Publications
426 results found
Hunter A, Maudet N, Toni F, et al., 2018, Foreword to the Special Issue on supporting and explaining decision processes by means of argumentation, EURO JOURNAL ON DECISION PROCESSES, Vol: 6, Pages: 235-236, ISSN: 2193-9438
Toni F, 2018, Argumentation-based clinical decision support system in ROAD2H, Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine, ISSN: 1613-0073
© 2018 CEUR-WS. All rights reserved. The ROAD2H project aims to build a clinical decision support system integrating argumentation and optimisation techniques to reconcile guidelines providing conflicting recommendations for patients with comorbidities, and taking into account national and regional specificities and constraints imposed by local health ensurance schemes. Here I provide a high-level overview of the project.
Cyras K, Delaney B, Prociuk D, et al., 2018, Argumentation for explainable reasoning with conflicting medical recommendations, Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine (MedRACER 2018), Pages: 14-22
Designing a treatment path for a patient suffering from mul-tiple conditions involves merging and applying multiple clin-ical guidelines and is recognised as a difficult task. This isespecially relevant in the treatment of patients with multiplechronic diseases, such as chronic obstructive pulmonary dis-ease, because of the high risk of any treatment change havingpotentially lethal exacerbations. Clinical guidelines are typi-cally designed to assist a clinician in treating a single condi-tion with no general method for integrating them. Addition-ally, guidelines for different conditions may contain mutuallyconflicting recommendations with certain actions potentiallyleading to adverse effects. Finally, individual patient prefer-ences need to be respected when making decisions.In this work we present a description of an integrated frame-work and a system to execute conflicting clinical guidelinerecommendations by taking into account patient specific in-formation and preferences of various parties. Overall, ourframework combines a patient’s electronic health record datawith clinical guideline representation to obtain personalisedrecommendations, uses computational argumentation tech-niques to resolve conflicts among recommendations while re-specting preferences of various parties involved, if any, andyields conflict-free recommendations that are inspectable andexplainable. The system implementing our framework willallow for continuous learning by taking feedback from thedecision makers and integrating it within its pipeline.
Baroni P, Borsato S, Rago A, et al., 2018, The "Games of Argumentation" web platform, 7th International Conference on Computational Models of Argument (COMMA 2018), Publisher: IOS Press, Pages: 447-448, ISSN: 0922-6389
This demo presents the web system “Games of Argumentation”, which allows users to build argumentation graphs and examine them in a game-theoretical manner using up to three different evaluation techniques. The concurrent evaluations of arguments using different techniques, which may be qualitative or quantitative, provides a significant aid to users in both understanding game-theoretical argumentation semantics and pinpointing their differences from alternative semantics, traditional or otherwise, to differentiate between them.
Toni F, 2018, Machine Arguing: From Data and Rules to Argumentation Frameworks, 7th International Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 3-3, ISSN: 0922-6389
Rago A, Baroni P, Toni F, 2018, On instantiating generalised properties of gradual argumentation frameworks, SUM 2018, Publisher: Springer Verlag, Pages: 243-259, ISSN: 0302-9743
Several gradual semantics for abstract and bipolar argumentation have been proposed in the literature, ascribing to each argument a value taken from a scale, i.e. an ordered set. These values somewhat match the arguments’ dialectical status and provide an indication of their dialectical strength, in the context of the given argumentation framework. These research efforts have been complemented by formulations of several properties that these gradual semantics may satisfy. More recently a synthesis of many literature properties into more general groupings based on parametric definitions has been proposed. In this paper we show how this generalised parametric formulation enables the identification of new properties not previously considered in the literature and discuss their usefulness to capture alternative requirements coming from different application contexts.
Schulz C, Toni F, 2018, On the responsibility for undecisiveness in preferred and stable labellings in abstract argumentation, Artificial Intelligence, Vol: 262, Pages: 301-335, ISSN: 1872-7921
Different semantics of abstract Argumentation Frameworks (AFs) provide different levels of decisiveness for reasoning about the acceptability of conflicting arguments. The stable semantics is useful for applications requiring a high level of decisiveness, as it assigns to each argument the label “accepted” or the label “rejected”. Unfortunately, stable labellings are not guaranteed to exist, thus raising the question as to which parts of AFs are responsible for the non-existence. In this paper, we address this question by investigating a more general question concerning preferred labellings (which may be less decisive than stable labellings but are always guaranteed to exist), namely why a given preferred labelling may not be stable and thus undecided on some arguments. In particular, (1) we give various characterisations of parts of an AF, based on the given preferred labelling, and (2) we show that these parts are indeed responsible for the undecisiveness if the preferred labelling is not stable. We then use these characterisations to explain the non-existence of stable labellings. We present two types of characterisations, based on labellings that are more (or equally) committed than the given preferred labelling on the one hand, and based on the structure of the given AF on the other, and compare the respective AF parts deemed responsible. To prove that our characterisations indeed yield responsible parts, we use a notion of enforcement of labels through structural revision, by means of which the preferred labelling of the given AF can be turned into a stable labelling of the structurally revised AF. Rather than prescribing how this structural revision is carried out, we focus on the enforcement of labels and leave the engineering of the revision open to fulfil differing requirements of applications and information available to users.
Cocarascu O, Cyras K, Toni F, 2018, Explanatory predictions with artificial neural networks and argumentation, Workshop on Explainable Artificial Intelligence (XAI)
Data-centric AI has proven successful in severaldomains, but its outputs are often hard to explain.We present an architecture combining ArtificialNeural Networks (ANNs) for feature selection andan instance of Abstract Argumentation (AA) forreasoning to provide effective predictions, explain-able both dialectically and logically. In particular,we train an autoencoder to rank features in input ex-amples, and select highest-ranked features to gen-erate an AA framework that can be used for mak-ing and explaining predictions as well as mappedonto logical rules, which can equivalently be usedfor making predictions and for explaining.Weshow empirically that our method significantly out-performs ANNs and a decision-tree-based methodfrom which logical rules can also be extracted.
Rago A, Cocarascu O, Toni F, 2018, Argumentation-based recommendations: fantastic explanations and how to find them, The Twenty-Seventh International Joint Conference on Artificial Intelligence, (IJCAI 2018), Pages: 1949-1955
A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.
Rago A, Baroni P, Toni F, 2018, Scalable uncertainty management, Scalable Uncertainty Management (SUM 2018), Publisher: Springer Verlag, ISSN: 0302-9743
Several gradual semantics for abstract and bipolar argumentation have been proposed in the literature, ascribing to each argument a value taken from a scale, i.e. an ordered set. These values somewhat match the arguments’ dialectical status and provide an indication of their dialectical strength, in the context of the given argumentation framework. These research efforts have been complemented by formulations of several properties that these gradual semantics may satisfy. More recently a synthesis of many literature properties into more general groupings based on parametric definitions has been proposed. In this paper we show how this generalised parametric formulation enables the identification of new properties not previously considered in the literature and discuss their usefulness to capture alternative requirements coming from different application contexts.
Baroni P, Rago A, Toni F, 2018, How many Properties do we need for Gradual Argumentation?, AAAI 2018, Publisher: AAAI
The study of properties of gradual evaluation methods inargumentation has received increasing attention in recentyears, with studies devoted to various classes of frame-works/methods leading to conceptually similar but formallydistinct properties in different contexts. In this paper we pro-vide a systematic analysis for this research landscape by mak-ing three main contributions. First, we identify groups of con-ceptually related properties in the literature, which can be re-garded as based on common patterns and, using these pat-terns, we evidence that many further properties can be consid-ered. Then, we provide a simplifying and unifying perspec-tive for these properties by showing that they are all impliedby the parametric principles of (either strict or non-strict) bal-ance and monotonicity. Finally, we show that (instances of)these principles are satisfied by several quantitative argumen-tation formalisms in the literature, thus confirming their gen-eral validity and their utility to support a compact, yet com-prehensive, analysis of properties of gradual argumentation.
Baroni P, Borsato S, Rago A, et al., 2018, The "Games of Argumentation" Web Platform., Publisher: IOS Press, Pages: 447-448
, 2018, Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018., Publisher: AAAI Press
Baroni P, Comini G, Rago A, et al., 2017, Abstract Games of Argumentation Strategy and Game-Theoretical Argument Strength, PRIMA, Publisher: Springer, Pages: 403-419, ISSN: 0302-9743
We define a generic notion of abstract games of argumentation strategy for (attack-only and bipolar) argumentation frameworks, which are zero-sum games whereby two players put forward sets of arguments and get a reward for their combined choices. The value of these games, in the classical game-theoretic sense, can be used to define measures of (quantitative) game-theoretic strength of arguments, which are different depending on whether either or both players have an “agenda” (i.e. an argument they want to be accepted). We show that this general scheme captures as a special instance a previous proposal in the literature (single agenda, attack-only frameworks), and seamlessly supports the definition of a spectrum of novel measures of game-theoretic strength where both players have an agenda and/or bipolar frameworks are considered. We then discuss the applicability of these instances of game-theoretic strength in different contexts and analyse their basic properties.
Rago A, Toni F, 2017, Quantitative Argumentation Debates with Votes for Opinion Polling, PRIMA, Publisher: Springer, Pages: 369-385, ISSN: 0302-9743
Opinion polls are used in a variety of settings to assess the opinions of a population, but they mostly conceal the reasoning behind these opinions. Argumentation, as understood in AI, can be used to evaluate opinions in dialectical exchanges, transparently articulating the reasoning behind the opinions. We give a method integrating argumentation within opinion polling to empower voters to add new statements that render their opinions in the polls individually rational while at the same time justifying them. We then show how these poll results can be amalgamated to give a collectively rational set of voters in an argumentation framework. Our method relies upon Quantitative Argumentation Debate for Voting (QuAD-V) frameworks, which extend QuAD frameworks (a form of bipolar argumentation frameworks in which arguments have an intrinsic strength) with votes expressing individuals’ opinions on arguments.
Cyras K, Schulz C, Toni F, 2017, Capturing Bipolar Argumentation in Non-flat Assumption-Based Argumentation, PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Publisher: Springer Verlag, Pages: 386-402, ISSN: 0302-9743
Bipolar Argumentation Frameworks (BAFs) encompass both attacks and supports among arguments. We study different semantic interpretations of support in BAFs, particularly necessary and deductive support, as well as argument coalitions and a recent proposal by Gabbay. We analyse the relationship of these different notions of support in BAFs with the semantics of a well established structured argumentation formalism, Assumption-Based Argumentation (ABA), which predates BAFs. We propose natural mappings from BAFs into a restricted class of (non-flat) ABA frameworks, which we call bipolar, and prove that the admissible and preferred semantics of these ABA frameworks correspond to the admissible and preferred semantics of the various approaches to BAFs. Motivated by the definition of stable semantics for BAFs, we introduce a novel set-stable semantics for ABA frameworks, and prove that it corresponds to the stable semantics of the various approaches to BAFs. Finally, as a by-product of modelling various approaches to BAFs in bipolar ABA, we identify precise semantic relationships amongst all approaches we consider.
Bao Z, Cyras K, Toni F, 2017, ABAplus: Attack Reversal in Abstract and Structured Argumentation with Preferences, PRIMA 2017: The 20th International Conference on Principles and Practice of Multi-Agent Systems, Publisher: Springer Verlag, ISSN: 0302-9743
We present ABAplus, a system that implements reasoningwith the argumentation formalism ABA+. ABA+ is a structured argumentationformalism that extends Assumption-Based Argumentation(ABA) with preferences and accounts for preferences via attack reversal.ABA+ also admits as instance Preference-based Argumentation whichaccounts for preferences by reversing attacks in abstract argumentation(AA). ABAplus readily implements attack reversal in both AA and ABAstylestructured argumentation. ABAplus affords computation, visualisationand comparison of extensions under five argumentation semantics.It is available both as a stand-alone system and as a web application.
Cyras K, Schulz C, Toni F, et al., 2017, Assumption-based argumentation: disputes, explanations, preferences, Journal of Applied Logics - IfCoLoG Journal of Logics and their Applications, Vol: 4, Pages: 2407-2455, ISSN: 2055-3706
Cocarascu O, Toni F, 2017, Identifying attack and support argumentative relations using deep learning, 2017 Conference on Empirical Methods in Natural Language Processing, Publisher: Association for Computational Linguistics, Pages: 1374-1379
We propose a deep learning architecture tocapture argumentative relations ofattackandsupportfrom one piece of text to an-other, of the kind that naturally occur ina debate. The architecture uses two (uni-directional or bidirectional) Long Short-Term Memory networks and (trained ornon-trained) word embeddings, and al-lows to considerably improve upon exist-ing techniques that use syntactic featuresand supervised classifiers for the sameform of (relation-based) argument mining.
Kakas A, Mancarella P, Toni F, 2017, On argumentation logic and propositional logic, Studia Logica, Vol: 106, Pages: 237-279, ISSN: 1572-8730
This paper studies the relationship between Argumentation Logic (AL), a recently defined logic based on the study of argumentation in AI, and classical Propositional Logic (PL). In particular, it shows that AL and PL are logically equivalent in that they have the same entailment relation from any given classically consistent theory. This equivalence follows from a correspondence between the non-acceptability of (arguments for) sentences in AL and Natural Deduction (ND) proofs of the complement of these sentences. The proof of this equivalence uses a restricted form of ND proofs, where hypotheses in the application of the Reductio of Absurdum inference rule are required to be “relevant” to the absurdity derived in the rule. The paper also discusses how the argumentative re-interpretation of PL could help control the application of ex-falso quodlibet in the presence of inconsistencies.
Carstens L, Toni F, 2017, Using argumentation to improve classification in natural language problems, ACM Transactions on Internet Technology, Vol: 17, ISSN: 1557-6051
Argumentation has proven successful in a number of domains, including Multi-Agent Systems and decision supportin medicine and engineering. We propose its application to a domain yet largely unexplored by argumentation re-search: Computational linguistics. We have developed a novel classification methodology that incorporates reasoningthrough argumentation with supervised learning. We train classifiers and thenargueabout the validity of their out-put. To do so we identify arguments that formalise prototypical knowledge of a problem and use them to correctmisclassifications. We illustrate our methodology on two tasks. On the one hand we addresscross-domain sentimentpolarity classification, where we train classifiers on one corpus, e.g. Tweets, to identify positive/negative polarity,and classify instances from another corpus, e.g. sentences from movie reviews. On the other hand we address a formof argumentation mining that we callRelation-based Argumentation Mining, where we classify pairs of sentencesbased on whether the first sentence attacks or supports the second, or whether it does neither. Whenever we findthat one sentence attacks/supports the other we consider both to be argumentative, irrespective of their stand-aloneargumentativeness. For both tasks we improve classification performance when using our methodology, compared tousing standard classifiers only.
Toni F, 2017, From logic programming and non-monotonic reasoning to computational argumentation and beyond, International Conference on Logic Programming and Nonmonotonic Reasoning, Pages: 36-39, ISSN: 0302-9743
© Springer International Publishing AG 2017. Argumentation has gained popularity in AI in recent years to support several activities and forms of reasoning. This talk will trace back the logic programming and non-monotonic reasoning origins of two well-known argumentation formalisms in AI (namely abstract argumentation and assumption-based argumentation). Finally, the talk will discuss recent developments in AI making use of computational argumentation, in particular to support collaborative decision making.
Cocarascu O, Toni F, 2017, Mining bipolar argumentation frameworks from natural language text, 17th Workshop on Computational Models of Natural Argument, Pages: 65-70
We describe a methodology for mining topic-dependent BipolarArgumentation Frameworks (BAFs) from natural language text.Our focus is on identifying attack and support argumentative re-lations between texts about the same topic, treating these texts asarguments when they are argumentatively related to other texts.We illustrate our methodology on a dataset of hotel reviews andoutline some possible applications using the BAFs resulting fromour methodology.
Schulz C, Toni F, 2017, Labellings for assumption-based and abstract argumentation, International Journal of Approximate Reasoning, Vol: 84, Pages: 110-149, ISSN: 1873-4731
The semantics of Assumption-Based Argumentation (ABA) frameworks are traditionally characterised as assumption extensions, i.e. sets of accepted assumptions. Assumption labellings are an alternative way to express the semantics of flat ABA frameworks, where one of the labels in, out, or undec is assigned to each assumption. They are beneficial for applications where it is important to distinguish not only between accepted and non-accepted assumptions, but further divide the non-accepted assumptions into those which are clearly rejected and those which are neither accepted nor rejected and thus undecided. We prove one-to-one correspondences between assumption labellings and extensions for the admissible, grounded, complete, preferred, ideal, semi-stable and stable semantics. We also show how the definition of assumption labellings for flat ABA frameworks can be extended to assumption labellings for any (flat and non-flat) ABA framework, enabling reasoning with a wider range of scenarios. Since flat ABA frameworks are structured instances of Abstract Argumentation (AA) frameworks, we furthermore investigate the relation between assumption labellings for flat ABA frameworks and argument labellings for AA frameworks. Building upon prior work on complete assumption and argument labellings, we prove one-to-one correspondences between grounded, preferred, ideal, and stable assumption and argument labellings, and a one-to-many correspondence between admissible assumption and argument labellings. Inspired by the notion of admissible assumption labellings we introduce committed admissible argument labellings for AA frameworks, which correspond more closely to admissible assumption labellings of ABA frameworks than admissible argument labellings do.
Cocarascu O, Toni F, 2016, Argumentation for machine learning: a survey, 6th International Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 219-230, ISSN: 0922-6389
Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future.
Cocarascu O, Toni F, 2016, A System for Supporting the Detection of Deceptive Reviews Using Argument Mining, 6th International Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 469-470, ISSN: 0922-6389
The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. We propose a system to identify two new argumentative features that a trained classifier can use to help determine whether a review is deceptive.
Cyras K, Satoh K, Toni F, 2016, Explanation for case-based reasoning via abstract argumentation, 6th International Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 243-254, ISSN: 0922-6389
Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent.
Cyras K, Rago A, Toni F, 2016, Adapting the DF-QuAD Algorithm to Bipolar Argumentation, First International Workshop on Systems and Algorithms for Formal Argumentation (SAFA), Publisher: CEUR, Pages: 34-39, ISSN: 1613-0073
We define a quantitative semantics for evaluating the strength of arguments in Bipolar Argumentation frameworks (BAFs) by adapting the Discontinuity-Free QuAD (DF-QuAD) algorithm previously used for evaluating the strength of arguments in Quantitative Argumentation Debates (QuAD) frameworks. We study the relationship between the new semantics and some existing semantics for other argumentation frameworks, as well as some properties of the semantics.
Cerutti F, Palmer A, Rosenfeld A, et al., 2016, A pilot study in using argumentation frameworks for online debates, Systems and Algorithms for Formal Argumentation (SAFA 2016), Publisher: CEUR, Pages: 63-74, ISSN: 1613-0073
We describe a pilot study in using argumentation frameworks obtained from an online debate to evaluate positions expressed in the debate. This pilot study aims at exploring the richness of Computational Argumentation methods and techniques for evaluating arguments to reason with the output of Argument Mining. It uses a hand-generated graphical representation of the debate as an intermediate representation from which argumentation frameworks can be extracted, but richer than any existing argumentation framework. The intermediate representation can provide insights for benchmark sets derived from online debates.
Cocarascu O, Toni F, 2016, Detecting deceptive reviews using argumentation, Conference on Empirical Methods in Natural Language Processing EMNLP 2017, Publisher: ACM
The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. In this paper we use Argumentation Frameworks (AFs) extracted from reviews and explore whether the use of these AFs can improve the performance of machine learning techniques in detecting deceptive behaviour, resulting from users lying in order to mislead readers. The AFs represent how arguments from reviews relate to arguments from other reviews as well as to arguments about the goodness of the items being reviewed.
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