We propose a general model explanation system (MES) for "explaining" the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key aspect is that we provide explanations applicable to a single prediction, rather than provide an interpretable set of parameters. We focus on explaining positive predictions (alerts). However, the presented methodology is symmetrically applicable to negative predictions.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC+ framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC+ framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Thus in effect Rescher & Manor's paraconsistent notion of weak consequence is embedded in ASPIC*. Another issue is minimality of arguments. If arguments can apply defeasible inference rules, then they cannot be required to have subset-minimal premises, since defeasible rules based on more information may well make an argument stronger. In this paper instead minimality is required of applications of strict rules throughout an argument. It is shown that under some plausible assumptions this does not affect the set of conclusions. In addition, circular arguments are in the new ASPIC* framework excluded in a way that satisfies closure and consistency postulates and that generates finitary argumentation frameworks if the knowledge base and set of defeasible rules are finite. For the latter result the exclusion of chaining of strict rules is essential. Finally, the combined results of this paper are shown to be a proper extension of classical-logic argumentation with preferences and defeasible rules.

Adding weights or preferences to Abstract Argumentation Frameworks can help disentangle semantics from otherwise all-equivalent attacks. Having such information makes possible to distil the set of found extensions by reducing their number. In this work we provide a new definition of weighted defence: according to it, all the attacks from an argument to a set of arguments are considered with a single global weight, i.e., attacks are grouped together. This provides a coherent view w.r.t. defence, which is usually “collective” in the literature. Moreover, we model weighted defences from related works in the same algebraic framework: this allows us to compare all the different proposals together.

We present a novel approach to account for preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA). The new formalism, called ABA+, incorporates object-level preferences (over assumptions) directly into the attack relation to reverse attacks. We give several basic desirable properties of ABA+.

In this report from the field we describe jArgSemSAT, a Java re-implementation of ArgSemSAT. We show that jArgSemSAT can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. The performance section shows that — despite being written in Java — jArgSemSAT is very efficient w.r.t. preferred semantics, which has associated problems with high computational complexity.

In the study of argumentation-based reasoning, argument justification has received far more attention than statement justification, often treated as a simple byproduct of the former. As a consequence, counterintuitive results and significant losses of sensitivity can be identified in the treatment of statement justification by otherwise appealing formalisms. To overcome this limitation, we propose to reappraise statement justification as a formalism-independent component. To this purpose, we introduce a novel general model of argumentation-based reasoning based on multiple levels of labellings, one of which is devoted to statement justification. This model is able to encompass several literature proposals as special cases: we illustrate this ability for the case of the ASPIC+ formalism and provide a first example of tunable statement justification in this context.

Reasoning with the maximally consistent subsets (MCS) of the premises is awell-known approach for handling contradictory information. We introduce two argumentation-based methods for doing so: a declarative approach that is related to Dung-style semantics for abstract argumentation, and a computational approach that is based on extensions of Gentzen-type proofs systems. This brings about a new perspective on reasoning with MCS which shows a strong link between the latter and argumentation systems, and which can be extended to related formalisms. A by-product of this is the introduction of a dynamic proof system for classical logic and rebuttal attacks, which is sound and complete with respect to Dung's stable semantics for the associated argumentation framework.

The formalisms for knowledge representation and reasoning(KR&R) typically have a variety of semantics, each one having its particular application scenarios. However, the KR&Rcommunity cannot readily benefit from such a variety due toa lack of efficient solver technology. This is partly caused bythe fact that solver development is laborious and even accomplishing a working prototype can form a major effort.In this paper, we introduce a new framework that enables us todeclaratively specify a given semantics in second-order logicand to automatically generate a solver from that specification. Hence, KR&R researchers can rapidly develop a solverprototype for their new/existing semantics with a minimal effort. Technically, our framework builds on a recent approachfor nesting SAT solvers based on lazy clause generation.We evaluate our framework in the context of Dung’s argumentation frameworks, logic programming, and propositionallogic subject to standard and non-standard semantics. Weshow for each of those formalisms that one can easily specify its semantics using a few second-order sentences and thatone can effectively obtain a solver for that semantics usingour automated solver generation procedure.For instance, in the case of argumentation frameworks, weobtain 16 different solvers, each solving one of four inference tasks for one of four major argumentation semantics andshow that our solvers (slightly) outperform the best solverfrom the last system competition despite not being tuned forargumentation instances.

IBIS (Issue Based Information System) provides a widely adopted approach for knowledge representation especially suitable for the challenging task of representing wicked decision problems. While many tools for visualisation and collaborative development of IBIS graphs are available, automated decision support in this context is still underdeveloped, even though it would benefit several applications. QuAD (Quantitative Argumentation Debate) frameworks are a recently proposed IBIS-based formalism encompassing automated decision support by means of an algorithm for quantifying the strength of alternative decision options, based on aggregation of the strength of their attacking and supporting arguments. The initially proposed aggregation method, however, may give rise to discontinuities. In this paper we propose a novel, discontinuity-free algorithm for computing the strength of decision options in QuAD frameworks. We prove that this algorithm features several desirable properties and we compare the two aggregation methods, showing that both may be appropriate in the context of different application scenarios.

We provide new insights into the area of combining abstract argumentation frameworks with probabilistic reasoning. In particular, we consider the scenario when assessments on the probabilities of a subset of the arguments is given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalize this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistent-tolerant reasoning.