Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models

Artificial intelligence is a transformational development across multiple research areas within urban planning. Urban simulation models have been an important part of urban planning for decades. Current advances in artificial intelligence have changed the scope of these models by enabling the incorp...

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Main Authors: Solon Solomou, Ulysses Sengupta
Format: Article
Language:English
Published: Cogitatio 2025-01-01
Series:Urban Planning
Subjects:
Online Access:https://www.cogitatiopress.com/urbanplanning/article/view/8561
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author Solon Solomou
Ulysses Sengupta
author_facet Solon Solomou
Ulysses Sengupta
author_sort Solon Solomou
collection DOAJ
description Artificial intelligence is a transformational development across multiple research areas within urban planning. Urban simulation models have been an important part of urban planning for decades. Current advances in artificial intelligence have changed the scope of these models by enabling the incorporation of more complex agent behaviours in models aimed at understanding dweller behaviour within alternative future scenarios. The research presented in this article is situated in location choice modelling. It compares outcomes of two multi-agent systems, testing intelligent computer agent decision-making with selected behavioural patterns associated with human decision-making, given the same choices and scenarios. The majority of agent-based urban simulation models in use base the decision-making of agents on logic-based agent architecture and utility maximisation theory. This article explores the use of cognitive agent architecture as an alternative approach to endow agents with memory representation and experiential learning, thus enhancing their intelligence. The study evaluates the model’s suitability, strengths, and weaknesses, by comparing it against the results of a control model featuring commonly used logic-based architecture. The findings showcase the improved ability of cognitive-based intelligent agents to display dynamic market behaviours. The conclusion discusses the potential of utilising cognitive agent architectures and the ability of these models to investigate complex urban patterns incorporating unpredictability, uncertainty, non-linearity, adaptability, evolution, and emergence. The experiment demonstrates the possibility of modelling with more intelligent agents for future city planning and policy.
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spelling doaj-art-10f0e3e910ce4b5ead7fadcfa81e11872025-01-21T10:43:37ZengCogitatioUrban Planning2183-76352025-01-0110010.17645/up.85613937Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation ModelsSolon Solomou0Ulysses Sengupta1Department of Architecture, University of Nicosia, CyprusManchester School of Architecture, Manchester Metropolitan University, UK / Manchester School of Architecture, University of Manchester, UKArtificial intelligence is a transformational development across multiple research areas within urban planning. Urban simulation models have been an important part of urban planning for decades. Current advances in artificial intelligence have changed the scope of these models by enabling the incorporation of more complex agent behaviours in models aimed at understanding dweller behaviour within alternative future scenarios. The research presented in this article is situated in location choice modelling. It compares outcomes of two multi-agent systems, testing intelligent computer agent decision-making with selected behavioural patterns associated with human decision-making, given the same choices and scenarios. The majority of agent-based urban simulation models in use base the decision-making of agents on logic-based agent architecture and utility maximisation theory. This article explores the use of cognitive agent architecture as an alternative approach to endow agents with memory representation and experiential learning, thus enhancing their intelligence. The study evaluates the model’s suitability, strengths, and weaknesses, by comparing it against the results of a control model featuring commonly used logic-based architecture. The findings showcase the improved ability of cognitive-based intelligent agents to display dynamic market behaviours. The conclusion discusses the potential of utilising cognitive agent architectures and the ability of these models to investigate complex urban patterns incorporating unpredictability, uncertainty, non-linearity, adaptability, evolution, and emergence. The experiment demonstrates the possibility of modelling with more intelligent agents for future city planning and policy.https://www.cogitatiopress.com/urbanplanning/article/view/8561agent-based modellingartificial intelligencecognitive agentscomplexityhousehold location choiceintelligent agentsmarket dynamicsplanning toolsurban simulation
spellingShingle Solon Solomou
Ulysses Sengupta
Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
Urban Planning
agent-based modelling
artificial intelligence
cognitive agents
complexity
household location choice
intelligent agents
market dynamics
planning tools
urban simulation
title Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
title_full Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
title_fullStr Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
title_full_unstemmed Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
title_short Simulating Complex Urban Behaviours With AI: Incorporating Improved Intelligent Agents in Urban Simulation Models
title_sort simulating complex urban behaviours with ai incorporating improved intelligent agents in urban simulation models
topic agent-based modelling
artificial intelligence
cognitive agents
complexity
household location choice
intelligent agents
market dynamics
planning tools
urban simulation
url https://www.cogitatiopress.com/urbanplanning/article/view/8561
work_keys_str_mv AT solonsolomou simulatingcomplexurbanbehaviourswithaiincorporatingimprovedintelligentagentsinurbansimulationmodels
AT ulyssessengupta simulatingcomplexurbanbehaviourswithaiincorporatingimprovedintelligentagentsinurbansimulationmodels