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  • Conference paper
    Tirsi C-G, Proietti M, Toni F, 2023,

    ABALearn: an automated logic-based learning system for ABA frameworks

    , AIxIA 2023, Publisher: Springer Nature, ISSN: 1687-7470

    We introduce ABALearn, an automated algorithm that learns Assumption-Based Argumentation (ABA) frameworks from training data consisting of positive and negative examples, and a given background knowledge. ABALearn’s ability to generate comprehensible rules for decision-making promotes transparency and interpretability, addressing the challenges associated with the black-box nature of traditional machine learning models. This implementation is based on the strategy proposed in a previous work. The resulting ABA frameworks can be mapped onto logicprograms with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrat-ing its potential to be used in real-world applications. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) andprovides insights into how such learners can be applied to make predictions.
  • Conference paper
    Paulino Passos G, Toni F, 2023,

    Learning Case Relevance in Case-Based Reasoning with Abstract Argumentation

    , The 36th International Conference on Legal Knowledge and Information Systems
  • Conference paper
    Yin X, Potyka N, Toni F, 2023,

    Argument attribution explanations in quantitative bipolar argumentation frameworks

    , 26th European Conference on Artificial Intelligence ECAI 2023, Publisher: IOS Press, Pages: 2898-2905, ISSN: 0922-6389

    Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.
  • Conference paper
    Russo F, Toni F, 2023,

    Causal discovery and knowledge injection for contestable neural networks

    , 26th European Conference on Artificial Intelligence ECAI 2023, Publisher: IOS Press, Pages: 2025-2032, ISSN: 0922-6389

    Neural networks have proven to be effective at solvingmachine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novelmethod overcoming these issues by allowing a two-way interactionwhereby neural-network-empowered machines can expose the underpinning learnt causal graphs and humans can contest the machinesby modifying the causal graphs before re-injecting them into the machines, so that the learnt models are guaranteed to conform to thegraphs and adhere to expert knowledge (some of which can also begiven up-front). By building a window into the model behaviour andenabling knowledge injection, our method allows practitioners to debug networks based on the causal structure discovered from the dataand underpinning the predictions. Experiments with real and synthetic tabular data show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7xsmaller in the input layer, compared to SOTA regularised networks.
  • Conference paper
    Rago A, Gorur D, Toni F, 2023,

    ArguCast: a system for online multi-forecasting with gradual argumentation

    , Knowledge Representation 2023, Publisher: CEUR-WS.org, Pages: 40-51

    Judgmental forecasting is a form of forecasting which employs (human) users to make predictions about specied future events. Judgmental forecasting has been shown to perform better than quantitative methods for forecasting, e.g. when historical data is unavailable or causal reasoning is needed. However, it has a number of limitations, arising from users’ irrationality and cognitive biases. To mitigate against these phenomena, we leverage on computational argumentation, a eld which excels in the representation and resolution of conicting knowledge and human-like reasoning, and propose novel ArguCast frameworks (ACFs) and the novel online system ArguCast, integrating ACFs. ACFs and ArguCast accommodate multi-forecasting, by allowing multiple users to debate on multiple forecasting predictions simultaneously, each potentially admitting multiple outcomes. Finally, we propose a novel notion of user rationality in ACFs based on votes on arguments in ACFs, allowing the ltering out of irrational opinions before obtaining group forecasting predictions by means commonly used in judgmental forecasting.
  • Conference paper
    Leofante F, Botoeva E, Rajani V, 2023,

    Counterfactual explanations and model multiplicity: a relational verification view

    , The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023), Publisher: IJCAI Organization, Pages: 763-768, ISSN: 2334-1033

    We study the interplay between counterfactual explanationsand model multiplicity in the context of neural network clas-sifiers. We show that current explanation methods often pro-duce counterfactuals whose validity is not preserved undermodel multiplicity. We then study the problem of generatingcounterfactuals that are guaranteed to be robust to model multiplicity, characterise its complexity and propose an approach to solve this problem using ideas from relational verification.
  • Conference paper
    Rago A, Li H, Toni F, 2023,

    Interactive explanations by conflict resolution via argumentative exchanges

    , 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023), Publisher: IJCAI Organization, Pages: 582-592, ISSN: 2334-1033

    As the field of explainable AI (XAI) is maturing, calls forinteractive explanations for (the outputs of) AI models aregrowing, but the state-of-the-art predominantly focuses onstatic explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computational argumentation. Specifically, we define Argumentative eXchanges (AXs) for dynamically sharing, in multi-agent systems, information harboured in individual agents’ quantitative bipolar argumentation frameworks towards resolving conflicts amongst the agents. We then deploy AXs in the XAI setting in which a machine and a human interact about the machine’s predictions. We identify and assess several theoretical properties characterising AXs that are suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent behaviours, e.g. capturing counterfactual patterns of reasoning in machines and highlighting the effects ofcognitive biases in humans. We show experimentally (in asimulated environment) the comparative advantages of these behaviours in terms of conflict resolution, and show that the strongest argument may not always be the most effective.
  • Conference paper
    Kouvaros P, Leofante F, Edwards B, Chung C, Margineantu D, Lomuscio Aet al., 2023,

    Verification of semantic key point detection for aircraft pose estimation

    , The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR2023), Publisher: IJCAI Organization, Pages: 757-762, ISSN: 2334-1033

    We analyse Semantic Segmentation Neural Networks running on an autonomous aircraft to estimate its pose during landing. We show that automated reasoning techniques from neural network verification can be used to analyse the conditions under which the networks can operate safely, thus providing enhanced assurance guarantees on the behaviour of the over-all pose estimation systems.
  • Conference paper
    Nguyen H-T, Satoh K, Goebel R, Stathis K, Toni Fet al., 2023,

    Black-box analysis: GPTs across time in legal textual entailment task

    , ISAILD symposium - International Symposium on Artificial Intelligence and Legal Documents, Publisher: IEEE
  • Journal article
    Shah M, Inacio M, Lu C, Schiratti P-R, Zheng S, Clement A, Simoes Monteiro de Marvao A, Bai W, King A, Ware J, Wilkins M, Mielke J, Elci E, Kryukov I, McGurk K, Bender C, Freitag D, O'Regan Det al., 2023,

    Environmental and genetic predictors of human cardiovascular ageing

    , Nature Communications, Vol: 14, Pages: 1-15, ISSN: 2041-1723

    Cardiovascular ageing is a process that begins early in life and leads to a progressive change instructure and decline in function due to accumulated damage across diverse cell types, tissues andorgans contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence andend-organ damage, however the genetic architecture of cardiovascular ageing is not known. Herewe use machine learning approaches to quantify cardiovascular age from image-derived traits ofvascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated bycardiometabolic risk factors and we also identify prescribed medications that are potential modifiersof ageing. Through large-scale modelling of ageing across multiple traits our results reveal insightsinto the mechanisms driving premature cardiovascular ageing and reveal potential molecular targetsto attenuate age-related processes.
  • Journal article
    Toni F, Rago A, Cyras K, 2023,

    Forecasting with jury-based probabilistic argumentation

    , Journal of Applied Non Classical Logics, Vol: 33, Pages: 224-243, ISSN: 1166-3081

    Probabilistic Argumentation supports a form of hybrid reasoning by integratingquantitative (probabilistic) reasoning and qualitative argumentation in a naturalway. Jury-based Probabilistic Argumentation supports the combination of opinionsby different reasoners. In this paper we show how Jury-based Probabilistic Abstract Argumentation (JPAA) and a form of Jury-based Probabilistic Assumptionbased Argumentation (JPABA) can naturally support forecasting, whereby subjective probability estimates are combined to make predictions about future occurrences of events. The form of JPABA we consider is an instance of JPAA andresults from integrating Assumption-Based Argumentation (ABA) and probabilityspaces expressed by Bayesian networks, under the so-called constellation approach.It keeps the underlying structured argumentation and probabilistic reasoning modules separate while integrating them. We show how JPAA and (the considered formof) JPABA can be used to support forecasting by 1) supporting different forecasters (jurors) to determine the probability of arguments (and, in the JPABA case,sentences) with respect to their own probability spaces, while sharing arguments(and their components); and 2) supporting the aggregation of individual forecaststo produce group forecasts.
  • Conference paper
    Leofante F, Henriksen P, Lomuscio A, 2023,

    Verification-friendly networks: the case for parametric ReLUs

    , International Joint Conference on Neural Networks (IJCNN 2023), Publisher: IEEE, Pages: 1-9

    It has increasingly been recognised that verification can contribute to the validation and debugging of neural networks before deployment, particularly in safety-critical areas. While progress has been made in the area of verification of neural networks, present techniques still do not scale to large ReLU-based neural networks used in many applications. In this paper we show that considerable progress can be made by employing Parametric ReLU activation functions in lieu of plain ReLU functions. We give training procedures that produce networks which achieve one order of magnitude gain in verification overheads and 30-100% fewer timeouts with VeriNet, a SoA Symbolic Interval Propagation-based verification toolkit, while not compromising the resulting accuracy. Furthermore, we show that adversarial training combined with our approachimproves certified robustness up to 36% compared to adversarial training performed on baseline ReLU networks.
  • Conference paper
    Lim BWT, Flageat M, Cully A, 2023,

    Efficient exploration using model-based quality-diversity with gradients

    , Conference on Artificial Life, Publisher: MIT Press, Pages: 1-10

    Exploration is a key challenge in Reinforcement Learning,especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches have proven effective. Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours. However, these methods are driven by either undirected sampling (i.e. mutations) or use approximated gradients (i.e. Evolution Strategies) in the parameter space, which makes them highly sample-inefficient. In this paper, we propose Dynamics-Aware QD-Ext (DA-QD-ext) and Gradient and Dynamics Aware QD (GDA-QD), two model-based Quality-Diversity approaches. They extend existing QD methods to use gradients for efficient exploitation and leverage perturbations in imagination for efficient exploration.Our approach takes advantage of the effectiveness of QD algorithms as good data generators to train deep models and use these models to learn diverse and high-performing populations. We demonstrate that they outperform baseline RL approaches on tasks with deceptive rewards, and maintain the divergent search capabilities of QD approaches while exceeding their performance by ∼ 1.5 times and reaching the same results in 5 times less samples.
  • Conference paper
    Ayoobi H, Potyka N, Toni F, 2023,

    SpArX: Sparse Argumentative Explanations for Neural Networks

    , European Conference on Artificial Intelligence 2023

    Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs' outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original structure as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insightsinto the actual reasoning process of MLPs.
  • Conference paper
    Sanguedolce G, Naylor PA, Geranmayeh F, 2023,

    Uncovering the potential for a weakly supervised end-to-end model in recognising speech from patient with post-stroke aphasia

    , 5th Clinical Natural Language Processing Workshop, Publisher: Association for Computational Linguistics, Pages: 182-190

    Post-stroke speech and language deficits (aphasia) significantly impact patients' quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p < 0.001). We demonstrated that worse WER was related to the more severe aphasia as measured by expressive (overt naming, and spontaneous speech production) and receptive (written and spoken comprehension) language assessments. Stroke lesion size did not affect the performance of Whisper. Linear mixed models accounting for demographic factors, therapy duration, and time since stroke, confirmed worse Whisper performance with left hemispheric frontal lesions. We discuss the implications of these findings for how future ASR can be improved in PWA.
  • Conference paper
    Faldor M, Chalumeau F, Flageat M, Cully Aet al., 2023,

    MAP-elites with descriptor-conditioned gradients and archive distillation into a single policy

    , The Genetic and Evolutionary Computation Conference, Publisher: Association for Computing Machinery, Pages: 138-146

    Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics. However, MAP-Elites performs a divergent search based on random mutations originating from Genetic Algorithms, and thus, is limited to evolving populations of low-dimensional solutions. PGA-MAP-Elites overcomes this limitation by integrating a gradient-based variation operator inspired by Deep Reinforcement Learning which enables the evolution of large neural networks. Although high-performing in many environments, PGA-MAP-Elites fails on several tasks where the convergent search of the gradient-based operator does not direct mutations towards archive-improving solutions. In this work, we present two contributions: (1) we enhance the Policy Gradient variation operator with a descriptor-conditioned critic that improves the archive across the entire descriptor space, (2) we exploit the actor-critic training to learn a descriptor-conditioned policy at no additional cost, distilling the knowledge of the archive into one single versatile policy that can execute the entire range of behaviors contained in the archive. Our algorithm, DCG-MAP-Elites improves the QD score over PGA-MAP-Elites by 82% on average, on a set of challenging locomotion tasks.
  • Conference paper
    Grillotti L, Flageat M, Lim B, Cully Aet al., 2023,

    Don't bet on luck alone: enhancing behavioral reproducibility of quality-diversity solutions in uncertain domains

    , Genetic and Evolutionary Computation Conference (GECCO), Publisher: ACM

    Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same solution can differ significantly from one evaluation to another, leading to uncertainty in the estimation of such values. Given the elitist nature of QD algorithms, they commonly end up with many degeneratesolutions in such noisy settings. In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any QD algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%.
  • Conference paper
    De Angelis E, Proietti M, Toni F, 2023,

    ABA learning via ASP

    , ICLP 2023, Publisher: Open Publishing Association, Pages: 1-8, ISSN: 2075-2180

    Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples. We propose a novel method for implementing ABA Learning using Answer SetProgramming as a way to help guide Rote Learning and generalisation in ABA Learning.
  • Conference paper
    Toni F, Potyka N, Ulbricht M, Totis Pet al., 2023,

    Understanding ProbLog as probabilistic argumentation

    , ICLP 2023, Publisher: Open Publishing Association, Pages: 183-189, ISSN: 2075-2180

    ProbLog is a popular probabilistic logic programming language and tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study someconnections between ProbLog and a variant of another well-known formalism combining symbolicreasoning and reasoning under uncertainty, namely probabilistic argumentation. Specifically, weshow that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) underthe constellation approach, which builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with a variety of alternative semantics, inheritedfrom PAA/PABA, as well as obtaining novel argumentation semantics for PAA/PABA, leveraging onexisting connections between ProbLog and argumentation. Moreover, the connections pave the waytowards novel forms of argumentative explanations for ProbLog’s outputs.
  • Conference paper
    Mihailescu I, Weng A, Sharma S, Ghitu M, Grewal D, Chew K, Ayoobi H, Potyka N, Toni Fet al., 2023,

    PySpArX - A Python library for generating Sparse Argumentative eXplanations for neural networks

    , ICLP 2023, Publisher: Open Publishing Association, Pages: 336-336, ISSN: 2075-2180

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