Multi-agent Systems for Web-Based Map Information Retrieval

2002 ◽  
pp. 161-180 ◽  
Author(s):  
Maozhen Li ◽  
Sheng Zhou ◽  
Christopher B. Jones
Keyword(s):  
Information Retrieval ◽  
Multi Agent Systems ◽  
Web Based ◽  
Agent Systems ◽  
Multi Agent

Towards a Context Definition for Multi-Agent Systems

2011 ◽  
pp. 286-307
Author(s):  
Tarek Ben Mena ◽  
Narjès Bellamine-Ben Saoud ◽  
Mohamed Ben Ahmed ◽  
Bernard Pavard
Keyword(s):  
Information Retrieval ◽  
Knowledge Building ◽  
Point Of View ◽  
Multi Agent Systems ◽  
Context Model ◽  
Agent Systems ◽  
Research Fields ◽  
Present Context ◽  
Multi Agent ◽  
Abstract Notion

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.

scholarly journals Learning in multi-agent systems

2001 ◽  
Vol 16 (3) ◽  
pp. 277-284 ◽  
Author(s):  
EDUARDO ALONSO ◽  
MARK D'INVERNO ◽  
DANIEL KUDENKO ◽  
MICHAEL LUCK ◽  
JASON NOBLE
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Community Research ◽  
Intelligence Community ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Software Programs

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.

A Framework Based on Multi-agent Systems for Information Retrieval Through Mobile Devices

2005 ◽  
pp. 240-241 ◽  
Author(s):  
Angela Carrillo Ramos ◽  
Jérôme Gensel ◽  
Marlène Villanova-Oliver ◽  
Hervé Martin
Keyword(s):  
Information Retrieval ◽  
Mobile Devices ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Web-based Simulations of Multi-agent Systems

SIMULATION ◽  
2017 ◽  
Vol 93 (9) ◽  
pp. 737-748
Author(s):  
Massimiliano De Benedetti ◽  
Fabrizio Messina ◽  
Giuseppe Pappalardo ◽  
Corrado Santoro
Keyword(s):  
Group Formation ◽  
Fine Tuning ◽  
Communication Model ◽  
Multi Agent Systems ◽  
Modular Architecture ◽  
Web Browser ◽  
Web Based ◽  
Agent Systems ◽  
Multi Agent ◽  
3D Scene

This paper describes the architecture of AgentSimJs, a Javascript-based multi-agent simulator intended to execute and visualize simulations through a Web browser. It includes the needed capabilities to render a 3D scene with objects and agents. AgentSimJs has a modular architecture, the several different components provide a set of flexible primitives to program the agent behaviour. With AgentSimJs, a user can perform a fine tuning of all the aspects related to communication, motion, and group formation. Users can also define a custom network topology and communication model.

Formal reasoning on knowledge and commitments in multi-agent systems using Theatre

SIMULATION ◽  
2019 ◽  
Vol 96 (2) ◽  
pp. 233-250 ◽  
Author(s):  
Christian Nigro ◽  
Libero Nigro ◽  
Paolo F Sciammarella
Keyword(s):  
Formal Method ◽  
Multi Agent Systems ◽  
Formal Reasoning ◽  
Deterministic Analysis ◽  
Web Based ◽  
Modeling And Analysis ◽  
Agent Systems ◽  
Multi Agent ◽  
Analysis Of Knowledge ◽  
Event Occurrence

This paper proposes a formal method based on the Theatre framework for modeling and analysis of knowledge and commitments in multi-agent systems (MASs). Theatre centers on actors and a reduction on to Uppaal, which enables both non-deterministic analysis (that is, qualitative analysis by exhaustive verification, or showing that something can possibly occur) and quantitative analysis (that is, estimating probability measures of event occurrence through simulations) of the same model. The article describes the modeling and analysis approach based on Theatre and Uppaal, and shows its application to the modeling and property checking of the NetBill protocol used in web-based MAS applications when selling/buying goods. Properties of the NetBill protocol are demonstrated by experimental results.

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