The CRITICS project addresses science accessibility and literacy through the convergence of advanced Machine Translation (MT) based on Large Language Models (LLMs) and educational technology. By leveraging MT systems specifically optimized for scientific content, educational institutions can provide accurate, culturally relevant translations of scientific materials in higher-education students’ native languages, ensuring that complex scientific concepts are comprehensible while maintaining technical accuracy. Novel research on MT specifically tailored for scientific documents aims to break down language barriers in accessing cutting-edge research and educational materials currently only available in high-resourced languages such as English, thereby facilitating the democratization of scientific knowledge across linguistic boundaries.

Social media platforms face a growing challenge in addressing abusive content and hate speech, particularly as traditional natural language processing methods often struggle with detecting nuanced and implicit instances. To tackle this issue, our study enhances Large Language Models (LLMs) in the detection and explanation of implicit hate speech, outperforming classical approaches. We focus on two key objectives: (1) determining whether jointly predicting and generating explanations for why a message is hateful improves LLMs’ accuracy, especially for implicit cases, and (2) evaluating whether incorporating information from BERT-based models can further boost detection and explanation performance. Our method evaluates and enhances LLMs’ ability to detect hate speech and explain their predictions. By combining binary classification (Hate Speech vs. Non-Hate Speech) with natural language explanations, our approach provides clearer insights into why a message is considered hateful, advancing the accuracy and interpretability of hate speech detection.

Explainable fact-checking plays a vital role in the fight against disinformation in today’s digital landscape. With the increasing volume of unverified content online, providing justifications for fact-checking has become essential to help users make informed decisions. While recent studies provide user-friendly explanations through abstractive or extractive summarization, they often assume the availability of human-written fact-checking articles, which is not always the case. This demo introduces SAFE, an argument-based framework designed to enhance both fact-checking and its justification. Specifically, SAFE offers three key features: i) producing argument-structured summaries of human-written fact-checking articles, ii) in the absence of human-written articles, generating structured summaries based on evidence retrieved from a corpus through a jointly trained summarization and evidence retrieval system, and iii) assessing the truthfulness of a claim by analyzing the structured summary.

Research on language technology for the development of medical applications is currently a hot topic in Natural Language Understanding and Generation. Thus, a number of large language models (LLMs) have recently been adapted to the medical domain, so that they can be used as a tool for mediating in human-AI interaction. While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly). This is particularly true of text-to-text models, which typically require large amounts of domain-specific pre-training data, often not easily accessible for many languages. In this paper, we address these shortcomings by compiling, to the best of our knowledge, the largest multilingual corpus for the medical domain in four languages, namely English, French, Italian and Spanish. This new corpus has been used to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. Additionally, we present two new evaluation benchmarks for all four languages with the aim of facilitating multilingual research in this domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks, while being competitive with current state-of-the-art LLMs in English.

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument’s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

Argument(ation) Mining (AM) is the dimension of computational argumentation aiming at automatically processing natural language arguments and reason upon them. More precisely, argument mining aims at extracting, classifying and analysing natural language arguments and their relations from text, with the final goal of providing machine-processable structured data for computational models of argument. In this keynote talk, I will first introduce this research area, highlighting the main successful tasks and open issues in identifying argumentative structures from different kinds of texts (e.g., clinical trials, online user generated content, news articles). Then, I will present a key challenge which conjugate argument mining with formal computational models of argumentation, i.e., the assessment of the trustworthiness of natural language arguments, with a focus on fallacious argumentation, with the aim to show how these methods can be used to automatically identify fallacious arguments in political debates. Fallacies play a prominent role in argumentation since antiquity due to their contribution to argumentation in critical thinking education. Their role is even more crucial nowadays as contemporary argumentation technologies face challenging tasks as misleading and manipulative information detection in news articles and political discourse, and counter-narrative generation. I will conclude with some thoughts on the challenge of automatic generation of counter-arguments to fight online disinformation and hate speech.

The need for transparent AI systems in sensitive domains like medicine has become key. In this paper we present ANTIDOTE, a software suite proposing different tools for argumentation-driven explainable Artificial Intelligence for digital medicine. Our system offers the following functionalities: multilingual argumentative analysis for the medical domain, explanation extraction and generation of clinical diagnoses, multilingual large language models for the medical domain, and the first multilingual benchmark for medical question-answering. Experimental results demonstrate the efficacy of ANTIDOTE across different tasks, highlighting its potential as an asset in medical research and practice and fostering transparency, which is crucial for informed decision-making in healthcare.

The increasing presence of hate speech (HS) on social media poses significant societal challenges. While efforts in the Natural Language Processing community have focused on automating the detection of explicit forms of HS, subtler and indirect expressions often go unnoticed. This demo presents PEACE, a novel tool that, besides detecting if a social media message contains explicit or implicit HS, also generates detailed natural language explanations for such predictions. More specifically, PEACE addresses three main challenging tasks: i) exploring the characteristics of HS messages, ii) predicting hatefulness, and iii) elucidating the reasoning behind system predictions. A REST API is also provided to exploit the tool’s functionalities.

The potential effectiveness of counterspeech as a hate speech mitigation strategy is attracting increasing interest in the NLG research community, particularly towards the task of automatically producing it.However, automatically generated responses often lack the argumentative richness which characterises expert-produced counterspeech.In this work, we focus on two aspects of counterspeech generation to produce more cogent responses.First, by investigating the tension between helpfulness and harmlessness of LLMs, we test whether the presence of safety guardrails hinders the quality of the generations.Secondly, we assess whether attacking a specific component of the hate speech results in a more effective argumentative strategy to fight online hate.By conducting an extensive human and automatic evaluation, we show how the presence of safety guardrails can be detrimental also to a task that inherently aims at fostering positive social interactions.Moreover, our results show that attacking a specific component of the hate speech, and in particular its implicit negative stereotype and its hateful parts, leads to higher-quality generations.Content warning: this paper contains unobfuscated examples some readers may find offensive

Explaining Artificial Intelligence (AI) decisions is a major challenge nowadays in AI, in particular when applied to sensitive scenarios like medicine and law.However, the need to explain the rationale behind decisions is a main issue also for human-based deliberation as it is important to justify why a certain decision has been taken.Resident medical doctors for instance are required not only to provide a (possibly correct) diagnosis, but also to explain how they reached a certain conclusion.Developing new tools to aid residents to train their explanation skills is therefore a central objective of AI in education.In this paper, we follow this direction, and we present, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors.These explanations have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support).The Multilingual CasiMedicosarg dataset consists of 558 clinical cases in four languages (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations.We conclude by showing how competitive baselines perform over this challenging dataset for the argument mining task.

Political debates are one of the most salient moments of an election campaign, where candidates are challenged to discuss the main contemporary and historical issues in a country. These debates represent a natural ground for argumentative analysis, which has always been employed to investigate political discourse structure and strategy in philosophy and linguistics. In this paper, we present DISPUTool 2.0, an automated tool which relies on Argument Mining methods to analyse the political debates from the US presidential campaigns to extract argument components (i.e., premise and claim) and relations (i.e., support and attack), and highlight fallacious arguments. DISPUTool 2.0 allows also for the automatic analysis of a piece of a debate proposed by the user to identify and classify the arguments contained in the text. A REST API is provided to exploit the tool's functionalities.

Recent studies have highlighted that private instant messaging platforms and channels are major media of cyber aggression, especially among teens. Due to the private nature of the verbal exchanges on these media, few studies have addressed the task of hate speech detection in this context. Moreover, the recent release of resources mimicking online aggression situations that may occur among teens on private instant messaging platforms is encouraging the development of solutions aiming at dealing with diversity in digital harassment. In this study, we present BiRDy: a fully Web-based platform performing participant role detection in multi-party chats. Leveraging the pre-trained language model mBERT (multilingual BERT), we release fine-tuned models relying on various contextual window strategies to classify exchanged messages according to the role of involvement in cyberbullying of the authors. Integrating a role scoring function, the proposed pipeline predicts a unique role for each chat participant. In addition, detailed confidence scoring are displayed. Currently, BiRDy publicly releases models for French and Italian.

Argument(ation) mining (AM) is an area of research in Artificial Intelligence (AI) that aims to identify, analyse and automatically generate arguments in natural language. In a pipeline, the identification and analysis of the arguments and their components (i.e. premises and claims) in texts and the prediction of their relations (i.e. attack and support) are then handled by argument-based reasoning frameworks so that, for example, fallacies and inconsistencies can be automatically identified. Recently, the field of argument mining has tackled new challenges, namely the evaluation of argument quality (e.g. strength, persuasiveness), natural language argument summarisation and retrieval, and natural language argument generation. In this paper, I discuss my main contributions in this area as well as some lines of future research. This paper is part of the AAAI-23 New Faculty Highlights.

The research carried out so far in detecting abusive content in social media has primarily focused on overt forms of hate speech.While explicit hate speech (HS) is more easily identifiable by recognizing hateful words, messages containing linguistically subtle and implicit forms of HS (as circumlocution, metaphors and sarcasm) constitute a real challenge for automatic systems.While the sneaky and tricky nature of subtle messages might be perceived as less hurtful with respect to the same content expressed clearly, such abuse is at least as harmful as overt abuse.In this paper, we first provide an in-depth and systematic analysis of 7 standard benchmarks for HS detection, relying on a fine-grained and linguistically-grounded definition of implicit and subtle messages.Then, we experiment with state-of-the-art neural network architectures on two supervised tasks, namely implicit HS and subtle HS message classification.We show that while such models perform satisfactory on explicit messages, they fail to detect implicit and subtle content, highlighting the fact that HS detection is not a solved problem and deserves further investigation.

Fallacies are arguments that employ faulty reasoning.Given their persuasive and seemingly valid nature, fallacious arguments are often used in political debates.Employing these misleading arguments in politics can have detrimental consequences for society, since they can lead to inaccurate conclusions and invalid inferences from the public opinion and the policymakers.Automatically detecting and classifying fallacious arguments represents therefore a crucial challenge to limit the spread of misleading or manipulative claims and promote a more informed and healthier political discourse.Our contribution to address this challenging task is twofold.First, we extend the ElecDeb60To16 dataset of U.S. presidential debates annotated with fallacious arguments, by incorporating the most recent Trump-Biden presidential debate.We include updated tokenlevel annotations, incorporating argumentative components (i.e., claims and premises), the relations between these components (i.e., support and attack), and six categories of fallacious arguments (i.e., Ad Hominem, Appeal to Authority, Appeal to Emotion, False Cause, Slippery Slope, and Slogans).Second, we perform the twofold task of fallacious argument detection and classification by defining neural network architectures based on Transformers models, combining text, argumentative features, and engineered features.Our results show the advantages of complementing transformer-generated text representations with non-textual features.

International audience

Fallacies play a prominent role in argumentation since antiquity due to their contribution to argumentation in critical thinking education. Their role is even more crucial nowadays as contemporary argumentation technologies face challenging tasks as misleading and manipulative information detection in news articles and political discourse, and counter-narrative generation. Despite some work in this direction, the issue of classifying arguments as being fallacious largely remains a challenging and an unsolved task. Our contribution is twofold: first, we present a novel annotated resource of 31 political debates from the U.S. Presidential Campaigns, where we annotated six main categories of fallacious arguments (i.e., ad hominem, appeal to authority, appeal to emotion, false cause, slogan, slippery slope) leading to 1628 annotated fallacious arguments; second, we tackle this novel task of fallacious argument classification and we define a neural architecture based on transformers outperforming state-of-the-art results and standard baselines. Our results show the important role played by argument components and relations in this task.

Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool’s functionalities.

Over the past decades, the number of episodes of cyber aggression occurring online has grown substantially, especially among teens. Most solutions investigated by the NLP community to curb such online abusive behaviors consist of supervised approaches relying on annotated data extracted from social media. However, recent studies have highlighted that private instant messaging platforms are major mediums of cyber aggression among teens. As such interactions remain invisible due to the app privacy policies, very few datasets collecting aggressive conversations are available for the computational analysis of language. In order to overcome this limitation, in this paper we present the CyberAgressionAdo-V1 dataset, containing aggressive multiparty chats in French collected through a role-playing game in high-schools, and annotated at different layers. We describe the data collection and annotation phases, carried out in the context of a EU and a national research projects, and provide insightful analysis on the different types of aggression and verbal abuse depending on the targeted victims (individuals or communities) emerging from the collected data.

Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling.As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings.In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD).We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented.Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level.We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., "stop Brexit" vs. "support Brexit").Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit's stance from key-concepts, showing that VAD influence the support and opposition aspects.

In the last years, several empirical approaches have been proposed to tackle argument mining tasks, e.g., argument classification, relation prediction, argument synthesis. These approaches rely more and more on language models (e.g., BERT) to boost their performance. However, these language models require a lot of training data, and size is often a drawback of the available argument mining data sets. The goal of this paper is to assess the robustness of these language models for the argument classification task. More precisely, the aim of the current work is twofold: first, we generate adversarial examples addressing linguistic perturbations in the original sentences, and second, we improve the robustness of argument classification models using adversarial training. Two empirical evaluations are addressed relying on standard datasets for AM tasks, whilst the generated adversarial examples are qualitatively evaluated through a user study. Results prove the robust-ness of BERT for the argument classification task, yet highlighting that it is not invulnerable to simple linguistic perturbations in the input data.

Argument(ation) Mining (AM) is the research area which aims at extracting argument components and predicting argumentative relations (i.e., support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources were created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in AM. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task in AM. Thus, our baselines can be employed by the AM community to compare more effectively how well a method performs on the argumentative relation prediction task.

Argument(ation) Mining (AM) typically aims at identifying argumentative components in text and predicting the relations among them. Evidence-based decision making in the health-care domain targets at supporting clinicians in their deliberation process to establish the best course of action for the case under evaluation. Although the reasoning stage of this kind of frameworks received considerable attention, little effort has been devoted to the mining stage. We extended an existing dataset by annotating 500 abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database, leading to a dataset of 4198 argument components and 2601 argument relations on different diseases (i.e., neoplasm, glau-coma, hepatitis, diabetes, hypertension). We propose a complete argument mining pipeline for RCTs, classifying argument components as evidence and claims, and predicting the relation, i.e., attack or support , holding between those argument components. We experiment with deep bidirectional transformers in combination with different neural architectures (i.e., LSTM, GRU and CRF) and obtain a macro F1-score of .87 for component detection and .68 for relation prediction , outperforming current state-of-the-art end-to-end AM systems.

The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article, we propose a robust neural architecture that is shown to perform in a satisfactory way across different languages; namely, English, Italian, and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network–specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media in English, Italian, and German.

Political debates offer a rare opportunity for citizens to compare the candidates' positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.

This paper presents a framework for modelling legislative deliberation in the form of dialogues. Roughly, in legislative dialogues coalitions can dynamically change and propose rule-based theories associated with different utility functions, depending on the legislative theory the coalitions are trying to determine.

Argumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements.

Political debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016).

To resolve conflicts amongst norms, various non-monotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent non-monotonic logics. In this paper we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems (HANS), we define three kinds of prioritized normative reasoning approaches called Greedy, Reduction and Optimization. Then, after formulating an argumentation theory for a HANS, we show that for a totally ordered HANS, Greedy and Reduction can be represented in argumentation by applying the weakest link and the last link principles, respectively, and Optimization can be represented by introducing additional defeats capturing the idea that for each argument that contains a norm not belonging to the maximal obeyable set then this argument should be rejected.

In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

Also in Volume 305: Computational Models of Argument (IOS Press)

Argument-based decision making has been employed to support a variety of reasoning tasks over medical knowledge. These include evidence-based justifications of the effects of treatments, the detection of conflicts in the knowledge base, and the enabling of uncertain and defeasible reasoning in the health-care sector. However, a common limitation of these approaches is that they rely on structured input information. Recent advances in argument mining have shown increasingly accurate results in detecting argument components and predicting their relations from unstructured, natural language texts. In this study, we discuss evidence and claim detection from Randomized Clinical Trials. To this end, we create a new annotated dataset about four different diseases (glaucoma, diabetes, hepatitis B, and hypertension), containing 976 argument components (697 containing evidence, 279 claims). Empirical results are promising, and show the portability of the proposed approach over different branches of medicine.

Agent-based recommender systems have been exploited in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommenda-tions' computation. However, many real-world scenarios, like the traffic one, require the additional feature of representing and reasoning about spatial and temporal knowledge, considering also their vague connotation. This paper tackles this challenge and introduces CARS, a spatio-temporal agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal information to represent and reason about fuzzy beliefs and desires dynamics. An experimental evaluation about spatio-temporal reasoning in the traffic domain is carried out using the NetLogo platform, showing the improvements our recommender system introduces to support agents in achieving their goals.

Argument mining is the research area aiming at extracting natural language arguments and their relations from text, with the final goal of providing machine-processable structured data for computational models of argument. This research topic has started to attract the attention of a small community of researchers around 2014, and it is nowadays counted as one of the most promising research areas in Artificial Intelligence in terms of growing of the community, funded projects, and involvement of companies. In this paper, we present the argument mining tasks, and we discuss the obtained results in the area from a data-driven perspective. An open discussion highlights the main weaknesses suffered by the existing work in the literature, and proposes open challenges to be faced in the future.

The latest years have seen an increasing interest in the topic of Artificial Intelligence (AI), the challenges it is facing, and the recent advances it has achieved, e.g., intelligent personal assistants. Differently from the past, where research on AI was mainly confined in research labs, the topic is now attracting interest from a wider audience, including policy-makers, information technology companies, and philosophers. Alas, these advances have also raised a number of concerns on AI’s social, economic, and legal impact. Hence, the definition of design principles and automated methods to support transparent intelligent machine deliberation is highly desirable. Argumentation is important for handling conflicting beliefs, assumptions, opinions, goals, and many other mental attitudes. Argumentation pervades human intelligent behavior, and I believe that it is a mandatory element to conceive autonomous artificial machines that can exploit argumentation models and tools in the cognitive tasks they are required to carry out. Results in this area will allow reducing the gap between humans and machines towards a good AI hybrid society.

Agent-based computing addresses the challenges in managing distributed computing systems and networks through monitoring, communication, consensus-based decision-making and coordinated actuation.As a result, intelligent agents and multi-agent systems have demonstrated the capability to use intelligence, knowledge representation and reasoning, and other social metaphors like 'trust', 'game' and 'institution', not only to address real-world problems in a human-like way but also to transcend human performance.This has had a transformative impact in many application domains, particularly in e-commerce, and also in planning, logistics, manufacturing, robotics, decision support, transportation, entertainment, emergency relief & disaster management, and data mining & analytics.As one of the largest and still growing research fields of Computer Science, agent-based computing today remains a unique enabler of inter-, multi-and trans-disciplinary research.The International Conference on Conference on Principles and Practice of Multi-Agent Systems (PRIMA) originally started in 1998 as a regional (Asia-Pacific) workshop and in the last decade it grew to become one of the leading and influential scientific conferences for research on multi-agent systems.Each year, PRIMA brings together active researchers, developers and practitioners from both academia and industry to showcase, share and promote research in several domains, ranging from foundations of agent theory and engineering aspects of agent systems, to emerging interdisciplinary areas of agent-based research.Previous successful editions were held in Nagoya, Japan (2009), Kolkata, India (2010), Wollongong, Australia (2011), Kuching, Malaysia (2012), Dunedin, New Zealand (2014), and Gold Coast Australia (2014).The last two editions were held in Phuket, Thailand (2016) and in Nice, France (2017).This issue contains selected papers from the eighteenth edition of PRIMA, which took place from 26 to 30 October 2015 in Bertinoro, FC, Italy.The conference received 94 submissions from 30 countries.From the 29 papers presented at the conference, the best theoretically-oriented ones were invited to submit an extended version for this special issue with Fundamenta Informaticae, while another special issue with the Journal of Agent-Oriented Software Engineering has been organized around the more practically-oriented papers.The contributions that were eventually submitted underwent a thorough two-or three-stage reviewing procedure, resulting in the ten papers in the present collection.They constitute the most recent advances in the theory and practice of multi-agent systems, with interesting intersections with other domains.

Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in.Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media.In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification.In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification.We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.

In this paper we try to improve Information Extraction in legal texts by creating a legal Named Entity Recognizer, Classifier and Linker. With this tool, we can identify relevant parts of texts and connect them to a structured knowledge representation, the LKIF ontology.

In this paper, we propose a proof of concept for the ontological representation of normative requirements as Linked Data on the Web. Starting from the LegalRuleML ontology, we present an extension of this ontology to model normative requirements and rules. Furthermore, we define an operational formalization of the deontic reasoning over these concepts on top of the Semantic Web languages.

The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe an ongoing work towards the creation of a complete argument mining pipeline over Twitter messages: (i) we identify which tweets can be considered as arguments and which cannot, (ii) over the set of tweet-arguments, we group them by topic, and (iii) we predict whether such tweets support or attack each other. The final goal is to compute the set of tweets which are widely recognized as accepted, and the different (possibly conflicting) viewpoints that emerge on a topic, given a stream of messages.

RDF is a standard for the conceptual description of knowledge, and SPARQL is the query language conceived to query RDF data. The RDF data is cherished and exploited by various domains such as life sciences, Semantic Web, social network, etc. Further, its integration at Web-scale compels RDF management engines to deal with complex queries in terms of both size and structure. In this paper, we propose AMbER (Attributed Multigraph Based Engine for RDF querying), a novel RDF query engine specifically designed to optimize the computation of complex queries. AMbER leverages subgraph matching techniques and extends them to tackle the SPARQL query problem. First of all RDF data is represented as a multigraph, and then novel index- ing structures are established to efficiently access the in- formation from the multigraph. Finally a SPARQL query is represented as a multigraph, and the SPARQL querying problem is reduced to the subgraph homomorphism prob- lem. AMbER exploits structural properties of the query multigraph as well as the proposed indexes, in order to tackle the problem of subgraph homomorphism. The performance of AMbER, in comparison with state-of-the-art systems, has been extensively evaluated over several RDF benchmarks. The advantages of employing AMbER for complex SPARQL queries have been experimentally validated.

In this paper, we propose a multi-agent recommender system based on the Belief-Desire-Intention (BDI) model applied to multi-context systems. First, we extend the BDI model with additional contexts to deal with sociality and information uncertainty. Second, we propose an ontological representation of planning and intention contexts in order to reason about plans and intentions. Moreover, we show a simple real-world scenario in healthcare in order to illustrate the overall reasoning process of our model.

In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender system in a realistic scenario, to understand how groups of agents behave in a social network compared to individual agents. Results show that agents within a social network have better collective performance than individual ones. The utility and the satisfaction of agents is increased by the exchange of messages when executing intentions.

Managing licensing information and data rights is becoming a crucial issue in the Linked (Open) Data scenario. An open problem in this scenario is how to associate machine-readable licenses specifications to the data, so that automated approaches to treat such information can be fruitfully exploited to avoid data misuse. This means that we need a way to automatically extract from a natural language document specifying a certain license a machine-readable description of the terms of use and reuse identified in such license.

In the latest years, natural models of argumentation and argument mining are becoming more and more important topics in the argumentation community. Given this tendency, there is the need to produce standard datasets on which natural language approaches to argumentation can be evaluated. In this paper, we present NoDE, a benchmark of natural language arguments composed of three datasets, built from different textual sources and annotated highlighting positive and negative connections between arguments.

Requirements engineering is an essential step of the software development process during which the behavior of a software system is defined. A lot of artifacts are created at this stage of the development process, and stakeholders need to be supported in managing requirements' consistency and evolution over time. In this paper, we present ArgRE, an argumentation-based system to be used by stakeholders to structure complex goal-based requirements, and maintain their consistency over time. In particular, we rely on meta-argumentation, where requirements are represented as arguments, and the standard Dung-like argumentation framework is extended with the relations holding among goal-based requirements.

In the last years, argumentation theory has been exploited to reason about norms, argue about enforced obligations and permissions, and establish the validity of norms seen as argumentative claims. In this paper, we start from the dynamic legal argumentation framework recently proposed by Prakken and Sartor, and we extend their ASPIC-based system by introducing deontic modalities, to include also normative concepts like factual and deontic detachment, and normative dynamics. Properties of the original and proposed legal argumentation system are presented and discussed, and related to deontic logic and logics of normative systems.

This paper describes a dataset of licenses expressed as RDF. The most important rights and conditions present in licenses for software, data and general works are expressed with the Open Digital Rights Language (ODRL) 2.0 vocabulary and extensions thereof. The dataset contains licenses identified by a dereferenceable URI, which are served with content negotiation providing a double representation for humans and machines alike. This feature enables a generalized machine-to-machine commerce if generally adopted.

In the Web of Data, the absence of clarity about the licensing terms under which the data is released prevents data reuse, and thus data publication and interlinking at the expenses of the Web of Data itself. In addition, even when terms are clear, the absence of automated processing of the licenses prevents scaling data reuse and integration. In this paper, we provide a semantic model of licenses for the Web of Data. The key idea of our approach consists first in verifying the compatibility and compliance of the licensing terms associated to the data queried by the consumer, and second, if compatibility arises, in composing the single licenses into a unique license which provides the terms of reuse for the whole data consumed during the query solving. In particular, we propose a deontic logic semantics which is able to (i) formally define the deontic components of the licenses, i.e., Permissions, Obligations, and Prohibitions, and reason over them, (ii) verify the compatibility of the elements composing the single licenses, and return those elements which can be included into the composite license, and (iii) provide a formal account of the heuristics proposed to guide the composition.

The Web of Data is assisting to a growth of interest with respect to the open challenge of representing and reasoning in an automated way over licenses and copyright. In this paper, we deal with the problem of checking the composing together a set of licensing terms associated to a single query result on the Web of Data to create a so called composite license. More precisely, we analyze two composition heuristics, AND-composition and OR-composition, showing how they can be used to combine the deontic components specified by the licenses, i.e., permissions, obligations, and prohibitions, and which are the most suitable combinations depending on the starting licenses. Such heuristics are evaluated using the SPINdle logic reasoner.

In the domain of Linked Open Data a need is emerging for developing automated frameworks able to generate the licensing terms associated to data coming from heterogeneous distributed sources. This paper proposes and evaluates a deontic logic semantics which allows us to define the deontic components of the licenses, i.e., permissions, obligations, and prohibitions, and generate a composite license compliant with the licensing items of the composed different licenses. Some heuristics are proposed to support the data publisher in choosing the licenses composition strategy which better suits her needs w.r.t. the data she is publishing. These 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.

Cooperative boolean games (CBG) are a family of coalitional games where agents may depend on each other for the satisfaction of their personal goals. In Dunne et al. (2008, Cooperative Boolean games. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 1015–1022), the authors define as solution concept the notion of core showing that several decision problems, such as core-emptiness, are Π2p-complete. In this work, we investigate how to improve the computation of the core. In particular, we introduce two different types of dependence networks, abstract dependence networks and refined dependence networks, that are used to define the notion of stable coalitions and Δ-reduction, respectively. Stable coalitions enable to focus on a subset of the agents and use results to determinate the core of the whole game. Δ-reduction prunes the search space by returning a set of actions that are not admissible to be executed. We present an algorithm based on stable coalitions and a Δ-reduction implemented in Prolog and experimental results that show how they effectively improve the computation of the core.

This paper introduces Input/Output Argumentation Frameworks, a novel approach to characterize the behavior of an argumentation framework as a sort of black box exposing a well-defined external interface. As a starting point, we define the novel notion of semantics decomposability and analyze complete, stable, grounded and preferred semantics in this respect. Then we show as a main result that, under grounded, complete, stable and credulous preferred semantics, Input/Output Argumentation Frameworks with the same behavior can be interchanged without affecting the result of semantics evaluation of other arguments interacting with them.

Many argumentation tools have been proposed nowadays to support the users in on-line social discussions. However, the main drawback of these tools is that they do not cope with the automatic generation of the arguments from the natural language discussions of the users. In this paper, we propose to use a technique from computational linguistics, namely textual entailment, to generate in an automatic way the abstract arguments from the dialogues. The abstract arguments as well as their relationships are then structured in an argumentation graph to evaluate the dialogue as a whole. The success criteria of the proposed approach is that it is able to represent the dynamics of the dialogues among users allowing to find the use of argumentation natural enough to be really adopted.

In this paper, we consider two drawbacks of Cayrol and Lagasque-Schiex's meta-argumentation theory to model bipolar argumentation frameworks. We consider first the “lost of admissibility” in Dung's sense and second, the definition of notions of attack in the context of a support relation. We show how to prevent these drawbacks by introducing support meta-arguments. Like the model of Cayrol and Lagasque-Schiex, our formalization confirms the use of meta-argumentation to reuse Dung's properties. We do not take a stance towards the usefulness of a support relation among arguments, though we show that if one would like to introduce them, it can be done without extending Dung's theory. Finally, we show how to use meta-argumentation to instantiate an argumentation framework to represent defeasible support. In this model of support, the support relation itself can be attacked.