Building custom, adaptive and heterogeneous multi-agent systems for semantic information retrieval using organizational-multi-agent systems engineering, O-MaSE

Author(s):  
Gaurav Kant Shankhdhar ◽  
Manuj Darbari
Author(s):  
Tarek Ben Mena ◽  
Narjès Bellamine-Ben Saoud ◽  
Mohamed Ben Ahmed ◽  
Bernard Pavard

This chapter aims to define context notion for multi-agent systems (MAS). Starting from the state of the art on context in different disciplines, we present context as a generic and abstract notion. We argue that context depends on three characteristics: domain, entity, and problem. By specifying this definition with MAS, we initially consider context from an extensional point of view as three components—actant, role, and situation—and then from an intensional one, which represents the context model for agents in MAS which consist of information on environment, other objects, agents, and relations between them. Therefore, we underline a new way of representing agent knowledge, building context on this knowledge, and using it. Furthermore, we prove the applicability of contextual agent solution for other research fields, particularly in personalized information retrieval by taking into account as agents: crawlers and as objects: documents.


2001 ◽  
Vol 16 (3) ◽  
pp. 277-284 ◽  
Author(s):  
EDUARDO ALONSO ◽  
MARK D'INVERNO ◽  
DANIEL KUDENKO ◽  
MICHAEL LUCK ◽  
JASON NOBLE

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.


Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 312 ◽  
Author(s):  
Manuel Herrera ◽  
Marco Pérez-Hernández ◽  
Ajith Kumar Parlikad ◽  
Joaquín Izquierdo

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.


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