This paper presents an experiment on the effects of inter-agents emotional alignment, a prerequisite for empathic communication, in Human-Robot Interaction (HRI). We describe a pipeline built around the Pepper robot with the idea of verifying the effect of emotionally-aligned communication toward a user. In particular, our goal is twofold, in that we investigate if and to what extent an emotionally-aligned, empathic dialogue impacts on (i) the communication style of the user, and (ii) the persuasive effectiveness of the robot, intended as its ability to alter or reinforce its interlocutors' attitudes and beliefs about the conversation topic. Both these aspects have been assessed in a controlled experiment with 46 participants, comparing a condition where the robot addresses participants with emotionally neutral sentences with a condition where the robot provides answers tailored to the emotions expressed in participants' input utterances. Results show how emotion alignment acts as an effective trigger for the elicitation of different communication styles of the users but also that, contrary to what we expected, it does not play any persuasive effect.

We propose a system called METCL (Metaphor Elaboration in Typicality-Based Compositional Logic) able to generate and identify metaphors by using the TCL reasoning framework, specialized in human-like commonsense concept combination. We show that METCL is able to improve both state of-the-art Large Language Models (e.g DeepSeek-R1, GPT-4o, Qwen2.5-Max) and symbolic ones in the task of metaphor identification. Additionally, we show how the metaphors generated by METCL are generally well accepted by human subjects. The obtained results are encouraging and pave the way to research in automatic metaphor generation and comprehension based on the assumption that metaphors interpretation can be partially regarded as a categorization problem relying on generative commonsense concept combination.

The paper presents an experiment on the effects of adaptive emotional alignment between agents, considered a prerequisite for empathic communication, in Human-Robot Interaction (HRI). Using the NAO robot, we investigate the impact of an emotionally aligned, empathic, dialogue on these aspects: (i) the robot’s persuasive effectiveness, (ii) the user’s communication style, and (iii) the attribution of mental states and empathy to the robot. In an experiment with 42 participants, two conditions were compared: one with neutral communication and another where the robot provided responses adapted to the emotions expressed by the users. The results show that emotional alignment does not influence users’ communication styles or have a persuasive effect. However, it significantly influences attribution of mental states to the robot and its perceived empathy.

This article presents an affective-based sensemaking system for grouping and suggesting stories created by the users about the cultural artefacts in a museum. By relying on the TCL commonsense reasoning framework, the system exploits the spatial structure of the Plutchik’s “wheel of emotions” to organize the stories according to their extracted emotions. The process of emotion extraction, reasoning, and suggestion is triggered by an app, called GAMGame, and integrated with the sensemaking engine. Following the framework of Citizen Curation, the system allows classifying and suggesting stories encompassing cultural items able to evoke not only the very same emotions of already experienced or preferred museum objects but also novel items sharing different emotional stances and, therefore, able to break the filter bubble effect and open the users’ view toward more inclusive and empathy-based interpretations of cultural content. The system has been designed tested, in the context of the H2020EU SPICE project (Social cohesion, Participation, and Inclusion through Cultural Engagement), in cooperation with the community of the d/Deaf and on the collection of the Gallery of Modern Art (GAM) in Turin. We describe the user-centered design process of the web app and of its components and we report the results concerning the effectiveness of the diversity-seeking, affective-driven, recommendations of stories.

In this work we exploit a recently introduced nonmonotonic extension of Description Logics, able to deal with the problem of knowledge invention via commonsense concept combination, to dynamically generate novel editorial contents in the context of a real broadcasting company: RAI – Radiotelevisione Italiana, the Italian public broadcaster. In particular, we introduce the system implementing such logic, i.e. DENOTER: Dynamic gEnerator of NOvel contents in mulTimEdia bRoadcasting (available online at the URL: http://di.unito.it/denoter), that has been applied and tested in the online multimedia platform of RAI (i.e. RaiPlay) as a tool for both the generation/suggestion of novel genres of multimedia on-demand contents and the reclassification of the available items within such new genres. Our system works by extracting the typical properties characterizing the available genres (with a standard information extraction pipeline) and by building novel classes of genres as the result of a creative combination of such extracted representations. We have tested DENOTER (i) by reclassifying the available contents in RaiPlay with respect to the new generated genres (ii) with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended contents (iii) with a qualitative evaluation done with a small group of experts of RAI. The obtained results are encouraging and pave the way to many possible further improvements and research directions.

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of the combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC+TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition.We first extend the logic of typicality ALC+TR by typicality inclusions of the form p::T(C)⊑D, whose intuitive meaning is that ‘we believe with degree p about the fact that typical Cs are Ds’. As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is ExpTime-complete as for the underlying standard Description Logic ALC.

In this work we describe a knowledge based system employed for the dynamic, goal-directed and creative generation of novel knowledge in cognitive agents. This system exploits a recently introduced extension of a Description Logic of typicality (TCL) able to combine prototypical (i.e. commonsense) descriptions of concepts. In particular: given a goal expressed as a set of properties, in case an intelligent agent cannot find a concept in its knowledge base able to fulfill these properties, our system exploits the Description Logic TCL in order to find at least two concepts whose creative combination allows to satisfy the goal. The knowledge base of the agent is then extended via a mechanism of commonsense concept combination where the resulting combined concept represents the solution for the initial goal. The proposed approach has been tested in the task of object composition and compared with the responses provided by the OROC system.

In this work we describe a novel approach to goal-oriented knowledge capturing that, differently from the standard knowledge acquisition pipelines, employs a dynamic conceptual reframing mechanismrelying on a non monotonic reasoning procedure. This approachhas been implemented in a knowledge based system able to findthe solution to not directly satisfiable goals by recombining, in aninnovative way, at least two concepts of a given knowledge base(KB). The output of such combinatorial mechanism results, de facto, in an extension of the initial KB able to satisfy the original goal. The proposed approach has been tested in the task of goal-driven concept invention and has been compared with human responses.

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and – in addition – that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual- PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.

The research in Artificial Intelligence (AI) has been based, from an historical standpoint, on a strong collaboration between computer scientists, psychologists, engineers, philosophers, linguists ...

In this work we present a knowledge-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the classical representational and reasoning capabilities of the ontology-based frameworks towards the realm of the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality based one (grounded on the conceptual spaces framework). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science with the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorisation task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially extends the representational and reasoning ‘conceptual’ capabilities of standard ontology-based systems.

In recent years, a number of projects have been undertaken for the access to cultural heritage that rely on metadata derived from an ontology. Ontologies also open to visualization techniques that leverage the structure of semantic relations to bridge the gap between the complexity of data and the need for immediate, simplified access. In this paper, we address the representation and access to the intangible cultural heritage, that has received much attention recently and typically lacks a physical counterpart. In order to illustrate this approach, we describe two case studies in ontologies and visualization for cultural heritage. In the first case study, the user is immersed in a 3D labyrinth where turning points and paths represent, respectively, a set of cultural artifacts and the semantic relations holding among them, the second case study relies on an ontology of contemporary art and a cartographic visualization of the public art in a city.

In this work we present an ontology-based system equipped with a hybrid architecture for the representation of conceptual information. The proposed system aims at extending the representational and reasoning capabilities of classical ontology-based systems towards more realistic and cognitively grounded scenarios, such as those envisioned by the prototype theory. The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science and the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves on the representational and reasoning “conceptual” capabilities of standard ontology-based systems

The representation of characters' intentions in a story is of great importance for media scholars and analysts, and it is susceptible of applicative scenarios within the media industry. In this paper, we introduce an interactive system for the visualization of a story analysis based on a plan-based representation of the characters' intentions. The system relies on an ontology of drama and builds upon the unrestricted annotation provided by narrative enthusiasts and media students. The system is able to build the mapping between a library of plans and the users' annotation, and to visualize the contributions of the several characters' intentions to the whole plot. The system was tested on the analysis of a short movie.