Juan Galán-Páez

Complex concept lattices for simulating human prediction in sport

In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In particular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis (FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized in concept lattices). In complex environments, such as sport competitions, the large amount of information currently available turns concept lattices into complex networks. The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure- is analyzed. It is applied to building a knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.

Qualitative Reasoning on Complex Systems from Observations

A hybrid approach to phenomenological reconstruction of Complex Systems (CS), using Formal Concept Analysis (FCA) as main tool for conceptual data mining, is proposed. To illustrate the method, a classic CS is selected (cellular automata), to show how FCA can assist to predict CS evolution under different conceptual descriptions (from different observable features of the CS).

Simulating Language Dynamics by Means of Concept Reasoning

A problem in the phenomenological reconstruction of Complex Systems (CS) is the extraction of the knowledge that elements playing in CS use during its evolution. This problem is important because such a knowledge would allow the researcher to understand the global behavior of the system [1, 2]. In this paper an approach to partially solve this problem by means of Formal Concept Analysis (FCA) is described in a particular case, namely Language Dynamics. The main idea lies in the fact that global knowledge in CS is naturally built by local interactions among agents, and FCA could be useful to represent their own knowledge. In this way it is possible to represent the effect of interactions on individual knowledge as well as the dynamics of global knowledge. Experiments in order to show this approach are given using WordNet.

Modeling Lexicon Emergence as Concept Emergence in Networks

A model for lexicon emergence in social networks is presented. The model is based on a modified version of classic Naming Games, where agents’ knowledge is represented by means of formal contexts. That way it is possible to represent the effect interactions have on individual knowledge as well as the dynamics of global knowledge in the network.

Emergent Concepts on Knowledge Intensive Processes

An approach to refine and revise the general framework of KiP (Knowledge Intensive Process) is presented. The specific case of collaborative KiP is studied and the prominent role of collaborative KiPs in the general context of Business Processes is revealed. The approach is based on Formal Concept Analysis.

On Experimental Efficiency for Retraction Operator to Stem Basis

In this paper, we introduce an implementation of an inference rule called “Independence Rule” which lets us reduce the size of knowledge basis based on the retraction problem. This implementation is made in a functional language, Scala, and specialized on attribute implications. We evaluate its efficiency related to the Stem Base generation.

Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Urban Data management represents a major challenge in the field of Smart Cities. Its understanding is essential for the development of better smart services, which are a persistent demand in urban policies. From all the sources of data available, those that involve a collective processing of urban information (by the citizens or other collectives) deliver in fact, useful insights into social perception. Such is the case, for example, of data collected from mobile networks. Prior to the design of sociotechnical artifacts in cities, it seems important to extract the qualitative, quantitative opinions, sentiment, feedbacks present in these data. In this paper we present three solutions for mining these contents through Knowledge Extraction methods, as a previous step to the prospection of new smart services.

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.

ASAP: A framework for designing gamified models of complex systems

This paper introduces ASAP, a framework for the development of mobile gamification of problems where Complex Systems play a fundamental role. This framework allows adding a layer of gamification concepts over a set of multi-agent systems for the raw modeling of the problem. Over it, a layer of data acquisition can be added in order to apply data analysis to the interactions and evolution of games. Some simple running examples are provided.