Emotion detection is a central problem in NLP, with recent progress driven by transformer-based models trained on established datasets. However, little is known about the linguistic regularities that characterize how emotions are expressed across different corpora and labels. This study examines whether linguistic features can serve as reliable interpretable signals for emotion recognition in text. We extract emotion-specific linguistic signatures from 13 English datasets and evaluate how incorporating these features into transformer models impacts performance. Our RoBERTa-based models enriched with high level linguistic features achieve consistent performance gains of up to +2.4 macro F1 on the GoEmotions benchmark, showing that explicit lexical cues can complement neural representations and improve robustness in predicting emotion categories.

A robot can affect its social environment beyond the person who is interacting with it. Within this context, we believe it is important to explore Human-Robot Interactions (HRI) in complex social settings. We examine the effect of different robot shapes in a multi-person context during dance routines and observe how the design of the robot enhances the artistic process. We identify key factors through which human preferences are being shaped, within a novel third party setting human-robot-human interaction (HRHI).

This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features.

Argumentation is a process of evaluating and comparing sets of arguments. Ranking-based semantics received a lot of attention recently. All of the semantics introduced so far are applicable to binary attack relations. In this paper, we study a more general case when sets of arguments can jointly attack an argument. We generalise existing postulates for ranking-based semantics to fit this framework, define a general variant of h-categoriser, prove that it converges for every argumentation framework and study the postulates it satisfies. We also study the link between binary and hypergraph version of h-categoriser.

International audience

Logic-based argumentation is a well-known approach for reasoning with inconsistent logic knowledge bases.Such frameworks have been shown to suffer from a major practical drawback consisting of a large number of arguments and attacks.To address this issue, we provide an argumentation framework that considers sets of attacking arguments and provide a theoretical analysis of the new framework with respect to its syntactic and semantic properties.We provide a tool for generating such argumentation frameworks from a Datalog knowledge base and study their characteristics.

In this paper we consider a bipolar graph structure encoding conflicting knowledge with logic formulas.Gradual semantics provide a way to assign strength values in the unit interval to nodes (i.e.logical inference steps) in the bipolar graph.Here, we introduce a new class of semantics based on the notions of T-norms and T-conorms and show that they handle circular reasoning and satisfy desirable properties such as provability and rewriting.

Bipolar argumentation studies argumentation graphs where attacks are combined with another relation between arguments. Many kind of relations (e.g. deductive support, evidential support, necessities etc.) have been defined and investigated from a Dung semantics perspective. We place ourselves in the context of argumentation systems with necessities and provide the first study to investigate ranking semantics in this setting. To this end, we (1) provide a set of postulates specifically designed for necessities and (2) propose the first ranking-based semantics in the literature to be shown to respect these postulates.

In this demonstration we focus on the task of a data engineer choosing what tool to perform defeasible reasoning with a first order logic knowledge base.

In this demonstration extended abstract we present a workflow of how to generate a benchmark for logical argumentation graphs issued from knowledge bases expressed using existential rules.

To address the needs of the EU NoAW project, in this paper we solve the problem of efficiently generating the argumentation graphs from knowledge bases expressed using existential rules. For the knowledge bases without rules, we provide a methodology that allows to optimise the generation of argumentation graphs. For knowledge bases with rules, we show how to filter out a large number of arguments and reduce the number of attacks.

To address the needs of the EU NoAW project, in this paper we introduce a new modular framework that generates viewpoints (i.e. extensions) based on ranking argumentation semantics by considering a selection function, a ranking on arguments and a lifting function as its input parameters. We study the different combinations of the input parameters and introduce a set of postulates investigated for the framework's different classes of output.

In this paper, we place ourselves in the Ontology Based Data Access (OBDA) setting and investigate reasoning with inconsistent existential rules knowledge bases. We use the notion of inconsistency measures on sets of facts to rank and filter repairs. We propose a generic framework to answer queries by using the best repairs and study productivity and properties of such a framework.

In this paper we present a prototype of a framework called dalek (DiALectical Explanation in Knowledge-bases). This framework implements dialectical approaches to explain query answers in inconsistent knowledge bases. The motivation behind the prototype is as follows: given an inconsistent knowledge base represented within Datalog±, a semantics for handling inconsistency and a query 𝒬, the goal is to explain why 𝒬 is accepted or not accepted under such semantics. The explanation takes a dialogical form (cf. [1,3]).

In this paper we demonstrate how to benefit from structured argumentation frameworks and their implementations to provide for reasoning capabilities of Ontology Based Data Access systems under inconsistency tolerant semantics. More precisely, given an inconsistent Datalog±knowledge base we instantiate it using the ASPIC+framework and show that the reasoning provided by ASPIC+is equivalent to the main inconsistent tolerant semantics in the literature. We provide a workflow that shows the practical interoperability of the logic based frameworks handling Datalog±and ASPIC+.

Given a logic-based argumentation framework built over a knowledge base in a logical language and a query in that language, the query is universally accepted if it is entailed from all extensions. As shown in [2, 14], universal acceptance is different from skeptical acceptance as a query may be entailed from different arguments distributed over all extensions but not necessarily skeptical ones. In this paper we provide a dialectical proof theory for universal acceptance in coherent logic-based argumentation frameworks. We prove its finiteness, soundness, completeness, consistency and study its dispute complexity. We give an exact characterization for non-universal acceptance and provide an upper-bound for universal acceptance.

In this paper we approach both procedural and substantive irrationality of artificial agent cognitive systems and consider that when it is not possible for an agent to make a logical inference (too expensive cognitive effort or not enough knowledge) she might replace certain parts of the logical reasoning with mere associations.

This paper deals with the process of decision making in the reverse engineering mode and highlights the need for polyvalent information. Three aspects are considered. 1) Reverse engineering implies a preliminary assumption: having defined a desired outcome of the decision process. Defining goals on the possible outcomes is a complex, multi-actor process based on ubiquitous information. Once identified at best, several alternative scenarios may lead to the desired outcome. The first issue consists in evaluating these alternative scenarios. 2) While taking into consideration the positive consequences that the different alternatives will generate, the decision process has to allow for possible negative impacts, which are not explicitly expressed in the defined goals. We thus consider the reverse engineering process has to be bipolar and take rejections into account. 3) Finally, the simultaneous achievement (respectively, avoidance) of several goals (respectively, rejections) is not always possible and depends, in particular, on whether the actions leading to each of these goals (respectively avoiding these rejections) are compatible or not. We thus seek the “best” compatible set of actions and propose to define it as optimizing the bipolar preferences expressed on the outcomes. The approach is both graphical and logical and is focused on a case study in breadmaking technology.

We address the problem of explaining Boolean Conjunctive Query (BCQ) failure in the presence of inconsistency within the Ontology-Based Data Access (OBDA) setting, where inconsistency is handled by the intersection of closed repairs semantics (ICR) and the ontology is represented by Datalog+/− rules. Our proposal relies on an interactive and argumentative approach where the processes of explanation takes the form of a dialogue between the User and the Reasoner. We exploit the equivalence between argumentation and ICR-semantics to prove that the Reasoner can always provide an answer for user's questions.

Within the framework of the European project EcoBioCap (ECOefficient BIOdegradable Composite Advanced Packaging), aiming at conceiving the next generation of food packagings, we introduce an argumentation-based tool for management of conflicting viewpoints between preferences expressed by the involved parties (food and packaging industries, health and waste management etc.).

With the aim of evaluating and improving link quality in bibliographical knowledge bases, we develop a decision support system based on partitioning semantics. Two such semantics have been proposed, the novelty of this approach consisting on using symbolic values criteria for partitioning. In this paper we investigate the limits of those partitioning semantics: how the characteristics of the input (objects and criteria) influences characteristics of the result, namely correctness of the result and execution time.

We introduce in this paper a quantitative preference based argumentation system relying on ASPIC argumentation framework [1] and fuzzy set theory. The knowledge base is fuzzified to allow agents expressing their expertise (premises and rules) attached with grades of importance in the unit interval. Arguments are then attached with a strength score aggregating the importance expressed on their premises and rules. Extensions, corresponding to subsets of consistent arguments, are also attached with forces computed based on their strong arguments. The forces are used then to rank extensions from the strongest to the weakest one, upon which decisions can be made. We have also shown that the strength preference relation defined over arguments is reasonable [2] and our fuzzy ASPIC argumentation system can be seen as a computationally efficient instantiation of the generic model of structured argumentation framework introduced in [2].

In this paper we present the EcoBioCap project and the modelling needs of this project in terms of argumentation based preference aggregation. The aim of the paper is to well describe the problem encountered in this context and to propose a preference logic in line with the expressivity needed by the application. We then show how to embed this logic within the ASPIC+ system. Finally, we show how argument by expert opinion could be integrated within our framework where preference aggregation needs to take into consideration the different expertise of the project stakeholders.

The specification of acceptable behaviour can be achieved via the use of obligations, permissions and prohibitions, collectively known as norms, which identify the states of affairs that should, may, or should not hold. Norms provide the ability to constrain behaviour while preserving individual agent autonomy. While much work has focused on the semantics of norms, the design of normative systems, and in particular understanding the impact of norms on a system, has received little attention. Since norms often interact with each other (for example, a permission may temporarily derogate an obligation, or a prohibition and obligation may conflict), understanding the effects of norms and their interactions becomes increasingly difficult as the number of norms increases. Yet this understanding can be critical in facilitating the design and development of effective or efficient systems. In response, this paper addresses the problem of norm explanation for Naïve users by providing of a graphical norm representation that can explicate why a norm is applicable, violated or complied with, and identify the interactions between permissions and other types of norms. We adopt a conceptual graph based semantics to provide this graphical representation while maintaining a formal semantics.

In this paper we propose a novel approach to represent coalitional games, called a Coalition-Flow Network (CF-NET), that builds upon a generalization of the network flow literature. Specifically, this representation is based on our observation that the coalition formation process can be viewed as the problem of directing the flow through a network where every edge has certain capacity constraints.

A distributed decision support system involving multiple clinical centres is crucial to the diagnosis of rare diseases. Although sharing of valid diagnosed cases can facilitate later decision making, possibly from geographically different centres, the released information could reveal patient privacy if it is not properly protected. Clinical centres may have to impose their distinct regulations and rules that govern the use of their data externally. The collaboration of centres, therefore, must respect the collective policies and ideally, serve users the most appropriate and useful resources possible in the system according to the past experience. In this way, the system’s value is entrusted and even elevated through continuous collaboration. We present in this paper a link-anonymised data scheme and in addition to that, a security model that together enforce privacy data security and secure resource access for distributed clinical centres. Our illustration of the approach involves a prototype medical decision support system, HealthAgents, for brain tumour diagnosis.

In this paper we introduce a new graph based bidding language for combinatorial auctions. In our language, each bidder submits to the arbitrator a generalized flow network (netbid) representing her bids. The interpretation of the winner determination problem as an aggregation of individual preferences represented as flowbids allows building an aggregate netbid for its representation. Labelling the nodes with appropriate procedural functions considerably improves upon the expressivity of previous bidding languages.

This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing electronic healthcare records in a consistent manner. We present HADOM (HealthAgents domain ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of conceptual graphs (CGs). This allows to build upon the existing ontology permitting for modularity and flexibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval.

We present in this paper an adaptive security model for Multi-agent systems. A security meta-model has been developed in which the traditional role concept has been extended. The new concept incorporates the need of both security management as used by role-based access control (RBAC) and agent functional behaviour in agent-oriented Software Engineering (AOSE). Our approach avoids weaknesses of traditional RBAC approaches and provides a practically usable security model for multi-agent systems (MAS). A unified role interaction model framework has been put forward that incorporates not only functional requirements but also security constraints in MAS. A security policy rule scheme has been used to express security requirements in relation to affective roles. The major contribution of the work is that little redevelopment effort will be required when security is to be engineered into the overall MAS architecture, hence minimising the impact of the security requirements changes to the MAS architecture. We illustrate the approach through its potential application in a clinical trial setting involving a prototype medical decision support system, HealthAgents.

Software systems must be capable of coping with continuous requirements changes and at the same time wisely make use of emerging components and services to remain useful in their environment. In this paper, the adaptive agent model (AAM) approach is proposed. The AAM uses configurable interaction model to drive adaptive agent behaviour. The model captures user requirements and is maintained by experts at a high level of abstraction. The AAM interaction model has been discussed with regard to interaction specification and interaction coordination, in line with a coordination language for the OpenKnowledge project. A major benefit of using the approach is agents can dynamically choose disparate components and services already developed for computation via their interaction with each other at runtime, when a new interaction model has been configured for them towards an emerging business goal. A simple expert seeking scenario has been used to illustrate the approach.

Ontologies, as a knowledge representation paradigm, rely on semantics for performing logical inference. However, in the context of ontology query and reuse, the semantics are hard to be manipulated. When searching for ontologies and identifying potentially relevant candidates, a mechanism is required to exploit (i) the appropriate semantic restrictions as well as the (ii) meaning of concepts/properties. This paper proposes an approach that will address both requirements. The contribution of this paper is to conceptually detail how a set of ontology indices can be obtained. More specifically, to obtain a relevant set of keywords describing an ontology, we use logic-based constructors to generate an initial set of keywords. The meaning of these keywords is further explored by consulting a comprehensive text corpora serving as the background/domain knowledge. The novelty of our work lies in the combination of logic-based formalisms with a collaboratively contributed web repository. We illustrate our work by the means of a real world example.