Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning

A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

“…Three main units compose the non-monotonic fuzzy reasoning models: 1) a fuzzification module, 2) an inference engine and 3) a defuzzification module (figure 1 left). The argument-based models are structured over 5 layers, as proposed in [13] (figure 1 right): 1) definition of the structure of arguments, 2) definition of their conflicts, 3) their evaluation 4) the computation of the dialectical status of each argument and 5) their final accrual. A comparison of the inferences produced by AT and fuzzy reasoning was done by assessing their true positive (TPR) and false positive (FPR) rates on a dataset of 93 elderly patients described by 51 biomarkers (feature set).…”

“…Furthermore, it is getting momentum thanks to its higher capacity and transparency to justify and retrace inferences [15,16]. In recent works [19,20] it is shown how different knowledge-bases can be translated into different argument-based models following a 5-layer schema upon which argumentation systems are generally built [13]. This schema includes the definition of the internal structure of arguments, the attacks and the resolution of conflicts as well as the computation of their dialectical status and the production of a final justifiable inference (schema adopted in this study and detailed in section 3.3).…”

“…The definition of argument based-models follows the 5-layer modelling approach proposed in [13] (and depicted in figure 1 right).…”

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

“…Thus, being closer to the way humans reason under uncertainty and leading to a higher explanatory capacity (Longo, 2016). This can be exemplified by its attempted use for the development of argumentation-based approaches to explainable AI (Zeng et al, 2018).…”

“…For instance, previous studies (Rizzo et al, 2018b,a) suggest that defeasible argumentation offers more powerful conflict resolution strategies; fuzzy reasoning is suitable for robust representation of linguistic information through the application of fuzzy membership functions; and expert systems focus on imitating the problem-solving ability of an expert. These approaches have all been extensively used in practical domains such as medicine, pharmaceutical industry and engineering (Longo, 2016;Glasspool et al, 2006;Mardani et al, 2015;Liao, 2005). However, scholars have predominantly focused on their individual application for non-monotonic reasoning, but barely attempted to empirically investigate their differences in terms of inferential capacity.…”