Abstract This paper shows how argumentation logic (AL) can be further extended to cover more fully paraconsistent forms of logical reasoning. The extension is based on the notion of non-acceptable, self-defeating arguments as a generalization of the Reductio ad Absurdum principle. In this case, a self-defeating argument is either in conflict with itself or with an argument that is needed for its defense against one of its counter-arguments. In the proposed extended AL, such self-defeating arguments are recognized as arguments that do not need to be explicitly defended against. Hence attacks on other arguments by self-defeating arguments can be ignored, thus extending the possibility of the attacked arguments to be acceptable.

Although a plethora of research has been published in the literature, providing both qualitative and quantitative analyses of cardiovascular risk, there remains a need to improve interpretability, explainability, and accuracy in the assessment of cardiovascular disease risk. To achieve this, the present study proposes a methodology that extracts knowledge from data to assess cardiovascular disease risk while also offering both local and global explanations that justify the underlying theory made and why in some cases no definite decision can be taken.

Emergency departments (EDs) require effective approaches for quickly identifying patients at risk of critical outcomes (inpatient mortality or ICU admission within 12 hours). This study developed interpretable machine learning models using Gradient Boosting (GB) and Logistic Regression (LR) with 13 initial triage variables from the MIMIC-IV-ED database. GB performed slightly better than LR (Accuracy: 78.21% vs. 77.27%, AUROC: 0.887 vs. 0.863, AUPRC: 0.445 vs. 0.370). The Te2Rules method was used to extract 43 clinically interpretable rules from the GB model, with a overall fidelity of 98.90%. The use of the Rule Coverage Index (RCI) to categorize rules into high, medium, and low levels futher enhanced clinical utility. This study aims to strike a balance between predictive accuracy and interpretability, facilitating clinicians in early detection of critically ill presenting to the ED.

Gorgias Cloud offers argumentation-based decision making as a service.The service includes an integrated development environment for the theories, testing and execution based on user scenarios, and, finally, an API for use by user applications.Gorgias is a structured argumentation framework where arguments are constructed using a basic scheme of argument rules.Two types of arguments are constructed within a Gorgias argumentation theory: object-level arguments and priority arguments expressing a preference between other arguments.Admissible composite arguments supporting a claim typically include both types of arguments.The Gorgias framework was introduced in [1], extended in [2] and applied to a variety of real-life application problems in [3].The Gorgias system allows us to code argumentation theories of the form described above and subsequently query the system to find out if there is an admissible (composite) argument that supports a desired Claim.The system of Gorgias has been publicly available since 2003 and has been used by several research groups to develop prototype real-life applications of argumentation in a variety of application domains.Today, it is available as a service over the internet with Gorgias Cloud, which provides an integrated environment for developing applications of argumentation with three novel features: Computational Models of Argument F.

The current adoption of Medical Artificial Intelligence (AI) solutions in clinical practice suggest that despite its undeniable potential AI is not achieving this potential. A major barrier to its adoption is the lack of transparency and interpretability, and the inability of the system to explain its results. Explainable AI (XAI) is an emerging field in AI that aims to address these barriers, with the development of new or modified algorithms to enable transparency, provide explanations in a way that humans can understand and foster trust. Numerous XAI techniques have been proposed in the literature, commonly classified as model-agnostic or model-specific. In this study, we examine the application of four model-agnostic XAI techniques (LIME, SHAP, ANCHORS, inTrees) to an XGBoost classifier trained on real-life medical data for the prediction of high-risk asymptomatic carotid plaques based on ultrasound image analysis. We present and compare local explanations for selected observations in the test set. We also present global explanations generated from these techniques that explain the behavior of the entire model. Additionally, we assess the quality of the explanations, using suggested properties in the literature. Finally, we discuss the results of this comparative study and suggest directions for future work.

A plethora of machine learning and deep learning methods are used for the assessment of Alzheimer's Disease (AD) from brain structural changes as seen in Magnetic Resonance Imaging (MRI) with highly satisfactory results. However, these models are black-box and lack an explicit declarative knowledge representation and thus there is a difficulty in generating the underlying explanatory imaging structures. The objective of this study was to investigate the usefulness of rule extraction in the assessment of AD using decision trees (DT) and random forests (RF) algorithms and integrating the extracted rules within an argumentation-based reasoning framework in order to make the results easy to interpret and explain. The DT and RF algorithms were applied on brain MRI images acquired from normal controls (NC) and AD subjects. The KNIME analytics platform was used to compute the DT and the R project was used for the RF. The argumentation model implemented in the Gorgias framework achieved an average accuracy of 91%, exhibiting improved results compared to the models of DT and RF. The overall performance of all models in this study is in agreement with other studies. In addition, the explanations given by our approach for the various possible predictions provide a more useful and complete assessment of the state of the patient/case at hand. This study demonstrated the usefulness of rule extraction in the assessment of AD based on MRI features and the positive results of the use of the argumentation based symbolic reasoning for composing and interpreting the ML results.

Despite the recent recognition of the value of Artificial Intelligence and Machine Learning in healthcare, barriers to further adoption remain, mainly due to their "black box" nature and the algorithm's inability to explain its results. In this paper we present and propose a methodology of applying argumentation on top of machine learning to build explainable AI (XAI) models. We compare our results with Random Forests and an SVM classifier that was considered best for the same dataset in [1].

Gorgias-B is a new tool that supports a methodology for the development of real life applications. It can be used by non-argumentation experts generating and testing automatically the target argumentation theory in Gorgias.

This paper presents a novel application of argumentation for automated Story Comprehension (SC). It uses argumentation to develop a computational approach for SC as this is understood and studied in psychology. Argumentation provides uniform solutions to various representational and reasoning problems required for SC such as the frame, ramification, and qualification problems, as well as the problem of contrapositive reasoning with default information. The grounded semantics of argumentation provides a suitable basis for the construction and revision of comprehension models, through the synthesis of the explicit information from the narrative in the text with the implicit (in the reader's mind) common sense world knowledge pertaining to the topic(s) of the story given in the text. We report on the empirical evaluation of the approach through a prototype system and its ability to capture both the majority and the variability of understanding of stories by human readers. This application of argumentation can provide an important test-bed for the more general development of computational argumentation.

We propose a novel logic-based argumentation framework, called Argumentation Logic (AL), built upon a restriction of classical Propositional Logic (PL) as its underlying logic. This allows us to control the application of Reduction ad Absurdum (RA). In the case of classically consistent theories, AL and PL are equivalent, and RA is recovered through a notion of (non-)acceptability of arguments. In the case of classically inconsistent theories, AL is an extension of PL that does not trivialize, enjoying good logic-based argumentation and general logical properties.

Arguments need to be judged against other arguments. The decision to accept or reject an argument is generally a global decision that involves examining the same question for other arguments that oppose or can defend the argument in question. This article presents the acceptability semantics for abstract argumentation that through a recursive definition gives a global assignment of the acceptable and non-acceptable subsets of arguments. This semantics stems from the aim to formalize directly the generally accepted intuition that: ‘An argument can be accepted if and only if all its challenging arguments can be rejected.’ The acceptability semantics tightly integrates the notion of defending against a challenging argument by counter-attacking it with the notion of self-defeating (or self-rejecting) arguments that (help to) bring about their own non-acceptability. The proposal is motivated by earlier studies of the semantics of Logic Programming (LP) in terms of argumentation, where the basic well founded and stable model semantics of LP can be uniformly captured using a recursively defined argumentation semantics for Negation as Failure and where these standard semantics of LP can be further extended through argumentation.

Many works have identified the potential benefits of using argumentation to address a large variety of multiagent problems. In this paper we take this idea one step further and develop the concept of a fully integrated argumentation-based agent architecture that allows us to develop agents that are coherently designed on an underlying argumentation based foundation. Under this architecture, an agent is composed of a collection of modules each of which is equipped with a local argumentation theory. Similarly, the intra-agent control of the agent is governed by local argumentation theories that are sensitive to the current situation of the agent through dynamically enabled feasibility arguments.

Firewalls remain the main perimeter security protection for corporate networks. However, network size and complexity make firewall configuration and maintenance notoriously difficult. Tools are needed to analyse firewall configurations for errors, to verify that they correctly implement security requirements and to generate configurations from higher-level requirements. In this paper we extend our previous work on the use of formal argumentation and preference reasoning for firewall policy analysis and develop means to automatically generate firewall policies from higher-level requirements. This permits both analysis and generation to be done within the same framework, thus accommodating a wide variety of scenarios for authoring and maintaining firewall configurations. We validate our approach by applying it to both examples from the literature and real firewall configurations of moderate size (ap 150 rules).

We propose a framework that brings together two major forms of default reasoning in Artificial Intelligence: applying default property classification rules in static domains, and default persistence of properties in temporal domains. Particular attention is paid to the central problem of qualification. We illustrate how previous semantics developed independently for the two separate forms of default reasoning naturally lead to the integration that we propose, and how this gives rise to domains where different types of knowledge interact and qualify each other while preserving elaboration tolerance.

This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model. This model allows the specification of heterogeneous agents that can interact with each other, and can exhibit both proactive and reactive behaviour allowing them to function in dynamic environments by adjusting their goals and plans when changes happen in such environments. KGP provides a highly modular agent architecture that integrates a collection of reasoning and physical capabilities, synthesised within transitions that update the agent's state in response to reasoning, sensing and acting. Transitions are orchestrated by cycle theories that specify the order in which transitions are executed while taking into account the dynamic context and agent preferences, as well as selection operators for providing inputs to transitions.

This paper describes the use of a mixture of abduction and induction for the temporal modeling of the effects of toxins in metabolic networks. Background knowledge is used which describes network topology and functional classes of enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples were derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxin. Hypotheses about inhibition are built using the inductive logic programming system Progol5.0 and predictive accuracy is assessed for both the ground and the non-ground cases.

In recent years, argumentation has been shown to be an appropriate framework in which logic programming with negation as failure as well as other logics for non-monotonic reasoning can be encompassed. Many of the existing semantics for negation as failure in logic programming can be understood in a uniform way using argumentation. Moreover, other logics for non-monotonic reasoning that can also be formulated via argumentation can be given new semantics, by a direct extension of the logic programming semantics. In this paper we develop an abstract computational framework where various argumentation semantics can be computed via different parametric variations of a simple basic proof theory. This proof theory is given in terms of derivations of trees where each node in a tree contains an argument (or attack) against its corresponding parent node. The proposed proof theory, defined here for the case of logic programming, generalizes directly to other logics for non-monotonic reasoning that can also be formalized via argumentation. The abstract proof theory forms the basis for developing concrete top-down proof procedures for query evaluation. These proof procedures are obtained by adopting specific search strategies and ways of computing attacks in the particular argumentation framework. For logic programming these procedures can be seen as a generalization of the Eshghi-Kowalski abductive proof procedure that in turn generalizes SLDNF.

This paper is a survey and critical overview of recent work on the extension of logic programming to perform abductive reasoning (abductive logic programming). We outline the general framework of abduction and its applications to knowledge assimilation and default reasoning; and we introduce an argumentation-theoretic approach to the use of abduction as an interpretation for negation as failure. We also analyse the links between abduction and the extension of logic programming obtained by adding a form of explicit negation. Finally we discuss the relation between abduction and truth maintenance.