Synthetizing Qualitative (Logical) Patterns for Pedestrian Simulation from Data

This 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.Ministerio de Economía y Competitividad TIN2013-41086-

Generating Armstrong ABoxes for ALC TBoxes

A challenge in ontology engineering is the mismatch in expertise between the ontology engineer and domain expert, which often leads to important constraints not being specified. Domain experts often only focus on specifying constraints that should hold and not on specifying constraints that could possibly be violated. In an attempt to bridge this gap we propose the use of “perfect test data”. The generated test data is perfect in that it satisfies all the constraints of an application domain that are required, including ensuring that the test data violates constraints that can be violated. In the context of Description Logic ontologies we call this test data an “Armstrong ABox”, a notion derived from Armstrong relations in relational database theory. In this paper we detail the theoretical development of Armstrong ABoxes for ALC TBoxes as well as an algorithm for generating such Armstrong ABoxes. The proposed algorithm is based, via the ontology completion algorithm of Baader et al., on attribute exploration in formal concept analysis.http://link.springer.combookseries/5582019-11-03hj2018Informatic