2021-03-262021-03-262016Aranda Corral, G.A., Borrego Díaz, J. y Galán Páez, J. (2016). Synthetizing Qualitative (Logical) Patterns for Pedestrian Simulation from Data. En IntelliSys 2016: SAI Intelligent Systems Conference (243-260), London, UK: Springer.978-3-319-56990-12367-3370https://hdl.handle.net/11441/106659This work introduces a (qualitative) data-driven framework to extract patterns of pedestrian behaviour and synthesize Agent-Based Models. The idea consists in obtaining a rule-based model of pedestrian behaviour by means of automated methods from data mining. In order to extract qualitative rules from data, a mathematical theory called Formal Concept Analysis (FCA) is used. FCA also provides tools for implicational reasoning, which facilitates the design of qualitative simulations from both, observations and other models of pedestrian mobility. The robustness of the method on a general agent-based setting of movable agents within a grid is shown.application/pdf18engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Agent-based modellingKnowledge acquisitionQualitative spatial reasoningFormal concept analysisinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess10.1007/978-3-319-56991-8_1921441660