Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/22377
Title: | Knowledge-Based Approach to Modeling Urban Dynamics | Authors: | Gievska, Sonja Lameski, Petre |
Keywords: | Pedestrian modeling · Bayesian inference · Multi-agent simulation | Issue Date: | 9-Jul-2017 | Publisher: | Springer, Cham | Conference: | International Conference on Distributed, Ambient, and Pervasive Interactions | Abstract: | The model representing the complexity of the pedestrian mobility has to incorporate the nature of the modeled phenomenon by accounting the interde‐ pendence between human behavior and urban environment. Our efforts are directed towards correlating emergent behavior patterns of different types of pedestrians to contextual knowledge that will help us map realistic pedestrian behavior into agent’s decision making capabilities. We propose that agent’s beliefs, goals and decision-making strategies should be derived directly from the integrated urban knowledge. Causal probabilistic models that are based on Baye‐ sian inference are proposed as a potential solution to some of the challenges in the pedestrian agent modeling. | URI: | http://hdl.handle.net/20.500.12188/22377 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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