Bounded Rationality for Data Reasoning Based on Formal Concept Analysis

Aranda-Corral, Gonzalo A.; Borrego-Dí­az, Joaquín; ́ez, Juan Gal ́n-P

doi:10.1109/dexa.2011.18

Cited by 4 publications

(17 citation statements)

References 18 publications

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“…The full model (described in Aranda‐Corral et al ., ) is composed of events (objects), which have a number of properties (attributes). They constitute a universal formal context

double-struckM

(the monster context ).…”

Section: Formal Concept Analysis Based Reasoning On Complex Systemmentioning

confidence: 99%

On the Phenomenological Reconstruction of Complex Systems—The Scale‐Free Conceptualization Hypothesis

2013

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Phenomenological reconstruction of a complex system (CS) from collected and selected data allows us to work with formal models (representations) of the system. The task of building a qualitative model necessitates the formalization of relationships among observations and concrete features. Formal concept analysis can help to understand the conceptual structure behind these qualitative representations by means of the so‐called concept lattices (CLs). The study of these kinds of semantic networks suggests that a strong relationship exists between its topological structure and its soundness/usefulness as a qualitative representation of the CS. The present paper is devoted to this question by presenting the so‐called scale‐free conceptualization hypothesis. The hypothesis claims that a scale‐free distribution of node connectivity appears on the CL associated to complex systems (CLCS) only when two requirements hold: CLCS is useful both to represent qualitative and reliable attributes on the CS, as well as to provide a basis for (qualitatively) successfully reasoning about the CS. Experiments revealed that the topologies of CLCS are similar when the amount of information on the CS is sufficient, whereas it is different in other CLs associated to random formal contexts or to other systems in which some of the former requirements do not hold. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The full model (described in Aranda‐Corral et al ., ) is composed of events (objects), which have a number of properties (attributes). They constitute a universal formal context

double-struckM

(the monster context ).…”

Section: Formal Concept Analysis Based Reasoning On Complex Systemmentioning

confidence: 99%

On the Phenomenological Reconstruction of Complex Systems—The Scale‐Free Conceptualization Hypothesis

2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [6], this general approach was applied using argumentative reasoning on contextual selections. The structure of contextual selection showed some insights on attribute selection [13] . …”

Section: Bounded Reasoning On Complex Networkmentioning

confidence: 99%

“…The model (described in [13]) is composed of events (objects) which have a number of properties (attributes). They constitute a universal formal context M (which we call monster context following the tradition in model theory).…”

Section: Reasoning Under Contextual Selection: Logical Foundationsmentioning

confidence: 99%

“…The reasoning model is based on arguments extracted from subcontexts [13] . Definition 3 Let L be an implication and Δ a background knowledge.…”

Section: Reasoning Under Contextual Selection: Logical Foundationsmentioning

confidence: 99%

“…On the other hand, we analyze the attribute selection problem as a feature selection problem, which would shape the behavior of the CS that represents professional soccer leagues. The theoretical framework, on which this model is based, is presented in [13]. Due to the huge amount of information, attribute selection advised by experts is mandatory.…”

Section: Introductionmentioning

confidence: 99%

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Complex concept lattices for simulating human prediction in sport

2013

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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.

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Knowledge representation for explainable artificial intelligence

Borrego-Díaz

Galán-Páez

2022

Complex Intell. Syst.

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Alongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.

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Bounded Rationality for Data Reasoning Based on Formal Concept Analysis

Cited by 4 publications

References 18 publications

On the Phenomenological Reconstruction of Complex Systems—The Scale‐Free Conceptualization Hypothesis

On the Phenomenological Reconstruction of Complex Systems—The Scale‐Free Conceptualization Hypothesis

Complex concept lattices for simulating human prediction in sport

Knowledge representation for explainable artificial intelligence

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