A multi‐agent system for itinerary suggestion in smart environments

Abstract Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on thei...

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Autores principales:	Alessandra De Paola, Salvatore Gaglio, Andrea Giammanco, Giuseppe Lo Re, Marco Morana
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	artificial intelligence pattern recognition Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/c89374e593824ba084def9ac170a3be1
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spelling	oai:doaj.org-article:c89374e593824ba084def9ac170a3be12021-11-17T03:12:43ZA multi‐agent system for itinerary suggestion in smart environments2468-232210.1049/cit2.12056https://doaj.org/article/c89374e593824ba084def9ac170a3be12021-12-01T00:00:00Zhttps://doi.org/10.1049/cit2.12056https://doaj.org/toc/2468-2322Abstract Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so‐called cold‐start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi‐agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi‐Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high‐quality suggestions while avoiding both cold‐start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real‐life scenarios, such as smart campuses and theme parks.Alessandra De PaolaSalvatore GaglioAndrea GiammancoGiuseppe Lo ReMarco MoranaWileyarticleartificial intelligencepattern recognitionComputational linguistics. Natural language processingP98-98.5Computer softwareQA76.75-76.765ENCAAI Transactions on Intelligence Technology, Vol 6, Iss 4, Pp 377-393 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence pattern recognition Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
spellingShingle	artificial intelligence pattern recognition Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 Alessandra De Paola Salvatore Gaglio Andrea Giammanco Giuseppe Lo Re Marco Morana A multi‐agent system for itinerary suggestion in smart environments
description	Abstract Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so‐called cold‐start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi‐agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi‐Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high‐quality suggestions while avoiding both cold‐start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real‐life scenarios, such as smart campuses and theme parks.
format	article
author	Alessandra De Paola Salvatore Gaglio Andrea Giammanco Giuseppe Lo Re Marco Morana
author_facet	Alessandra De Paola Salvatore Gaglio Andrea Giammanco Giuseppe Lo Re Marco Morana
author_sort	Alessandra De Paola
title	A multi‐agent system for itinerary suggestion in smart environments
title_short	A multi‐agent system for itinerary suggestion in smart environments
title_full	A multi‐agent system for itinerary suggestion in smart environments
title_fullStr	A multi‐agent system for itinerary suggestion in smart environments
title_full_unstemmed	A multi‐agent system for itinerary suggestion in smart environments
title_sort	multi‐agent system for itinerary suggestion in smart environments
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/c89374e593824ba084def9ac170a3be1
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A multi‐agent system for itinerary suggestion in smart environments

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