Some design guidelines for practical argumentation systems

A key ingredient of computational argumentation in AI is the generation of arguments in favor of or against claims under scrutiny. In this paper we look at the complexity of argument construction and reasoning in the prominent structured formalism of assumption-based argumentation (ABA). We point out that reasoning in ABA by means of constructing an abstract argumentation framework (AF) gives rise to two main sources of complexity: (i) constructing the AF and (ii) reasoning within the constructed graph. Since both steps are intractable in general, it is no surprise that the best performing state-of-the-art ABA reasoners skip the instantiation procedure entirely and perform tasks directly on the input knowledge base. Driven by this observation, we identify and study atomic and symmetric ABA, two ABA fragments that preserve the expressive power of general ABA, and that can be utilized to have milder complexity in the first or second step. We show that using atomic ABA allows for an instantiation procedure for general ABA leading to polynomially-bounded AFs and that symmetric ABA can be used to create AFs that have mild complexity to reason on. By an experimental evaluation, we show that using the former approach with modern AF solvers can be competitive with state-of-the-art ABA solvers, improving on previous AF instantiation approaches that are hindered by intractable argument construction.

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

Assumption-based argumentation can serve as an effective computational tool for argumentation-based epistemic and practical reasoning, as required in a number of applications. In this paper we substantiate this claim by presenting formal mappings from frameworks for epistemic and practical reasoning onto assumption-based argumentation frameworks. We also correlate these mappings to formulations of epistemic and practical reasoning in abstract argumentation terms.KeywordsPractical ReasoningInference RuleLogic ProgrammingDeductive SystemStrict RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The information available to intelligent agents is often uncertain, inconsistent and incomplete. It is then crucially important to develop logics for uncertainty to achieve different complex reasoning tasks. Uncertainty is present in many applications and may be represented using different frameworks: plausibility orderings, probability theory frameworks, argumentation systems, etc. This special issue gathers five contributions that cover different aspects of reasoning under uncertainty using non classical logics. Four of the five papers are fully revised and extended versions of contributions initially presented at the Fifth International Conference on Scalable Uncertainty Management (SUM’11) The two first papers concern extensions of logic-based programming languages to deal with uncertain information. The paper by Pere Pardo, Lluis Godo entitled “t-DeLP: an argumentation-based Temporal Defeasible Logic Programming framework” deals with handling temporal information using defeasible logic. The proposed framework, called t-DeLP, allows to extend argumentation-based defeasible logic in order to represent temporal processes. The idea is to associate temporal information to literals and to define temporal logic programs are sets of basic temporal facts and (strict or defeasible) durative rules. Concepts of argumentation systems, such as the one of undefeated arguments, are redefined in this t-DeLP framework. A procedure for determining a set of warranted literals is proposed. The paper also discusses rational properties satisfied by the t-DeLP framework. The paper by Georg Gottlob,Thomas Lukasiewicz, Maria Vanina Martinez and Gerardo I. Simari entitled “Query Answering under Probabilistic Uncertainty in Datalog+/ Ontologies” concerns an extension of Datalog+/− to deal with uncertainty. The authors use Markov Logic Networks to provide a probabilistic semantics for the probabilistic Datalog+/−. They provide scalable and sound algorithms for

Computational argumentation is primed to strengthen the current hot research field of Explainable Artificial Intelligence (XAI), e.g., by dialectical approaches. In this paper, we extend and discuss a recently proposed approach of so-called strong acceptance on abstract argumentation that aims to support explaining argumentative acceptance. Our goal is to push these results into the realm of structured argumentation. In this setting, a knowledge base induces an abstract argumentation framework (AF) via instantiation. We investigate how and under which conditions it is possible to transfer results regarding strong acceptance between the given knowledge base and the induced AF. To this end we consider generic functions formalizing the interaction of the AF and the knowledge base. This approach helps us to infer rather general results making basic assumptions rather than dealing with the technical details of several structured argumentation formalisms. Along the way, we apply our techniques to the concrete approach of assumption-based argumentation (ABA) which constitutes one of the primal structured argumentation formalisms.

Encoding deductive argumentation in quantified Boolean formulae

Conflicts exist in multi-agent systems for a number of reasons: agents have different interests and desires; agents hold different beliefs; agents make different assumptions. To resolve conflicts, agents need to better convey information to each other and facilitate fair negotiations yielding jointly agreeable outcomes. We present a two-agent, dialogical conflict resolution scheme developed with the Assumption-Based Argumentation (ABA) framework.

We introduce a novel method to aggregate Bipolar Argumentation (BA) Frameworks expressing opinions by different parties in debates. We use Bipolar Assumption-based Argumentation (ABA) as an all-encompassing formalism for BA under different semantics. By leveraging on recent results on judgement aggregation in Social Choice Theory, we prove several preservation results, both positive and negative, for relevant properties of Bipolar ABA.

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.

Argumentation systems are useful instruments for decision support when the arguments are changed dynamically in dependence of the users and the situation. In argumentation systems different strategies are used when the system prepares the argumentation for a given situation and/or user. These strategies correspond to some many valued logics and some of these strategies have fuzzy and rough interpretations. Many information systems have interpretation in different argumentation systems. For the lower and upper approximations there are equivalent argumentation strategies that correspond to the well known logical systems. Moreover when a practical system chooses the arguments for the user it needs more compact argumentation then the list of all procedures it uses. Then the argumentation system finds arguments with the same persuade power as its argumentation.

The existing approaches which map the given explicit preferences into standard assumption-based argumentation frameworks (ABAs) reveal some difficulties such as generating a huge number of rules and so on. To overcome them, we present an assumption-based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABAs is given by \(\mathcal{P}\) extensions selected from extensions of ABAs based on the given sentence ordering. The advantages of our approach are that not only it enables us to express different kinds of preferences such as preferences over rules, over goals, over decisions by means of sentence orderings in p_ABAs but we also successfully obtain solutions from \(\mathcal{P}\) extensions of the p_ABAs expressing the respective knowledge for various applications such as epistemic reasoning, practical reasoning, decision-making with preferences and so on in a uniform way without suffering from difficulties of existing ones.

In this paper, we extend the abstract argumentation framework proposed by [1] in order to take into account two kinds of interaction between arguments: a positive interaction (an argument can help, support another argument) and a negative interaction (an argument can attack another argument). In this new abstract argumentation framework, called a bipolar argumentation framework, we propose a gradual interaction-based valuation process. With this process, the value of each argument A only depends on the value of the arguments which are directly interacting with A in the argumentation system.

Based on Dung’s argumentation framework, using Dung’s extended argumentation semantics, importing reasoning argument and fact argument, design and implementing Jess-based argumentative E-commerce system, the system has important significance and high reference value for studying E-commerce argumentative negotiation systems.

This paper addresses the issue of the dynamic enforcement of a constraint in an argumentation system. The system consists in (1) an argumentation framework, made up, notably, of a set of arguments and of an attack relation, (2) an evaluation semantics, and (3) the evaluation result, computed from (1) and (2). An agent may want another agent to consider a new attack, or to have a given argument accepted, or even to relax the definition of the semantics. A constraint on any of the three components is thus defined, and it has to be enforced in the system. The enforcement may result in changes on components of the system. The paper surveys existing approaches for the dynamic enforcement of a constraint and its consequences, and reveals challenging enforcement cases that remain to be investigated.

In this paper we further progress the analysis of the recently introduced multi-valued argumentation frameworks (MVAFs). MVAFs are an extension of Dung's abstract argumentation, where arguments have a degree of truth associated with them. Here we describe a list of properties of MVAFs considering the major multi-valued logics such as those proposed by Gödel, Zadeh and Łukasiewicz. We then propose a computational framework that joins multi-valued and probabilistic argumentation frameworks to handle situations where arguments affected by vagueness and/or probabilities coexist. The findings are a contribution to the field of non-monotonic approximate reasoning and they also represent a well-grounded proposal towards the introduction of gradualism in argumentation systems.