Large Language Models (LLMs) have become a significant milestone in the history of artificial intelligence, representing a powerful technology that drives advancements in natural language understanding and generation. In this paper, we propose an approach in which LLMs are utilized to support the task of translating natural language arguments into computational representations. Our approach is grounded in using argumentation schemes to classify arguments, providing context to LLMs for performing the proposed task. Our results demonstrate that LLMs, even with a short context, can handle simple argument structures. Moreover, our findings suggest that a larger context would likely enhance the performance, particularly when dealing with more complex argument structures.

Computational medicine research requires clinical data for training and testing purposes, so the development of datasets composed of real hospital data is of utmost importance in this field. Most such data collections are in the English language, were collected in anglophone countries, and do not reflect other clinical realities, which increases the importance of national datasets for projects that hope to positively impact public health. This paper presents a new Brazilian Clinical Dataset containing over 70,000 admissions from 10 hospitals in two Brazilian states, composed of a sum total of over 2.5 million free-text clinical notes alongside data pertaining to patient information, prescription information, and exam results. This data was collected, organized, deidentified, and is being distributed via credentialed access for the use of the research community. In the course of presenting the new dataset, this paper will explore the new dataset’s structure, population, and potential benefits of using this dataset in clinical AI tasks.

Ambient intelligence (AmI) emerged in Europe with the idea that the computational support available in a living environment can help people’s lives. AmI introduces new challenges and critical issues because this technology differs from the traditional form of interaction that is centered on a device or system. In this work, we aim to contribute to this topic by conducting a systematic literature review in order to identify Human-Computer Interaction guidelines for the design of ambient intelligence systems. We found a total of 120 guidelines from 27 papers, and we grouped similar guidelines creating different categories. These categories of guidelines later became a unified guideline incorporating also some of our own ideas. As a result, this paper identifies ten categories and guidelines to improve user interaction with ambient intelligence systems. We believe that these guidelines significantly contribute to designing more intuitive AmI systems for users, including those with disabilities.

This work presents GoDonnie a programming language to command a robot to improve orientation and mobility(O&M) skills in people who are visually impaired (PVI). The GoDonnie programming language is based on the Logo language. GoDonnie runs in a programming environment called Donnie. This environment has a 2D graphic simulator with a virtual robot, in which one can visualise and receive sound feedback from the execution of the language commands for moving the virtual robot in the environment. GoDonnie has been evaluated with PVI to verify its usability and support to O&M. The results indicate that GoDonnie has good usability, supports the development of O&M in PVI and meets the expectations regarding the programming environment. A video of GoDonnie execution is available in https://youtu.be/HE__sAgfNBo

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Using advanced technologies is imperative to support quick decision-making in a hospital, where people work with complex and critical processes.An example of a complex and very important task is to make the best decision about in which room and bed a patient should be admitted to hospital, considering the patient needs, characteristics, and available resources.In this case, bad decisions can even compromise patient health.With this study, we aim to facilitate patient-related decisions related to bed allocation, based on an ontology.To do so, we developed an ontology that takes patients' information into account to help health professionals decide where to allocate them.Our main contribution is an ontology, with classes, relationships, individuals, and rules to be used in specific scenarios.Additionally, we exemplify some scenarios in which the rules could be applied.

Abstract Task allocation is an important aspect of multiagent coordination. However, there are many challenges in developing appropriate strategies for multiagent teams so that they operate efficiently. Real‐world scenarios such as flooding disasters usually require the use of heterogeneous robots and the execution of tasks with different structures and complexities. In this paper, we propose a decentralized task allocation mechanism considering different types of tasks for heterogeneous agent teams where agents play different roles and carry out tasks according to their own capabilities. We have run several experiments to evaluate the proposed mechanism. The results show that the proposed mechanism appears to scale well and provides near‐optimal allocations.

One of the main challenges in integrating different smart applications is security. Among the problems related to security, data access control is one of the most important, given that it involves end-users' privacy. In this paper, we propose an approach to the modelling of data access control interfaces using argumentation-based agents. In particular, we introduce argumentation schemes for data access control, which are based on some of the most relevant models for data access control currently available. Our approach considers not only the usual access control policies, describing which category of agents has access to which category of information, but also emergency policies, describing situations where special emergency access control rules apply. Using argumentation-based agents as access control interfaces allows us to deal with the uncertainty about external information, allowing a correct categorisation of agents that request access to information, as well as allowing agents to expose emergency situations in which emergency access control rules may apply.

Recently, argumentation frameworks have been extended in order to consider trust when defining preferences between arguments, given that arguments (or information that supports the arguments) from more trustworthy sources may be preferred to arguments from less trustworthy sources. Although such literature presents interesting results on argumentation-based reasoning and how agents define preferences between arguments, there is little work taking into account agent strategies for argumentation-based dialogues using such information. In this work, we propose an argumentation framework in which agents consider how much the recipient of an argument trusts others in order to choose the most suitable argument for that particular recipient, i.e., arguments constructed using information from those sources that the recipient trusts. Our approach aims to allow agents to construct more effective arguments, depending on the recipients and on their views on the trustworthiness of potential sources.

We have put forward an approach to online collaborative discussion of software development problems based on Argumentation theory.Having records of past discussions can significantly help solve problems in new projects, and CBR techniques are used to retrieve the most similar cases.However, long discussions on past projects still contain too much information to provide support in new discussions.To address this problem, in this paper we introduce the idea of explanation templates that are able to summarize past experiences, particularly for risk management discussions.We formalize this notion of template, introduce the main templates we have developed to support explanation of past experience with risk management, and report the results of a case study on a realworld software project to assess the usefulness of those templates.

The engagement of project stakeholders in collaborative debates of risk management has an important contribution to software projects.To promote the identification, (re)use and critical analysis of stakeholders' arguments in these debates, this paper lays out a knowledge engineering process for the development of "argumentation schemes" for risk management.This process covers activities of identification, interpretation and causal-and-effect analysis of typical risk statements.From such risk management information and reusing generalized argumentation templates from argumentation catalogues discussed in the field of Artificial Intelligence, the process leads to the specification, generalization, validation and indexing of the developed schemes.As implemented in our project, a web-based system to support the execution of these development activities allows the recording of these schemes in a semi-structured representation format.An argumentation scheme for risks of non-stable requirements is presented so as to show the reusable argumentation artifacts that can be produced when our development process is followed.

Managing risks in real-world software projects is of paramount importance.A significant class of such risks is related to the engineering of requirements, commonly involving the presentation and analysis of risk management arguments from both software engineers and clients involved in collaborative debates.In this work, drawing inspiration from argumentation theory in Artificial Intelligence, we introduce a number of "argumentation schemes" and associated "critical questions" to support such discussions.In doing so, we propose schemes related to risks due to excessive numbers of requirements; inadequate client representatives and poor understanding of client needs; incorrect, incomplete and conflicting requirements, and complex and non-traceable requirements.We also present a case study where the developed schemes were used to support the discussion of requirement risks in the context of a research and prototyping software project for the Brazilian Army.

Managing risks in real-world software projects is of paramount importance. A significant class of such risks is related to the engineering of requirements, commonly involving the presentation and analysis of risk management arguments from both software engineers and clients involved in collaborative debates. In this work, drawing inspiration from argumentation theory in Artificial Intelligence, we introduce a number of “argumentation schemes” and associated “critical questions” to support such discussions. In doing so, we propose schemes related to risks due to excessive numbers of requirements; inadequate client representatives and poor understanding of client needs; incorrect, incomplete and conflicting requirements; complex and non-traceable requirements; non-stable requirements; and low quality requirements. We also discuss a case study and two experiments where the developed schemes supported the discussion of requirement risks in software projects. The overall results of these experiments indicate that our schemes are useful in the identification, proposition and analysis of requirement risks, adequately supporting debates on requirement risks.

Several advantages can be obtained by allowing multi-agent systems to easily access ontologies, for example, in scenarios where agents make their decisions based on knowledge provided by ontologies. Thus, this paper presents an infrastructure to allow the use of web ontologies in different agent-oriented platforms. The agents use this infrastructure layer as a tool for storing, accessing and querying domain-specific OWL ontologies. As a result, this layer allows an integration of agent platforms with semantic web data and ontologies. We exemplify in practice how agents, coded in one such platform, can use the proposed access layer to ontological reasoning engines, as well as which features can be obtained from it. We evaluated and compared performance and memory consumption of this semantic infrastructure against usual knowledge representation in agent programming.

In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried

Norms have been considered an attractive means for the regulation of the dynamics of multi-systems (MAS). Typically, it is left for the individual agents to apply (general) norms to the particular concrete situations that arise during the operation of the system. This article summarizes our work on situated normative infrastructures for multi-agent systems, which aim to facilitate the operationalization of general norms for their use by autonomous agents in particular concrete situations. The article: (i) clarifies the concepts of normative object, normative space and situated normative infrastructure; (ii) explains the implications of such concepts for the normative reasoning of autonomous agents; and (iii) illustrates the ways in which a situated normative infrastructure may define alternative normative systems for a given normative multi-agent system.

Jason is a platform for agent-based software development that is characterised both by being based on a programming language with formal semantics as well as having many language and platforms features that are very useful for practical programming, but not fully formalised. In this paper, we make significant progress in the direction of formalising the aspects of the variant of AgentSpeak that is interpreted by Jason that were not included in previous work on giving formal semantics to AgentSpeak. In particular, we give semantics to the plan failure handling mechanism which is unique to Jason, and also for some of the predefined internal actions that can alter an agent's mental state. Such internal actions are essential for some aspects of BDI-based programming, such as checking or dropping current goals or intentions, and therefore need to be formally defined within the operational semantics of the language.

Agents programmed in BDI-inspired languages have goals to achieve and a library of plans that can be used to achieve them, typically requiring further goals to be adopted. This is most naturally represented by a structure that has been called a Goal-Plan Tree. One of the uses of such structure is in agent deliberation (in particular, deciding whether to commit to achieving a certain goal or not). In previous work, a Petri net based approach for reasoning about goal-plan trees was defined. This paper presents a constraint-based approach to perform the same reasoning, which is then compared with the Petri net approach.

We here address the modelling and analysis of human-agent teamwork, specifically in the context of proposed astronaut-robot collaboration in future space missions. We are particularly interested in modelling such systems at a level that allows formal verification techniques to be applied, and hence carry out sophisticated analysis of the reliability and effectiveness of the teams before the system is deployed in real scenarios. In this paper we describe our ongoing research in this area.

Programming languages designed specifically for multi-agent systems represent a new programming paradigm that has gained popularity over recent years, with some multi-agent programming languages being used in increasingly sophisticated applications, often in critical areas. To support this, we have developed a set of tools to allow the use of model-checking techniques in the verification of systems directly implemented in one particular language called AgentSpeak. The success of model checking as a verification technique for large software systems is dependent partly on its use in combination with various state-space reduction techniques, an important example of which is property-based slicing. This article introduces an algorithm for property-based slicing of AgentSpeak multi-agent systems. The algorithm uses literal dependence graphs, as developed for slicing logic programs, and generates a program slice whose state space is stuttering-equivalent to that of the original program; the slicing criterion is a property in a logic with LTL operators and (shallow) BDI modalities. In addition to showing correctness and characterizing the complexity of the slicing algorithm, we apply it to an AgentSpeak program based on autonomous planetary exploration rovers, and we discuss how slicing reduces the model-checking state space. The experiment results show a significant reduction in the state space required for model checking that agent, thus indicating that this approach can have an important impact on the future practicality of agent verification.

In this paper, we show that the flexible model-checking of multi-agent systems, implemented using agent-oriented programming languages, is viable thus paving the way for the construction of verifiably correct applications of autonomous agents and multi-agent systems. Model checking experiments were carried out on AJPF (agent JPF), our extension of Java PathFinder that incorporates the agent infrastructure layer, our unifying framework for agent programming languages. In our approach, properties are specified in a temporal language extended with (shallow) agent-related modalities. The framework then allows the verification of programs written in a variety of agent programming languages, thus removing the need for individual languages to implement their own verification framework. It even allows the verification of multi-agent systems comprised of agents developed in a variety of different (agent) programming languages. As an example, we also provide model checking results for the verification of a multi-agent system implementing a well-known task sharing protocol.

The concept of anagentis increasingly used in contemporary software applications, particularly those involving the Internet, autonomous systems, or cooperation. However, with dependability and safety in mind, it is vital that the mechanisms for representing and implementing agents are clear and consistent. Hence there has been a strong research effort directed at using formal logic as the basis for agent descriptions and agent implementation. Such a logical basis not only presents the clarity and consistency required but also allows for important techniques such as logical verification to be applied. We present a road map of research into the use of computational logic in agent‐based systems and survey much of the recent work in these areas. Even though, with such a rapidly changing field, it is impossible to cover every development, we aim to give the reader sufficient background to understand the current research problems and potential future developments in this maturing area.

Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives. With respect to communication, implemented agent programming languages have tended to be rather ad hoc. This paper addresses both of these problems, by giving semantics to speech-act based messages received by an AgentSpeak agent. AgentSpeak is a logic-based agent programming language which incorporates the main features of the PRS model of reactive planning systems. The paper builds upon a structural operational semantics to AgentSpeak that we developed in previous work. The main contributions of this paper are as follows: an extension of our earlier work on the theoretical foundations of AgentSpeak interpreters; a computationally grounded semantics for (the core) performatives used in speech-act based agent communication languages; and a well-defined extension of AgentSpeak that supports agent communication.