If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
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The study of properties of gradual evaluation methods in argumentation has received increasing attention in recent years, with studies devoted to various classes of frameworks/methods leading to conceptually similar but formally distinct properties in different contexts. In this paper we provide a systematic analysis for this research landscape by making three main contributions. First, we identify groups of conceptually related properties in the literature, which can be regarded as based on common patterns and, using these patterns, we evidence that many further properties can be considered. Then, we provide a simplifying and unifying perspective for these properties by showing that they are all implied by the parametric principles of (either strict or non-strict) balance and monotonicity. Finally, we show that (instances of) these principles are satisfied by several quantitative argumentation formalisms in the literature, thus confirming their general validity and their utility to support a compact, yet comprehensive, analysis of properties of gradual argumentation.
Atkinson, Katie (University of Liverpool) | Baroni, Pietro (Università degli Studi di Brescia) | Giacomin, Massimiliano (Università degli Studi di Brescia) | Hunter, Anthony (University College London) | Prakken, Henry (Utrecht University) | Reed, Chris (University of Dundee) | Simari, Guillermo (Universidad Nacional del Sur) | Thimm, Matthias (Universität Koblenz-Landau) | Villata, Serena (Université Côte d'Azur)
The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to retrieve (by derivation) multiple statements (sometimes assumed) or results from the literature.
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.
Baroni, Pietro (Università degli Studi di Brescia) | Governatori, Guido (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO)) | Lam, Ho-Pun (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO)) | Riveret, Régis (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO))
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.
The adoption of a generic contrariness notion in ASPIC+ substantially enhances its expressiveness with respect to other formalisms for structured argumentation. In particular, it opens the way to novel investigation directions, like the use of multivalued logics in the construction of arguments. This paper points out however that in the current version of ASPIC+ a serious technical difficulty related with generic contrariness is present. With the aim of preserving the same level of generality, the paper provides a solution based on a novel notion of closure of the contrariness relation at the level of sets of formulas and an abstract representation of conflicts between sets of arguments. The proposed solution is shown to satisfy the same rationality postulates as ASPIC+ and represents a starting point for further technical and conceptual developments in structured argumentation.
This paper describes a preliminary proposal of an argumentation-based approach to modeling articulated decision support contexts. The proposed approach encompasses a variety of argument and attack schemes aimed at representing basic knowledge and reasoning patterns for decision support. Some of the defined attack schemes involve attacks directed towards other attacks, which are not allowed in traditional argumentation frameworks but turn out to be useful as a knowledge and reasoning modeling tool: in particular, we demonstrate their use to support what-if reasoning capabilities, which are of primary importance in decision support. Formal backing to this approach is provided by the AFRA formalism, a recently proposed extension of Dung’s argumentation framework. A literature example concerning a decision problem about medical treatments is adopted to illustrate the approach.
In the context of Dung's theory of abstract argumentation frameworks, the recently introduced resolution-based grounded semantics features the unique property of fully complying with a set of general requirements, only partially satisfied by previous literature proposals. This paper contributes to the investigation of resolution-based grounded semantics by analyzing its computational properties with reference to a standard set of decision problems for abstract argumentation semantics: (a) checking the property of being an extension for a set of arguments; (b) checking agreement with traditional grounded semantics; (c) checking the existence of a non-empty extension; (d) checking credulous acceptance of an argument; (e) checking skeptical acceptance of an argument. It is shown that problems (a)-(c) admit polynomial time decision processes, while (d) is NP-complete and (e) coNP-complete.