Hanna Suominen

Lorraine Goeuriot

Liadh Kelly
email: liadh.kelly@mu.ie

Laura Alonso Alemany
email: lauraalonsoalemany@unc.edu.ar

Elias Bassani
email: elias.bassani@consorzioc2t.it

Nicola Brew-Sam

Viviana Cotik
email: vcotik@dc.uba.ar

Darío Filippo
email: dfilippo@gmail.com

Gabriela González-Sáez

Franco Luque
email: francolq@unc.edu.ar

Philippe Mulhem

Gabriella Pasi

Roland Roller
email: roland.roller@dfki.de

Sandaru Seneviratne

Rishabh Upadhyay

Jorge Vivaldi
email: jorge.vivaldi@upf.edu

Marco Viviani

Chenchen Xu

Overview of the CLEF eHealth Evaluation Lab 2021

Keywords: Entity Linking, Evaluation, Health Records, Information Extraction, Information Retrieval, Medical Informatics, Self-Diagnosis, Test-set Generation, Text Classification, Text Segmentation

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In recent years, electronic health (eHealth) content has become available in a variety of forms, ranging from patient records and medical dossiers, scientific publications, and health-related websites to medical-related topics shared across social networks. Laypeople, clinicians, and policy-makers need to easily retrieve and make sense of such medical content to support their clinical judgement and decision-making. The increasing difficulties experienced by these stakeholders in retrieving and digesting valid and relevant information in their preferred language to make health-centred decisions has motivated CLEF eHealth to organise yearly shared challenges since 2013.

More specifically, CLEF eHealth 15 was established as a lab workshop in 2012 as part of the Conference and Labs of the Evaluation Forum (CLEF, formerly known as Cross-Language Evaluation Forum). Since 2013 it has offered evaluation labs in the fields of layperson and professional health information extraction (IE), management, and retrieval (IR) with the aims of bringing together researchers working on related information access topics and providing them with datasets to work with and validate the outcomes. These labs and their subsequent workshops target:

1. developing processing methods and resources (e.g., dictionaries, abbreviation mappings, and data with model solutions for method development and evaluation) in a multilingual setting: (a) to enrich difficult-to-understand eHealth texts, (b) to provide personalized reliable access to medical information, and (c) to provide valuable documentation; 2. developing an evaluation setting and releasing evaluation results for these methods and resources; 3. contributing to the participants and organizers' professional networks and interaction with all interdisciplinary actors of the ecosystem for producing, processing, and consuming eHealth information.

The vision for the Lab is two-fold: (1) to develop tasks that potentially impact patient understanding of medical information and (2) to provide the community with an increasingly sophisticated dataset of clinical narratives, enriched with links to standard knowledge bases, evidence-based care guidelines, systematic reviews, and other further information, to advance the state-of-the-art in multilingual IE and IR in health care.

The ninth annual CLEF eHealth evaluation lab, CLEF eHealth 2021, aiming to build upon the resource development and evaluation approaches proposed in the previous years of the lab [START_REF] Suominen | Overview of the ShARe/CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the ShARe/CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | CLEF 2017 eHealth Evaluation Lab overview[END_REF][START_REF] Suominen | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | Overview of the CLEF eHealth evaluation lab 2020[END_REF], offered the following two tasks:

-Task 1. Multilingual IE [START_REF] Cotik | Overview of CLEF eHealth Task 1 -SpRadIE: A challenge on information extraction from Spanish Radiology Reports[END_REF] and -Task 2. Consumer Health Search (CHS) [START_REF] Goeuriot | Consumer Health Search at CLEF eHealth[END_REF].

The Multilingual IE task builds upon the six previous editions of the task (2015-2020) which already addressed the analysis of biomedical text in English, French, Hungarian, Italian, Spanish, and German [START_REF] Névéol | CLEF eHealth evaluation lab 2015 task 1b: clinical named entity recognition[END_REF][START_REF] Névéol | Clinical information extraction at the CLEF eHealth evaluation lab 2016[END_REF][START_REF] Névéol | CLEF eHealth 2017 multilingual information extraction task overview: ICD10 coding of death certificates in English and French[END_REF][START_REF] Névéol | CLEF eHealth 2018 multilingual information extraction task overview: ICD10 coding of death certificates in French, Hungarian and Italian[END_REF][START_REF] Neves | Overview of task 1 in CLEF eHealth 2019: Indexing German nontechnical summaries of animal experiments[END_REF][START_REF] Miranda-Escalada | Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth[END_REF]. This year, the task focuses on Named Entity Recognition in Spanish ultrasound reports. Ten different classes of concepts in the radiology domain are distinguished, including Anatomical Entities, and Findings, that describe a pathological or abnormal event, negations, and indicators of probability or future outcomes. As well as complex entities, the task includes the challenge of semantic split of the dataset. That is, training, development, and test sets cover different semantic fields. This allows for a more realistic held-out evaluation.

The Consumer Health Search task is a continuation of the previous CLEF eHealth IR tasks that ran in 2013-2018, and 2020 [START_REF] Goeuriot | ShARe/CLEF eHealth Evaluation Lab 2013, Task 3: Information retrieval to address patients' questions when reading clinical reports[END_REF][START_REF] Goeuriot | Task 3: User-centred health information retrieval[END_REF][START_REF] Palotti | Task 2: Retrieving Information about Medical Symptoms[END_REF][START_REF] Zuccon | The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval[END_REF][START_REF] Palotti | CLEF 2017 Task Overview: The IR Task at the eHealth Evaluation Lab[END_REF][START_REF] Jimmy | Overview of the CLEF 2018 consumer health search task[END_REF][START_REF] Goeuriot | An Analysis of Evaluation Campaigns in ad-hoc Medical Information Retrieval: CLEF eHealth 2013 and[END_REF][START_REF] Goeuriot | Overview of the CLEF eHealth 2020 task 2: Consumer health search with ad hoc and spoken queries[END_REF]. It embraces the Text REtrieval Conference (TREC) -style evaluation process, with a shared collection of documents and queries, the contribution of runs from participants and the subsequent formation of relevance assessments and evaluation of the participants submissions. The 2021 task generates a new representative web corpus and collection of layperson medical queries. The task is structured into a number of optional subtasks as follows: (1) ad-hoc search, (2) weakly-supervised IR, and (3) document credibility assessment.

The remainder of this paper is structured as follows: in Section 2, we detail the tasks, evaluation, and datasets created; in Section 3, we describe the submission and results for each task; and in Section 4, we provide conclusions.

Materials and Methods

In this section, we describe the materials and methods used in the two tasks of the CLEF eHealth evaluation lab 2021. After specifying our text documents to process in Section 2.1, we address the human annotations, queries, and relevance assessments in Section 2.2. Finally, in Section 2.3, we introduce our evaluation methods.

Text Documents

Task 1. The dataset for this task consists of a corpus of Spanish radiology reports, more concretely pediatric ultrasounds from an Argentinian public hospital. These reports are generally written within a hospital information system by direct typing in a computer and are informed in only one section, where the most relevant findings are described. They are written using standard templates that guide physicians on the structure of the report when the findings are normal, but most of the time they are written in free text to be able to describe the findings discovered in abnormal studies. This fact results in great variations in both size and content of the reports, ranging from 8 to 193 words. Also, there are misspellings and inconsistencies in the usage of abbreviations, punctuation, and line breaks, as can be seen in Figure 1.

Fig. 1. A sample report, with its translation. It shows abbreviations ("RD" for right kidney, "RI" for left kidney, "Diam" for diameter), typos ("formsa" for "forma"), and inconsistencies (capitalization of "Vejiga" because of start of sentence without a full stop.) Task 2. The document corpus used in the CHS task consists of web pages acquired from the CommonCrawl dump of 2021-04 16 . An initial list of websites was acquired from the 2018 CHS task which was built by submitting a set of medical queries to the Microsoft Bing Application Programming Interfaces (through the Azure Cognitive Services) repeatedly over a period of a few weeks, and acquiring the uniform resource Locators (URL) of the retrieved results. The domains of the acquired URLs were then included in the list, except some domains that were excluded for decency reasons. The list was augmented by including a number of known reliable and unreliable health websites, and social media contents of ranging reliability levels, from lists previously compiled by health institutions and agencies [START_REF] Jimmy | Overview of the CLEF 2018 consumer health search task[END_REF]. From this initial list of domains, a sample of domains was identified for final acquisition. This list was further extended by including websites, which were highly relevant for the task queries to create the final domain list with 600 domains. This introduced 13 new domains compared to the 2018 collection, and all domains were newly crawled from the latest CommonCrawl 2021-04.

The corpus was complemented with social media documents from Reddit and Twitter. A list of 150 health topics related to various health conditions was selected. Search queries were manually generated from those topics and were submitted to Reddit to retrieve posts and comments. The same process was applied on Twitter to get related tweets from the platform. A social media document was defined as a text obtained by a single interaction, therefore for Reddit one document is composed by a post, one comment of the post and associated meta-information. For Twitter, a document is a single tweet with its associated meta-information.

Human Annotations, Queries, and Relevance Assessments

Task 1. The radiology text data is annotated with seven different classes of entities: Finding, Anatomical Entity, Location, Measure, Degree, Type of Measure and Abbreviation. Additionally, hedges are also identified, distinguishing Negation, Uncertainty and Conditional Temporal. An example annotation can be found in Figure 2, and the frequency of each type of entity can be seen in Figure 3.

The phenomena under study have some challenging properties. For example, entities can be embedded within other entities. Moreover, entities can be discontinuous, and they can even span over sentence boundaries. The entity type Finding is particularly challenging, as it presents great variability in its textual forms. It ranges from a single word to more than ten words in some cases, comprising all kinds of phrases. However, this is also the most informative type of entity for the potential users of these annotations. Another challenging phenomena is the regular polysemy observed between Anatomical entities and Locations. In the manual annotation process, we have found that human annotators have less agreement on those categories than on the rest, and automatic classifiers also experience difficulties to consistently classify those as well.

The given corpus consists of a total of 513 ultrasound reports, with 35, 000 words and over 15, 000 annotated named entities. In order to assess the portability of the approaches, half of the reports were provided as training, and the other half for testing, making sure that the testing partition contained portions of text that belonged to previously unseen phenomena. Reports were manually annotated by clinical experts [START_REF] Cotik | Annotation of Entities and Relations in Spanish Radiology Reports[END_REF] and then revised by linguists. Annotation guidelines and training were provided for both rounds of annotation. More information about the dataset can be found in [START_REF] Cotik | Annotation of Entities and Relations in Spanish Radiology Reports[END_REF]. Nevertheless, for the challenge the annotation criteria has been reviewed and some annotations have been modified.

The task, called SpRadIE (for Spanish Radiology Information Extraction), was inspired by previous research on this subject [START_REF] Cotik | Spanish named entity recognition in the biomedical domain[END_REF][START_REF] Cotik | Information extraction from Spanish radiology reports[END_REF].

Task 2. The CHS task, Task 2, used a new set of 55 queries in English for realistic search scenarios. The queries were constructed either by hand, based on research interests and expertise of the organizers on multiple sclerosis and diabetes, or by using searches issued by the public to social media search services. Namely, the queries were manually authored and tailored by experts from established search scenarios and manually selected from a list of Google trends related queries to best fit each automatically extracted search scenario from social media (e.g., Twitter and Reddit).

Each query was manually labelled by the organizers with a narrative in English to describe the search intent or to capture the submission text for manually created queries and social medial queries, respectively. To illustrate, some queries and narratives appear as follows:

-Scenario 22:

• Query: my risk for developing type 2 diabetes • Narrative: You read that the risk for developing type 2 diabetes is increasing due to environmental and lifestyle factors, and you want to know more about your own risk. -Scenario 68:

• Query: List of multiple sclerosis symptoms • Narrative: I am a 40 year old patient with MS, and I have very vage symptoms, including fatigue, brain fog, foot drop, difficulties passing urine, problems turning right. Are these related to MS or might I have another disease in addition? -Scenario 105:

• Query: wisdom tooth cuts gum pain • Narrative: Hi all My wisdom tooth is currently cutting it's way through my bottom right gum the pain is intense throbbing aching jaw and weirdly a sore throat especially when swallowing. I just wonder if this is normal as I've had two wisdom tooth come through before with no pain at all. Thank you!

People with lived experience of the related medical conditions were consulted to motivate, validate, and refine the narratives. Furthermore, the queries were enriched by the organizers to have a theme (manually created ones) or name (social media) to ease classifying them, but these were neither released to the participants nor used for evaluation.

The subtasks 1, 2, and 3 used these 55 queries with 5 released for training and 50 reserved for testing; the test topics contained a balanced sample of the manually constructed and automatically extracted search scenarios.

Relevance assessments are currently in progress and will be detailed in the CHS task overview [START_REF] Goeuriot | Consumer Health Search at CLEF eHealth[END_REF]. Similar to the 2016 and 2017 pools, we created the pool using the rank-biased precision (RBP)-based Method A (Summing contributions) [START_REF] Moffat | Rank-biased precision for measurement of retrieval effectiveness[END_REF] in which documents are weighted according to their overall contribution to the effectiveness evaluation as provided by the RBP formula (with p = 0.8, following a study published in 2007 on RBP [START_REF] Park | On the distribution of user persistence for rank-biased precision[END_REF]). This strategy, named RBPA has been proven more efficient than traditional fixed-depth or stratified pooling to evaluate systems under fixed assessment budget constraints [START_REF] Lipani | Fixed-cost pooling strategies based on IR evaluation measures[END_REF], as it is the case for this task. All participants' runs were considered on the document's pool, along with six baselines provided by the organizers. In order to guarantee the judgements of the documents of the participants' runs, half of the pool is composed by their documents and half from documents of the baselines' runs.

Along with relevance assessments, readability and credibility judgments were also collected for the assessment pool; these were used to evaluate systems across different dimensions of relevance (see [START_REF] Goeuriot | Overview of the CLEF eHealth 2020 task 2: consumer health search with ad hoc and spoken queries[END_REF] for further information about the three dimensions).

The relevance, readability, and credibility assessments were performed by 26 volunteers in May-June 2021. Of these assessors, 16 were from Australia, 1 from Finland, 3 from France, 2 from Ireland, and 4 from Italy. The numbers of female and male assessors were 19 and 7, respectively. All assessors were recruited, trained, and supervised by the organizers by using bespoke written materials from April to June 2021. The recruitment took place on social media and via email, using both organizers' existing contacts and snowballing.

Assessments were implemented online by the organizers' expanding and customising the Relevation! tool for relevance assessments [START_REF] Koopman | Relevation!: an open source system for information retrieval relevance assessment[END_REF] to capture our task dimensions, scales, and other preferences (Figures 4, 5, and6). Each assessor was initially assigned 2 queries to be assessed, and in the end, every assessor completed 1 to 4 queries. Each query was associated with 250 documents to be assessed with respect to their relevance, readability, and credibility.

Ethical approval (2021/013) was obtained from the Human Research Ethics Committee of the Australian National University. Each study participant provided informed consent.

Evaluation Methods

Task 1. Participants could submit up to 4 runs. Lenient and exact match precision, recall, and F1 score were calculated. Submissions were evaluated with micro-averaged lenient match F1. The lenient match is calculated using the Jaccard Index, as described in [START_REF] Goeuriot | CLEF eHealth 2021 Evaluation Lab[END_REF] and based on [START_REF] Bossy | BioNLP shared task 2013 -an overview of the Bacteria Biotope Task[END_REF].

Task 2. For Subtasks 1, 2, and 3, participants could submit up to 4 runs in TREC format. Evaluation measures for Subtask 1, adhoc search task are Normalized Discounted Cumulative Gain (NDCG) at 10 (NDCG@10), BPref, and RBP, as well as other metrics adapted to other relevance dimensions such as uRBP and cRBP (with alpha value capturing the user expertise), an adapted metric to measure credibility relevance dimension based on uRBP. Subtask 3 Fig. 6. CLEF eHealth Consumer Health Search Task 2021: Assessor's document view used F1, Area under the receiver operating characteristic Curve (AUC), and Accuracy to measure a given system's ability to predict document credibility.

Results

The number of teams who registered their interest in CLEF eHealth 2021 Tasks 1 and 2 was 58 and 43 (and a total of 67 unique teams). In total, 7 and 4 teams submitted to the two shared tasks, respectively.

Task 1 Overall seven different teams participated in our shared task. Most prominent were participants from Spain, but also from Italy, UK and Colombia. Most participating teams were experimenting with different variations of neural networks, particularly transformer-based approaches [START_REF] Solarte-Pabón | Information Extraction from Spanish Radiology Reports using multilingual BERT[END_REF][START_REF] López-Úbeda | Pre-trained language models to extract information from radiological reports[END_REF][START_REF] Suárez-Paniagua | A multi-BERT hybrid system for Named Entity Recognition in Spanish radiology reports[END_REF][START_REF] Polignano | Comparing Transformer-based NER approaches for analysing textual medical diagnoses[END_REF], but also bi-LSTMs [START_REF] Fabregat | LSI_UNED at CLEF eHealth2021: Exploring the effects of transfer learning in negation detection and entity recognition in clinical texts[END_REF]. Besides the challenge also includes submissions of a CRF [START_REF] Ángel | Extracting information from radiology reports by Natural Language Processing and Deep Learning[END_REF], and a pattern based approach [START_REF] Nunzio | IMS-UNIPD @ CLEF eHealth Task 1: A Memory Based Reproducible Baseline[END_REF]. Overall, overlapping and discontinuous entities of the given dataset were the biggest challenge of the dataset, which made preand post-processing steps necessary. Moreover, in order to deal with the overlapping entities appropriately, the two highest scored teams make usage of multiple classifiers.

Table 1 shows the best result of each team's run. Best lenient precision, recall, and F1 are written in bold. The variation of the performance of the different systems across different kinds of entities can be seen in the boxplots in Figure 7. We can see that, although there is much variation in performance across systems (hence the long boxes), for some entities performance is lower, mostly those with fewer examples. Interestingly, types of entity with a big number of examples, like Location, still have low performance, for example, if compared with Anatomical Entities. It is interesting to see how performance for Abbreviations is very varied across approaches.

Task 2 had 4 teams submitting runs: In Subtask 2.1 on Ad Hoc IR, a 4-member team from the School of Computer Science, Zhongyuan University of Technology (ZUT) in Zhengzhou, China and a team with two members from the Information Management Systems (IMS) Research Group of the Italian University of Padova (UniPd) submitted runs. In Subtasks 2.2 on Weakly Supervised IR and 2.3 on Document Credibility Prediction, the leader of this IMS UniPd team, who has been a regular participant in previous CLEF eHealth IR tasks, submitted runs. Participants submissions were due by May 8th 2021 and the relevance assessments are being collected at the time of writing of this paper. See the Task 2 overview paper for further details and the results of the evaluation [START_REF] Goeuriot | Consumer Health Search at CLEF eHealth[END_REF].

Conclusions

This paper provided an overview of the CLEF eHealth 2021 evaluation lab. The CLEF eHealth workshop series was established in 2012 as a scientific workshop with an aim of establishing an evaluation lab [START_REF] Suominen | CLEFeHealth2012 -The CLEF 2012 Workshop on Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis[END_REF]. Since 2013, this annual workshop has been supplemented with two or more preceding shared tasks each year. In other words, they are the CLEF eHealth 2013-2020 evaluation labs [START_REF] Suominen | Overview of the ShARe/CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the ShARe/CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | CLEF 2017 eHealth Evaluation Lab overview[END_REF][START_REF] Suominen | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Kelly | Overview of the CLEF eHealth Evaluation Lab[END_REF][START_REF] Goeuriot | Overview of the CLEF eHealth evaluation lab 2020[END_REF]. These labs have offered a recurring contribution to the creation and dissemination of text analytics resources, methods, test collections, and evaluation benchmarks in order to ease and support patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring eHealth information in a multilingual setting.

In 2021, the CLEF eHealth lab offered two shared task. The first task was on multilingual IE and the second task was on CHS. These tasks built on the IE and IR tasks offered by the CLEF eHealth lab series since its inception in 2013. Test collections generated by these shared tasks offered a specific task definition, implemented in a dataset distributed together with an implementation of relevant evaluation metrics to allow for direct comparability of the results reported by the systems evaluated on the collections. These established CLEF IE and IR tasks used a traditional shared task model for evaluation in which a community-wide evaluation is executed in a controlled setting: independent training and test datasets were used and all participants gained access to the test data at the same time, following which no further updates to systems were allowed. Shortly after releasing the test data (without labels or other solutions), the participating teams submitted their outputs from the frozen systems to the task organizers, who evaluated these results and reported the resulting benchmarks to the community.

The annual CLEF eHealth workhops and evaluation labs have matured and established their presence in 2012-2021. In total, 67 unique teams registered their interest and 11 teams took part in the 2021 tasks (7 in Task 1 on IE and 4 in Task 2 on IR). Given the significance of the tasks, all problem specifications, test collections, and text analytics resources associated with the lab have been made available to the wider research community through our CLEF eHealth website 17 .

Fig. 2 .

 2 Fig. 2. A snippet of the report in Figure 1, with manual annotations. Abbreviations: AE -Anatomical Entity, ABR -Abbreviation, MType -Type of Measure.

Fig. 3 .

 3 Fig. 3. Number and frequency of occurrences of the different kinds of entities in the annotated dataset for Task 1.

Fig. 4 .Fig. 5 .

 45 Fig. 4. CLEF eHealth Consumer Health Search Task 2021: Assessor's landing page

Fig. 7 .

 7 Fig. 7. Variation in the performance of different systems across different kinds of entities.

Table 1 .

 1 Overall results for the best performing system for each team on the SpRadIE task, sorted by lenient micro-averaged F1.

	lenient	exact

https://clefehealth.imag.fr/

Acknowledgements

The CLEF eHealth 2021 evaluation lab has been supported in part by the CLEF Initiative. It has also been supported in part by the Our Health in Our Hands (OHIOH) initiative of the Australian National University (ANU), as well as the ANU School of Computing, ANU Research School of Population Health, and Data61/Commonwealth Scientific and Industrial Research Organisation. OHIOH is a strategic initiative of the ANU which aims to transform health care by developing new personalised health technologies and solutions in collaboration with patients, clinicians, and health care providers. Moreover, the lab has been supported in part by the bi-lateral Kodicare (Knowledge Delta based improvement and continuous evaluation of retrieval engines) project funded by the French ANR (ANR-19-CE23-0029) and Austrian FWF. We are also thankful to the people involved in the annotation, query creation, and relevance assessment exercises. Last but not least, we gratefully acknowledge the participating teams' hard work. We thank them for their submissions and interest in the lab.


Leila Amgoud

Henri Prade

Threat, reward and explanatory arguments: generation and evaluation

Keywords:

Current logic-based handling of arguments has mainly focused on explanation-oriented purposes in presence of inconsistency, so only one type of argument has been considered. Several argumentation frameworks have then been proposed for generating and evaluating such arguments. However, recent works on argumentation-based negotiation have emphasized different other types of arguments such as threats, rewards, appeals. The purpose of this paper is to provide a logical setting which encompasses the classical argumentation-based framework and handles the new types of arguments. More precisely, we give the logical definitions of these arguments and their weighting systems. These definitions take into account that negotiation dialogues involve not only agents' beliefs (of various strengths), but also their goals (having maybe different priorities), as well as the beliefs on the goals of other agents. In other words, from the different belief and goal bases maintained by agents, all the possible threats, rewards, explanations, appeals which are associated with them can be generated.

Introduction

Various argument-based frameworks have been developed in defeasible reasoning [START_REF] Amgoud | A reasoning model based on the production of acceptable arguments[END_REF][START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF][START_REF] Prakken | Argument-based extended logic programming with defeasible priorties[END_REF] for generating and evaluating arguments. In that explanation-oriented perspective, only one type of argument has been considered. Namely, what we call explanatory arguments. Recent works on negotiation [START_REF] Amgoud | Reaching agreement through argumentation: A possibilistic approach[END_REF][START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF][START_REF] Parsons | Agents that reason and negotiate by arguing[END_REF][START_REF] Ramchurn | Persuasive negotiation for autonomous agents: a rhetorical approach[END_REF] have argued that argumentation plays a key role in finding a compromise. Indeed, an offer supported by a 'good argument' has a better chance to be accepted by another agent. Argumentation may also lead an agent to change its goals and finally may constrain an agent to respond in a particular way. For example, if an agent receives a threat, this agent may accept the offer even if it is not really acceptable for it. In addition to explanatory arguments studied in classical argumentation frameworks, the above works on argumentation-based negotiation have emphasized different other types of arguments such as threats, rewards, and appeals. In [START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF][START_REF] Ramchurn | Persuasive negotiation for autonomous agents: a rhetorical approach[END_REF], these arguments are treated as speech acts with pre-conditions and post-conditions. The purpose of this paper is to provide a logical framework which encompasses the classical argumentation-based framework and handles the new types of arguments. More precisely, we give the logical definitions of these arguments and their weighting systems. These definitions take into account the fact that negotiation dialogues involve not only agents' beliefs (of various strengths), but also their goals (having maybe different priorities), and the beliefs on the goals of other agents. Thus, from the different belief and goal bases maintained by an agent, any possible threats, rewards, explanations, appeals, which are associated with them can be generated. Note that our weighting systems for threats and rewards reflect the certainty that they can take place and the importance of their consequences. However, they don't account for the propensity of the agent to act or not as it promise to do. An illustrative example is provided in the last section.

Types of arguments

In what follows, L denotes a propositional language, classical inference, and ≡ logical equivalence. In all what follows, we suppose also that an agent P presents an argument to another agent C. Each negotiating agent has got a set G of goals to pursue, a knowledge base, K, gathering the information it has about the environment, and finally a base GO, containing what the agent believes the goals of the other agent are, as already assumed in [START_REF] Amgoud | Reaching agreement through argumentation: A possibilistic approach[END_REF]. K may be pervaded with uncertainty (the beliefs are more or less certain), and the goals in G and GO may not have equal priority. Thus, levels of certainty are assigned to formulas in K, and levels of priority are assigned to the goals. This leads to three possibilistic bases [START_REF] Dubois | Possibilistic logic[END_REF] that model gradual knowledge and preferences: K = {(ki, αi), i = 1, . . . , n}, G = {(gj, βj), j = 1, . . . , m}, GO = {(go l , δ l ), l = 1, . . . , p}, where ki, gj, go l are propositions of the language L and αi, βj, δ l are elements of [0, 1], or of any linearly ordered scale, finite or not. Both beliefs and goals are represented by propositional formulas. Thus a goal is viewed as a piece of information describing a set of desirable states (corresponding to the models of the associated proposition) one of which should be reached. Each of these bases is associated at the semantics level with a possibility distribution which rank-orders the possible states of the world according either to their plausibility or to their satisfaction level. For instance, for the base G this distribution is a kind of qualitative utility function which is all the greater for a given state of the world as the corresponding interpretation does not violate any important goal in G (for more details on the semantics of possibilistic logic see [START_REF] Dubois | Possibilistic logic[END_REF]). We shall denote by K * , G * and GO * the corresponding sets of classical propositions when weights are ignored. We distinguish between three categories of arguments according to their logical definitions: threats, rewards and explanatory arguments.

Threats

Threats are very common in human negotiation. They have a negative character and are applied to force an agent to behave in a certain way. Two forms of threats can be distinguished: 'You should do A otherwise I will do B' and 'You should not do A otherwise I will c 2004 CMNA IV, 4 th Workshop on Computational Models of Natural Argument Edited by Floriana Grasso, Chris Reed and Giuseppe Carenini ECAI 2004 do B'. The first case occurs when an agent P needs an agent C to do A and C refuses P then threatens C to do B which, according to its beliefs, will have bad consequences for C. Let us consider the following example:

Example 1 A mother asks her child (A) to carry out his school work and he refuses. The mother then threatens him (B) not to let him go to the party organized by her friend the next week-end.

The second kind of threats occurs when an agent C wants to do some action A, which is not acceptable for P . In this case, P threatens that if C insists to do A then it will do B which, according to P 's beliefs, will have bad consequences for C. To illustrate this kind of threat, we consider the following example borrowed from [START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF].

Example 2 A labor union insists on having (A) a wage increase.

The management says it cannot afford it, and asks the union to withdraw its request. The management threatens that, if it grants this increase, it will have (B) to lay off employees. According to the management, this will compensate for the higher operational cost that the increase will entail.

In fact, for a threat to be effective, it should be painful for its receiver and conflict with at least one of its goals. A threat is then made up of three parts: the conclusion that the agent who makes the threat wants, the threat itself and finally the threatened goal. In the case of of example 1, the mother has a threat in favor of having the school work done. Formally:

Definition 1 (Threat) A threat is a triple <H, h, φ> such that: 1) h is a proposition of the language L, 2) H ⊆ K * , 3) H ∪ {¬h} ¬φ such that φ ∈ GO * , 4
) H ∪ {¬h} is consistent and minimal (for set inclusion) among the sets satisfying the conditions 1, 2, 3. At will denote the set of all threats that may be constructed from the bases <K, G, GO>. H is the support of the threat, h its conclusion and φ is the threatened goal.

Such a definition allows h to be a proposition whose truth can be controlled by the agent (e.g the result of an action), as well as a proposition which is out of the control of the agent. For instance, "it rains and you are going to be wet". We may however restrict the set where h is taken, in order to exclude the last case. We may also allow for h = ⊥ (the contradiction). This corresponds to the case of a gratuitous threat. Here actions are just literals whose conditional or unconditional effects are described through propositions stored in K. Note that the above definition captures the two above forms of threats. Indeed, in the first case (You should do A otherwise I will do B), h = A and in the second case (You should not do A otherwise I will do B), h = ¬A. B refers to an action which may be inferred from H.In example 1, H includes {¬W orkDone → ¬LettingGoP arty}. The formal definition of threats is then slightly more general.

Example 3 Let us consider an agent P having the three following bases: K = {(¬ F inishW ork → overtime, 1)}, G = {(F inishW ork, 1)} and GO = {(¬overtime, 0.7)}. Let us suppose that the agent P asks the agent C to finish the work and that C refuses. P can then make the following threat: < {¬ F inishW ork → overtime}, F inishW ork, ¬overtime>.

Rewards

During a negotiation an agent P can entice agent C in order that it does A by offering to do an action B as a reward. Of course, agent P believes that B will contribute to the goals of C. Thus, a reward has generally, at least from the point of view of its sender, a positive character. As for threats, two forms of rewards can be distinguished: 'If you do A then I will do B' and 'If you do not do A then I will do B'.

Example 4 A sales agent tries to persuade a customer (A) to buy a computer by offering (B) a set of blank CDs.

Formally, a reward is defined in a way similar to threats as follows:

Definition 2 (Reward) A reward is a triple <H, h, φ> such that: 1) h is a proposition of the language L, 2) H ⊆ K * , 3) H ∪ {h} φ such that φ ∈ GO * , 4) H ∪ {h} is consistent and minimal (for set inclusion) among the sets satisfying the conditions 1, 2, 3. Ar will denote the set of all the rewards that can be constructed from <K, G, GO>. H is the support of the reward, h its conclusion and φ the rewarded goal.

Example 5 Let's consider an agent P having the three following bases: K = {(F inishW ork → HighBudget, 1), (HighBudget → HighSalary, 0.6)}, G = {(F inishW ork, 1)} and GO = {(HighSalary, 1)}. We suppose the agent P asks C to finish the work and C refuses. P can then present the following reward in favour of its request 'FinishWork': <{F inishW ork → HighBudget, HighBudget → HighSalary}, F inishW ork, HighSalary>.

In [START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF], another kind of arguments has been pointed out. It is the socalled appeal to self-interest. In this case, an agent P believes that the suggested offer implies one of C's goals. In fact, this case may be seen as a self-reward and consequently it is a particular case of rewards.

Explanatory arguments

Explanations constitute the most common category of arguments. In classical argumentation-based frameworks which have been developed for handling inconsistency in knowledge bases, each conclusion is justified by arguments. They represent the reasons to believe in the fact. Such arguments have a deductive form. Indeed, from premises, a fact or a goal is entailed. Formally: Definition 3 (Explanatory argument) An explanatory argument is a pair <H, h> such that: 1)

H ⊆ K * ∪ G * ∪ GO * , 2) H h,
3) H is consistent and minimal (for set inclusion) among the sets satisfying the conditions 1 and 2. Ae will denote the set of all the explanatory arguments that can be constructed from <K, G, GO>. H is the support of the argument and h its conclusion.

Example 6

Let us consider the case of an agent who wants to go to Sydney. K = {(conf erence, 0.8), (canceled, 0.4), (conf erence → Sydney, 1), (canceled → ¬conf erence, 1)}. G = {(Sydney, 1)} and GO = ∅. The agent wants to go to Sydney and justifies his goal by the following explanatory argument: <{conf erence, conference → Sydney}, Sydney>. Indeed, from the beliefs one can deduce Sydney.

In [START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF] other types of arguments called appeals are also considered. We argue that the different forms of appeals can be modeled as explanatory arguments. In what follows, we will show through examples how appeals can be defined in this way.

An appeal to prevailing practice: In this case, the agent believes that the opponent agent refuses to perform the requested action since it contradicts one of its own goals. However, the agent gives a counter-example from a third agent's actions, hoping it will serve as a convincing evidence. Of course, the third agent should have the same goals as the opponent and should have performed the action successfully.

Example 7 An agent P asks another agent C to make overtime. C refuses because he is afraid that this is punished by law. The bases of C are then: K = {(overtime → T oBeP unished, 1)}, G = {(F inishW ork, 1)} and GO = ∅. When the opponent C receives the offer overtime, he constructs an explanatory argument in favor of T oBeP unished: <{overtime, overtime → T oBeP unished}, T oBeP unished>. This argument confirms to him that his goal will be violated and he refuses the offer. The proponent P reassures him by telling that another colleague makes overtime and he never has problems with the law. In fact, he presents the following counter-argument: <{overtime, ¬T oBeP unished}, ¬(overtime → T oBeP unished)>. This last argument is an appeal to prevailing practice.

An appeal to past promise:

In this case, the agent expects the opponent agent to perform an action based on past promise. Let us illustrate it by the following example:

Example 8 A child asks his mother to buy a gift for him and the mother refuses. The child points out that she promised to buy something to him if he succeeds at his examinations. The bases of the child are: K = {(success, 1), (success → gift, 1)}, G = {(gift, 1)} and GO = ∅. The child's argument is then: <{success, success → gift}, gift>.

A counter-example:

This argument is similar to 'appeal to prevailing practice'; however, the counter-example is taken from the opponent agent's own history of activities. In this case, the counter argument produced by the proponent should be constructed from the beliefs of the opponent. In the case of example 7, the support of the counter-argument should be included in the base of C. Thus, C would have a conflicting base. These three types of arguments have the same nature and they are all deductive. They are defined logically as explanatory arguments. The nature of these arguments, however, plays a key role in the strategies used by the agents. For example, a counter-example may quickly lead the other agent to change its mind than an appeal to prevailing practice.

As a conclusion of this section let us emphasize that a threat or a reward cannot be reduced to an explanatory argument as can be already seen on the definitions. On the one hand, explanatory arguments may lead the other agent to revise its beliefs / goals (they affect the mental states of the agent), while threats or rewards may encourage or refrain the agent to do something. On the other hand, the key entailment condition in the definition of threat, reward and explanatory arguments allows the following respective readings, H threatens φ provided ¬h, H rewards φ provided h and finally H explains h. Despite this apparent formal similarity, the two first expressions should be understood in a reverse way from an explanatory perspective. Indeed, in case of a threat or a reward this is rather the pair (H, φ) (although φ is the consequence of the entailment) which provides a kind of abductive explanation for h. Moreover, another important feature of definitions 1 and 2 is the requirement that φ belongs to GO * which is distinct from K * from which H is taken.

The strength of arguments

In [START_REF] Amgoud | A reasoning model based on the production of acceptable arguments[END_REF], it has been argued that arguments may have different forces according to the beliefs from which they are constructed. The basic idea is that arguments using more certain beliefs are stronger than arguments using less certain beliefs. Thus, a level of certainty is assigned to each argument. These certainty levels make it possible to compare arguments. In fact, an argument A is preferred to another argument B iff A is stronger than B. As mentioned before, each of the three bases <K, G, GO> is pervaded with uncertainty or equipped with priority levels. From these degrees, we first define the force of an explanatory argument.

Definition 4 (Force of an explanatory argument)

Let A = <H, h> ∈ Ae. The force of <H, h> is F orce(A) = min{ai such that (ϕi, ai) ∈ H}.
Example 9 In example 6, the force of the explanatory argument <{conf erence, conference → Sydney}, Sydney> is 0.8. Whereas, the force of the argument <{canceled, canceled → ¬conf erence}, ¬conf erence> is 0.4.

Concerning threats, things are different since a threat involves goals and beliefs. Intuitively, a threat is strong if, according to the most certain beliefs, it invalidates an important goal. A threat is weak if, according to less certain beliefs, it invalidates a less important goal. In other terms, the force of a threat represents to what extent the agent (the agent sending it or receiving it) is certain that it will violate its most important goals. Hence the use of a 'min' combination of the certainty of H and the priority of the threatened goal. Indeed, a fully certain threat against a very low priority goal is not a very serious goal. Formally: Definition 5 (Force of a threat) Let A = <H, h, φ> ∈ At. The force of a threat A is F orce(A) = min(α, β) such that α = min{ai such that (ϕi, ai) ∈ H} and (φ, β) ∈ GO ∪ G.

Note that when a threat is evaluated by the proponent (the agent presenting the threat), then (φ, α) ∈ GO. However, when it is evaluated by its receiver, (φ, α) ∈ G.

Example 10

In example 3 the force of the threat < {¬ F inishW ork → overtime}, F inishW ork, ¬overtime> is min(1, 0.7) = 0.7.

As for threats, rewards involve beliefs and goals. Thus, a reward is strong when it is for sure that it will contribute to the achievement of an important goal. It is weak if it is not sure that it will contribute to the achievement of a less important goal. In fact, the forces of arguments will play two roles: on the one hand they allow an agent to compare different threats or different rewards in order to select the "best" one. On the other hand, the forces are useful for determining the acceptable arguments among the conflicting ones.

Conflicts between arguments

Due to inconsistency in knowledge bases, arguments may be conflicting. In this section, we will show the different kinds of conflicts which may exist between arguments of the same nature and also between arguments of different natures.

In what follows, we denote by Ax the set of arguments of nature x with x ∈ {t, r, e}.

Conflicts between explanatory arguments

In classical argumentation frameworks, different conflict relations between what we call in this paper explanatory arguments have been defined. The most common ones are the relations of rebuttal where two explanatory arguments support contradictory conclusions and the relation of undercut where the conclusion of an explanatory argument contradicts an element of the support of another explanatory argument.

Definition 8 Let <H, h>, <H , h > ∈ Ae. <H, h> def eatse <H , h > iff ∃ h" ∈ H such that h ≡ ¬h", or h ≡ ¬h .
Example 12 (Continued) In example 6, the explanatory argument <{canceled, canceled → ¬conf erence}, ¬conf erence> undercuts the argument <{conf erence, conference → Sydney}, Sydney> whereas it rebuts the argument <{conf erence}, conference>.

Conflicts between threats / rewards

Two arguments of type "threat" may be conflicting for one of the three following reasons:

• the support of an argument infers the negation of the conclusion of the other argument. This case occurs when, for example, an agent P threatens C to do β if C refuses to do α, and at his turn, C threatens P to do δ if P does β. • the threats support contradictory conclusions.

• the threatened goals are contradictory. Since a rational agent should have consistent goals, this case arises when the two threats are given by different agents.

As for threats, rewards may also be conflicting for one of the three following reasons:

• the support of an argument infers the negation of the conclusion of the other argument. This occurs when an agent P promises to C to do β if C refuses to do α. C, at his turn, promises to P to do δ if P does not pursue β. • the rewards support contradictory conclusions. This kind of conflict has no sense if the two rewards are constructed by the same agent. Because this means that the agent will contribute to the achievement of a goal of the other agent regardless what the value of h is. However, when the two rewards are given by different agents, this means that one of them wants h and the other ¬h and each of them tries to persuade the other to change its mind by offering a reward. • the rewarded goals are contradictory. Definition 9 Let <H, h, φ>, <H , h , φ > ∈ At (resp. ∈ Ar). <H , h , φ > def eatst <H, h, φ> (resp. <H , h , φ > def eatsr <H, h, φ>) iff: H ¬h, or h ≡ ¬h , or φ ≡ ¬φ .

Note that the conflict relation between threats (or rewards) is generally symmetric.

Mixed conflicts

It is obvious that explanatory arguments can defeat threats and rewards. In fact, one can easily undercut an element used in the support of a threat or a reward. The defeat relation used in this case is the relation "undercut" defined above. An explanatory argument can also defeat a threat or a reward when the two arguments have contradictory conclusions. Finally, an explanatory argument may conclude the negation of the goal threatened (resp. rewarded) by the threat (resp. the reward). Formally:

Definition 10 Let <H, h> ∈ Ae and <H , h , φ> ∈ At (resp. ∈ Ar). <H, h> def eatsm <H , h , φ> iff: ∃h" ∈ H such that h ≡ ¬h" or h ≡ ¬h or h ≡ ¬φ.

Conclusion

The aim of this paper is twofold. First, it presents a logical framework in which the arguments are defined, the different conflicts which may exist between them are described, the force of each kind of arguments is defined in a clear way on the basis of the different bases of an agent and finally the acceptability of the arguments is studied. This work can be seen as a first formalization of different kinds of arguments. This is beneficial both for negotiation dialogue and also for argumentation theory since in classical argumentation the nature of arguments is not taken into account or the arguments are supposed to have the same nature.

Definition 6 (Definition 7 (

 67 Force of a reward) Let A = <H, h, φ> ∈ Ar. The force of a reward A is F orce(A) = min(α, β) such that α = min{ai such that (ϕi, ai) ∈ H} and (φ, β) ∈ GO ∪ G.Example 11In example 5, the force of the reward <{F inishW ork → HighBudget, HighBudget → HighSalary}, F inishW ork, HighSalary> is 0.6.The forces of the arguments makes it possible to compare different arguments as follows: Preference relation) Let A1 and A2 be two arguments of Ax. A1 is preferred to A2, denoted by A1 A2, iff F orce(A1) ≥ F orce(A2).

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