The growing impact of artificial intelligence on everyday life has fueled the emergence of Human-Centered Artificial Intelligence (HCAI), an approach aimed at developing intelligent systems that not only emulate and extend human capabilities but also foster meaningful collaboration, societal integration, and ethical responsibility. In this work, we present a comprehensive survey of the role of argumentation in HCAI, examining its theoretical foundations, methodologies, and applications, as well as its potential to enhance communication, informed decision-making, and responsible human-AI interaction across diverse domains. Building on this analysis, we introduce the Argumentation-based Proof-Event Calculus (APEC), a formal and aligned framework designed to support trustworthy collaboration between humans and AI agents in goal-oriented decision processes. The proposed APEC framework advances HCAI by explicitly incorporating temporal dynamics, evaluating both individual and collective agent contributions in multi-agent systems, and supporting the iterative refinement and evolution of decisions toward well-justified outcomes.

Defining and reasoning about goals in multi-actor dialectical systems requires formalizing often incomplete or unclear stakeholder requirements within a goal model. This process involves actor dialogue to clarify assumptions, resolve gaps, and justify modeling decisions, making argumentation central to validating decisions collaboratively. This work explores how argumentation theories can capture the informal reasoning of multiple actors by integrating two frameworks: Argumentation-based Proof-Events (APEC), which treats goal-oriented problem solving as a social and temporal process, and Provers’ System (PS), which models the internal reasoning and meta-level attributes of individual actors. By combining these, the proposed APEC-PS framework bridges internal cognitive reasoning with external collaborative dynamics, offering a more comprehensive view of actor interactions and argument structures. Rooted in symbolic AI, it employs formal logic and structured representations to enable goal-oriented defeasible reasoning. This integration is particularly well-suited for managing evolving knowledge and uncertainty in open-ended decision-making contexts. We utilize a domain-agnostic example from mathematical practices, exemplified by the Mini-Polymath 4 project, to showcase how the model’s abstract and generic nature can support diverse goal-oriented applications through dynamic actor interactions without being constrained by specific domain aspects.

Creativity, informal reasoning and dynamic exchange of ideas form the pulsating heart of mathematical creation. In this approach, the concept of a mathematical object surpasses conventional boundaries of formal presentation, as they also encompass the intention to prove, significant creative stages within the proving, and the overall experience of prover's journey, which may involve arguments, debates, discovery insights, aesthetic visualizations, and narrative elements. In this paper, we present two conceptual frameworks, namely Argumentation-based Proof-Events Calculus (APEC) and Mathematical RUPAs, in order to provide distinct yet interconnected perspectives on informal thinking, knowledge creation, and proving in mathematics. Following the two perspectives, we explore the nature of mathematical objects, the diverse roles of provers, and the influence of social and pluralistic factors in proving practices. Through this analysis, we aim to create a synthesis, named RUPAPEC, that elucidates how the creative and sociocultural dimensions presented in these two approaches can come together into a harmonious whole by mutually reinforcing each other. This theoretical synthesis embodies an ontological exploration delineating the essence and existence of mathematical objects as dynamic entities shaped by creative cognitive processes and interactive dialogues.

This paper explores the relationship between informal reasoning, creativity in mathematics, and problem solving. It underscores the importance of environments that promote interaction, hypothesis generation, examination, refutation, derivation of new solutions, drawing conclusions, and reasoning with others, as key factors in enhancing mathematical creativity. Drawing on argumentation logic, the paper proposes a novel approach to uncover specific characteristics in the development of formalized proving using “proof-events.” Argumentation logic can offer reasoning mechanisms that facilitate these environments. This paper proposes how argumentation can be implemented to discover certain characteristics in the development of formalized proving with “proof-events”. The concept of a proof-event was introduced by Goguen who described mathematical proof as a multi-agent social event involving not only “classical” formal proofs, but also other informal proving actions such as deficient or alleged proofs. Argumentation is an integral component of the discovery process for a mathematical proof since a proof necessitates a dialogue between provers and interpreters to clarify and resolve gaps or assumptions. By formalizing proof-events through argumentation, this paper demonstrates how informal reasoning and conflicts arising during the proving process can be effectively simulated. The paper presents an extended version of the proof-events calculus, rooted in argumentation theories, and highlights the intricate relationships among proof, human reasoning, cognitive processes, creativity, and mathematical arguments.

As automation in robotics and artificial intelligence is increasing, we will need to automate a growing amount of ethical decision making. However, ethical decision-making raises novel challenges for designers, engineers, ethicists, and policymakers, who will have to explore new ways to realize this task. For example, engineers building wearable robots should take into consideration privacy aspects and their different context-based scenarios when programming the decision-making procedures. This in turn requires ethical input in order to respect norms concerning privacy and informed consent. The presented work focuses on the development and formalization of models that aim at ensuring a correct ethical behavior of artificial intelligent agents, in a provable way, extending and implementing a logic-based proving calculus. This leads to a formal theoretical framework of moral competence that could be implemented in artificial intelligent systems in order to best formalize certain parameters of ethical decision-making to ensure safety and justified trust.

Proof requires a dialogue between agents to clarify obscure inference steps, fill gaps, or reveal implicit assumptions in a purported proof. Hence, argumentation is an integral component of the discovery process for mathematical proofs. This work presents how argumentation theories can be applied to describe specific informal features in the development of proof-events. The concept of proof-event was coined by Goguen who described mathematical proof as a public social event that takes place in space and time. This new meta-methodological concept is designed to cover not only “traditional” formal proofs but all kinds of proofs and inference steps, including incomplete or purported proofs. Our approach attempts to make proof-events more comprehensive to express the complete trajectory of a mathematical proof-event until the ultimate validation of the proving outcome. Thus, we advance an extended version of proof-event calculus which is built on argumentation theories designed to capture the internal and external structure of collaborative mathematical practice and highlight the relationship between proof, human reasoning, and cognitive processes. In addition, another area in which argumentation can make a significant contribution is dealing with the defeasible knowledge of the Web which is a product of its open and ubiquitous nature. This approach seems to be sufficient for the presentation of Web-based proving processes as manifested in the case of the Mini-Polymath 4 project.

Abstract As automation in artificial intelligence is increasing, we will need to automate a growing amount of ethical decision making. However, ethical decision- making raises novel challenges for engineers, ethicists and policymakers, who will have to explore new ways to realize this task. The presented work focuses on the development and formalization of models that aim at ensuring a correct ethical behaviour of artificial intelligent agents, in a provable way, extending and implementing a logic-based proving calculus that is based on argumentation reasoning with support and attack arguments. This leads to a formal theoretical framework of ethical competence that could be implemented in artificial intelligent systems in order to best formalize certain parameters of ethical decision-making to ensure safety and justified trust.

Abstract In this paper, we introduce the subject of the special issue Trends in Argumentation Logic. Here we mainly describe two approaches to argumentation logic with explicating monotonic and non-monotonic, or defeasible, reasoning and explain the role of artificial intelligence in applying argumentation logic. Then we give a short overview of the papers contributed to the special issue.

This work implements argumentation as the basis for modeling the relevant EU legislation concerning medical devices classification. Stakeholders can consult a web application for determining the risk-based class of a medical device based on the relevant legislation. The described approach is generally applicable to any other analogous cases of decision-making based on legislative regulations. One of the main advantages of using argumentation is the explainability and the high modularity of software permitting the extension and/or modification of the code when new relevant regulations become available.

This work implements argumentation as the basis for modeling the relevant EU legislation concerning medical devices classification. Stakeholders can consult a web application for determining the risk-based class of a medical device based on the relevant legislation. The described approach is generally applicable to any other analogous cases of decision-making based on legislative regulations. One of the main advantages of using argumentation is the explainability and the high modularity of software permitting the extension and/ or modification of the code when new relevant regulations become available.

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

AbstractWearable robots are devices intended to improve the quality of users’ life by augmenting, assisting, or substituting human functions. Exoskeletons are one of the most widespread types of wearable robots, currently used extensively in medical applications (and also for industrial, assistive, or military purposes), thus governed by regulations for medical devices and their conformity assessment. On top of that, manufacturers must also specify if their exoskeletons can be categorized as machines and, therefore, additionally apply a number of requirements mandated from machinery regulations. This work focuses on capturing both the abovementioned requirements enacted by the Medical Devices Directive 2017/745 and the Machinery Directive 2006/42 into a single framework. It formalizes into Rules the Conformity Assessment procedures regarding the marketability of exoskeletons indicated by the CE marking (“Conformité Européene”). These Rules, expressed in the Positional-Slotted Object-Applicative (PSOA) RuleML code, were complemented by representative Facts based on real-life cases of commercialized exoskeletons. Additional Exoskeletons Facts can be included by users from other forms (such as MS Excel) and translated into the PSOA RuleML code through the provided Python script. The open-source Exoskeletons’ CE mark (ExosCE) Rules KB was tested by querying in the open-source PSOATransRun system. The ExosCE Rules prototype can assist in the compliance process of stakeholders and in the registration of exoskeletons with a CE mark.

In this position paper, we argue that users’ online consents to terms of services and privacy notices is naturally impaired by the unbalanced powers between online service providers and their users. We argue that a full fledged legal document management system relying on semantic representation is key to resolving this conflict and facilitating transparency of Online Legal Documents, and we give a quick overview of the LegiCrowd project, a crowdsourced approach to legal documents annotation, which paves the way towards such solution.

Neurorobotics is an interdisciplinary scientific field which focuses on embodied neural systems and spans scientific theory, research, development and clinical medical practice. It involves a variety of science and engineering disciplines, most frequently electronic, mechanical and control engineering (sometimes alternatively described as electrical, automation, computer or automation engineering), informatics, computer science, software engineering and artificial intelligence (Al), while in the faculty of health sciences it is often associated with neuroscience, neurophysiology and neurosurgery.

There is a growing producer and consumer interest in medical devices and the commensurate need for regulatory frameworks to ensure the quality of medical devices marketed locally and globally. This work focuses on formalizing the clauses enacted by Regulation (EU) 2017/745 for risk-based classification and class-based conformity assessment regarding marketability of medical devices. The resulting knowledge base (KB) represents clauses in Positional-Slotted Object-Applicative (PSOA) RuleML by integrating F-logic-like frames with Prolog-like relationships for atoms used as facts and in the conclusions and conditions of rules. Rules can apply polyadic functions, define polyadic relations, and augment conclusions with actions and conditions with events. The PSOA RuleML-implemented Medical Devices Rules KB was tested by querying in the open-source Java-implemented PSOATransRun system, which has provided a feedback loop for refinement and extension. This prototype can contribute to the licensing process of stakeholders and the registration of medical devices with a CE conformity mark.

This paper studies Knowledge Bases (KBs) in PSOA RuleML and IDP, aligning, interoperating, and co-executing them for a use case of Air Traffic Control (ATC) regulations. We focus on the common core of facts and rules in both languages, explaining basic language features. The used knowledge sources are regulations specified in (legal) English, and an aircraft data schema. In the modeling process, inconsistencies in both sources were discovered. We present the discovery process utilizing both specification languages, and highlight their unique features. We introduce three extensions to this ATC KB core: (1) While the current PSOA RuleML does not distinguish the ontology separately from the instance level, IDP does. Hence, we specify a vocabulary-enriched version of ATC KB in IDP for knowledge validation. (2) While the current IDP uses relational modeling, PSOA additionally supports graph modeling. Hence, we specify a relationally interoperable graph version of ATC KB in PSOA. (3) The KB is extended to include optimization criteria to allow the determination of an optimal sequence of more than two aircraft.

This work focuses on formalizing the rules enacted by Regulation (EU) 2017/745, for risk-based classification and for class-based conformity assessment options regarding medical devices marketability, in Positional-Slotted Object-Applicative (PSOA) RuleML. The knowledge base represents knowledge by integrating F-logic-like frames with Prolog-like relationships for atoms used as facts and in the conditions and conclusions of rules. We tested this open-source knowledge base by querying it in the open-source PSOATransRun system which provided a feedback loop for refinement and extension. This can support the licensing process for stakeholders and the registration of medical devices with a CE conformity mark.

The formalization of Air Traffic Control Regulations for the separation of aircraft during approach and departing phases is discussed. Our aim is to introduce rules in Positional-Slotted, Object-Applicative (PSOA) RuleML syntax that capture those regulations. This rulebase is combined with aircraft facts, resulting in a complete Knowledge Base for the computation of the required separation of aircraft. We provide examples of queries posed in the open-source PSOATransRun system and we show the capabilities and limitations of the Knowledge Base and PSOATransRun.

The registration and marketability of medical devices in Europe is governed by the Regulation (EU) 2017/745 and by guidelines published in terms thereof. This work focuses on formalizing the rules for risk-based classification of medical devices as well as the conformity assessment options for each class in Positional-Slotted Object-Applicative (PSOA) RuleML. We tested this open-source knowledge base by querying it in the open-source PSOATransRun system. The aim of this formalization is to create a computational guideline to assist stakeholders.