Publications
436 results found
Cocarascu O, Rago A, Toni F, 2019, Extracting dialogical explanations for review aggregations with argumentative dialogical agents, International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), Publisher: International Foundation for Autonomous Agents and Multiagent Systems
The aggregation of online reviews is fast becoming the chosen method of quality control for users in various domains, from retail to entertainment. Consequently, fair, thorough and explainable aggregation of reviews is increasingly sought-after. We consider the movie review domain, and in particular Rotten Tomatoes' ubiquitous (and arguably over-simplified) aggregation method, the Tomatometer Score (TS). For a movie, this amounts to the percentage of critics giving the movie a positive review. We define a novel form of argumentative dialogical agent (ADA) for explaining the reasoning within the reviews. ADA integrates: 1.) NLP with reviews to extract a Quantitative Bipolar Argumentation Framework (QBAF) for any chosen movie to provide the underlying structure of explanations, and 2.) gradual semantics for QBAFs for deriving a dialectical strength measure for movies, as an alternative to the TS, satisfying desirable properties for obtaining explanations. We evaluate ADA using some prominent NLP methods and gradual semantics for QBAFs. We show that they provide a dialectical strength which is comparable with the TS, while at the same time being able to provide dialogical explanations of why a movie obtained its strength via interactions between the user and ADA.
Zhong Q, Fan X, Luo X, et al., 2019, An explainable multi-attribute decision model based on argumentation, Expert Systems with Applications, Vol: 117, Pages: 42-61, ISSN: 0957-4174
We present a multi-attribute decision model and a method for explaining the decisions it recommends based on an argumentative reformulation of the model. Specifically, (i) we define a notion of best (i.e., minimally redundant) decisions amounting to achieving as many goals as possible and exhibiting as few redundant attributes as possible, and (ii) we generate explanations for why a decision is best or better than or as good as another, using a mapping between the given decision model and an argumentation framework, such that best decisions correspond to admissible sets of arguments. Concretely, natural language explanations are generated automatically from dispute trees sanctioning the admissibility of arguments. Throughout, we illustrate the power of our approach within a legal reasoning setting, where best decisions amount to past cases that are most similar to a given new, open case. Finally, we conduct an empirical evaluation of our method with legal practitioners, confirming that our method is effective for the choice of most similar past cases and helpful to understand automatically generated recommendations.
Baroni P, Rago A, Toni F, 2019, From fine-grained properties to broad principles for gradual argumentation: A principled spectrum, International Journal of Approximate Reasoning, Vol: 105, Pages: 252-286, ISSN: 0888-613X
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 novel 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 novel properties can be considered. Then, we provide a simplifying and unifying perspective for these groups of properties by showing that they are all implied by novel parametric principles of (either strict or non-strict) balance and monotonicity. Finally, we show that (instances of) these principles (and thus the group, literature and novel properties that they imply) are satisfied by several quantitative argumentation formalisms in the literature, thus confirming the principles' general validity and utility to support a compact, yet comprehensive, analysis of properties of gradual argumentation.
Rago A, 2019, Gradual Evaluation in Argumentation Frameworks: Methods, Properties and Applications
Gradual evaluation methods in argumentation frameworks provide semantics for assessing the gradual acceptance of arguments, differing from the qualitative semantics that have been used in argument evaluation since argumentation’s conception. These methods and their semantics are wide-ranging; they comprise those for group acceptance, probabilistic measures and game-theoretical strength, amongst many others. This affords numerous application areas and so the requisite behaviour for each needs to be justified by theoretical proofs of useful properties for a specific application.Our contributions to this field span three interweaving sub-categories, namely methods, properties and applications. For gradual evaluation methods, we develop a number of novel and useful methods themselves. For each method we detail the semantics’ and the frameworks’ definitions then undertake theoretical evaluations based on their properties, before applications targeting real-world problems are suggested for each method. As for gradual evaluation properties, we undertake a systematic analysis for this research landscape by first identifying groups of conceptually related properties in the literature and provide a simplifying and unifying perspective for these properties by showing that all the considered literature properties are implied by four, novel parametric principles. We then validate these principles by showing that they are satisfied by several quantitative argumentation formalisms in the literature. We also instantiate the extensive number of implied properties of these principles which are not present in the literature. These properties are also used to extract argumentation explanations for recommendations in recommender systems, a novel concept and application.
Kotonya N, Toni F, 2019, Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection, 6th Workshop on Argument Mining (ArgMining), Publisher: ASSOC COMPUTATIONAL LINGUISTICS-ACL, Pages: 156-166
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Cyras K, Domínguez J, Karamlou A, et al., 2019, ROAD2H: Learning Decision Support System for Low- and Middle-Income Countries., Publisher: AMIA
Hart MG, Hunter A, Hawkins N, et al., 2018, First-line treatments for people with single or multiple brain metastases, Cochrane Database of Systematic Reviews, Vol: 2018
© 2018 The Cochrane Collaboration. This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To compare the safety and efficacy of surgery, radiotherapy, and chemotherapy as first-line treatment for people with single or multiple brain metastases, either alone or in combination.
Cocarascu O, Toni F, 2018, Combining deep learning and argumentative reasoning for the analysis of social media textual content using small datasets, Computational Linguistics, Vol: 44, Pages: 833-858, ISSN: 0891-2017
The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analysing whether news headlines support tweets and whether reviews are deceptive by analysing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for Relation-based Argument Mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement towards a statement is a useful step towards determining its truthfulness. Furthermore we use our method for extracting Bipolar Argumentation Frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small datasets.
Popescu C, Cocarascu O, Toni F, 2018, A platform for crowdsourcing corpora for argumentative, The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI)
One problem that Argument Mining (AM) is facing is the difficultyof obtaining suitable annotated corpora. We propose a web-basedplatform, BookSafari, that allows crowdsourcing of annotated cor-pora forrelation-based AMfrom users providing reviews for booksand exchanging opinions about these reviews to facilitate argumen-tative dialogue. The annotations amount to pairwise argumentativerelations ofattackandsupportbetween opinions and between opin-ions and reviews. As a result of the annotations, reviews and opinionsform structured debates which can be understood as bipolar argu-mentation frameworks. The platform also empowers annotationsof the same pairs by multiple annotators and can support differentmeasures of inter-annotator agreement and corpora selection.
Cocarascu O, Cyras K, Rago A, et al., 2018, Explaining with Argumentation Frameworks Mined from Data, The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI)
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.
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