raynitchka Tzoneva

Keyword(s):

Real Time ◽

System Architecture ◽

Multi Agent System ◽

Real Time Control ◽

Agent System ◽

Industrial Systems ◽

The Real ◽

Time Control ◽

Control Functions ◽

Multi Agent

Multi-agent system architecture for coordination of the real-time control functions in complex industrial systems is presented. The problem which must be solved out is how efficiently to organize the interactions between tasks in order to satisfy the functionality and the time restriction of the system. In order to solve this problem, the treatment of the task interactions is separated from the tasks and is implemented by the proposed multi-agent system. A general three level multi-agent system is introduced to manage the interactions and schedule of tasks. A framework of building of the schedule of the tasks is also presented. Finally, the benefits of the proposed architecture are discussed.

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