Human-Based Models for Ambient Intelligence Environments

Ambient Intelligence gathers best results from three key technologies, Ubiquitous Computing, Ubiquitous Communication, and Intelligent User Friendly Interfaces. The functional and spatial distribution of tasks is a natural thrust to employ multiagent paradigm to design and implement AmI environments. Two critical issues, common in most of applications, are (1) how to detect in a general and efficient way context from sensors and (2) how to process contextual information in order to improve the functionality of services. Here we describe an agent-based ambient intelligence architecture able to deliver services on the basis of physical and emotional user status captured from a set of biometric features. Abstract representation and management is achieved thanks to two markup languages, H2ML and FML, able to model behavioral as well as fuzzy control activities and to exploit distribution and concurrent computation in order to gain real-time performances.

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Human-Based Models for Ambient Intelligence Environments

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch137 ◽

2011 ◽

pp. 2351-2364 ◽

Cited By ~ 1

Author(s):

Giovanni Acampora ◽

Vincenzo Loia ◽

Michele Nappi ◽

Stefano Ricciardi

Keyword(s):

Spatial Distribution ◽

Fuzzy Control ◽

Ambient Intelligence ◽

Contextual Information ◽

Agent Based ◽

Key Technologies ◽

Concurrent Computation ◽

Critical Issues ◽

User Friendly ◽

Biometric Features

Ambient Intelligence gathers best results from three key technologies, Ubiquitous Computing, Ubiquitous Communication, and Intelligent User Friendly Interfaces. The functional and spatial distribution of tasks is a natural thrust to employ multiagent paradigm to design and implement AmI environments. Two critical issues, common in most of applications, are (1) how to detect in a general and efficient way context from sensors and (2) how to process contextual information in order to improve the functionality of services. Here we describe an agent-based ambient intelligence architecture able to deliver services on the basis of physical and emotional user status captured from a set of biometric features. Abstract representation and management is achieved thanks to two markup languages, H2ML and FML, able to model behavioral as well as fuzzy control activities and to exploit distribution and concurrent computation in order to gain real-time performances.

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Toward Agent-Based Grid Computing

Architectural Design of Multi-Agent Systems ◽

10.4018/978-1-59904-108-7.ch009 ◽

2011 ◽

pp. 173-188

Author(s):

Lizhe Wang

Keyword(s):

Grid Computing ◽

Research Trends ◽

Future Research ◽

Research Issues ◽

Agent Based ◽

Computing Paradigm ◽

Natural Choice ◽

Key Technologies ◽

Computing Grid ◽

Multi Agent

This chapter discusses research issues related to agent-based Grid computing. Grid computing now becomes an innovative computing paradigm and helps build non-traditional computing infrastructures and applications. Multiple-agent systems and algorithms, on the other hand, mainly focus on solving corporative problems among multiple participants, mainly from theoretical aspects. It is thus a natural choice to combine these two key technologies together and benefit both research communities. This chapter first reviews background for multi-agent system, agent-based computing, and Grid computing. Research challenges and issues are characterized and identified together with possible solutions. After the investigation of current research efforts of agent-based Grid computing, future research trends are presented and studied.

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Construction and Debugging of a Multi-Agent Based Simulation to Study Ambient Intelligence Applications

Lecture Notes in Computer Science - Bio-Inspired Systems: Computational and Ambient Intelligence ◽

10.1007/978-3-642-02478-8_136 ◽

2009 ◽

pp. 1090-1097 ◽

Cited By ~ 2

Author(s):

Emilio Serrano ◽

Juan A. Botia ◽

Jose M. Cadenas

Keyword(s):

Ambient Intelligence ◽

Agent Based Simulation ◽

Agent Based ◽

Multi Agent

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Multi-Agent Based Flexible Deployment of Context Management in Ambient Intelligence Applications

Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection - Lecture Notes in Computer Science ◽

10.1007/978-3-319-18944-4_19 ◽

2015 ◽

pp. 225-239 ◽

Cited By ~ 7

Author(s):

Alexandru Sorici ◽

Gauthier Picard ◽

Olivier Boissier ◽

Adina Florea

Keyword(s):

Ambient Intelligence ◽

Context Management ◽

Agent Based ◽

Multi Agent

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AGENT-BASED, CONTEXT-AWARE INFORMATION SHARING FOR AMBIENT INTELLIGENCE

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000498 ◽

2011 ◽

Vol 20 (06) ◽

pp. 985-1000 ◽

Cited By ~ 9

Author(s):

ANDREI OLARU ◽

CRISTIAN GRATIE

Keyword(s):

Ambient Intelligence ◽

Self Organization ◽

Context Aware ◽

Multi Agent System ◽

Interconnected Network ◽

Agent Based ◽

Surrounding Environment ◽

Multi Agent ◽

The One ◽

Future Vision

In a future vision of Ambient Intelligence — or AmI — our surrounding environment will integrate a pervasive, interconnected network of sensors, intelligent appliances and computer-like devices. This implies, on the one hand, hardware and interface related issues, and, on the other hand, a layer of context-aware services that manages the large quantities of information generated throughout a system formed mostly of devices with limited capabilities. This paper presents the first steps toward the realization of the AmIciTy framework: a multi-agent system that relies on local interaction and the self-organization of agents, having as purpose the context-aware sharing of pieces of information. The paper presents the structure of the system, the design of the agents, the manner of building scenarios, experiments and the evaluation of a prototype.

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The Study on the Key Technologies in Multiple Agent-Based Intelligent Tutoring System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1889 ◽

2013 ◽

Vol 846-847 ◽

pp. 1889-1892

Author(s):

Yan Yu ◽

Jian Hua Wang

Keyword(s):

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Multi Agent Systems ◽

Teaching System ◽

Network Teaching ◽

Agent Based ◽

Agent Systems ◽

Multiple Agent ◽

Key Technologies ◽

Multi Agent

Network teaching system has changed the way of teaching, with the artificial intelligence technology, Currently, many traditional network teaching system has been unable to meet the needs of the public.After studying the defects in current teaching systems and combining features of multi-agent systems and their application theories in intelligent teaching systems, this paper, integrating the concept of multi-agent systems, discusses the key technologies in multiple agent-based intelligent tutoring systems based on multiple agent-based intelligent tutoring system models. To achieve true intelligent teaching system, simulate real teaching environments, offer customized teaching services, and really realize students' independent learning and collaborative learning.

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ONTOLOGY‐BASED MULTI‐AGENT SYSTEM TO SUPPORT BUSINESS USERS AND MANAGEMENT / DAUGIAAGENTINE SISTEMA GRINDŽIAMA ONTOLOGIJA VERSLO VARTOTOJAMS IR VADYBININKAMS PAREMTI

Technological and Economic Development of Economy ◽

10.3846/tede.2010.21 ◽

2010 ◽

Vol 16 (2) ◽

pp. 327-347 ◽

Cited By ~ 12

Author(s):

Dejan Lavbič ◽

Olegas Vasilecas ◽

Rok Rupnik

Keyword(s):

Mobile Communications ◽

Web Sites ◽

Supply And Demand ◽

Added Value ◽

Business Rules ◽

Agent Based ◽

External Data ◽

External Sources ◽

Multi Agent ◽

User Friendly

For some decision processes a significant added value is achieved when enterprises’ internal Data Warehouse (DW) can be integrated and combined with external data gained from web sites of competitors and other relevant Web sources. In this paper we discuss the agent‐based integration approach using ontologies (DSS‐MAS). In this approach data from internal DW and external sources are scanned by coordinated group of agents, while semantically integrated and relevant data is reported to business users according to business rules. After data from internal DW, Web sources and business rules are acquired, agents using these data and rules can infer new knowledge and therefore facilitate decision making process. Knowledge represented in enterprises’ ontologies is acquired from business users without extensive technical knowledge using user friendly user interface based on constraints and predefined templates. The approach presented in the paper was verified using the case study from the domain of mobile communications with the emphasis on supply and demand of mobile phones. Santrauka Priimant kai kuriuos sprendimus reikšminga pridetinė vertė pasiekiama, kai įmones duomenų saugykla gali būti integruojama ir sujungiama su išoriniu konkurentų bei kitais svarbiais duomenimis, gaunamais iš interneto šaltiniu. Straipsnyje nagrinejamas tokio uždavinio sprendimas taikant ontologiją ir daugia‐agenčių integracijos metodų grindžiama būdą, naudotą daugiaagentėje sistemoje sprendimams rengti (DSS‐MAS). Taikant ši būdą duomenys iš vidinių duomenų saugyklų ir išorinių šaltiniu nagrinėjami bei renkami koordinuojamų agentų grupių. Veliau semantiškai integruoti bei atrinkti reikšmingi duomenys pateikiami verslo vartotojams pagal galiojančias verslo taisykles. Kai verslo taisykles, duomenys iš vidiniu duomenų saugyklų ir interneto šaltinių yra gauti, agentai naudodami šiuos duomenis, gali išvesti naujas žinias ir palengvinti sprendimu rengimo procesą. Žinios, vaizduojamos įmonės ontologijoje, gaunamos iš verslo vartotoju nekeliant jiems aukštu išsilavinimo reikalavimu, nes naudojama nežalinga sąsaja, kuri grindžiama prasmingais ribojimais ir iš anksto apibrėžtais šablonais. Straipsnyje pateiktas būdas buvo patikrintas nagrinėjant atvejį iš mobiliujų komunikacijų dalykinės srities ir pabrežiant mobiliųjų telefonų tiekimą bei paklausa.

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