Meta-Information and Argumentation in Multi-Agent Systems

In this work we compile our research regarding meta-information in multi-agent systems. In particular, we describe some agents profiles represent- ing different attitudes which describe how agents consider meta-information in their decisions-making and reasoning processes. Furthermore, we describe how we have combined different meta-information available in multi-agent systems with an argumentation-based reasoning mechanism. In our approach, agents are able to decide more conflicts between information/arguments, given that they are able to use different meta-information (often combined) to decide between such conflicting information. Our framework for meta-information in multi- agent systems was implemented based on a modular architecture, thus other meta-information can be added, as well as different meta-information can be combined in order to create new agents profiles. Therefore, in our approach, different agents profiles can be instantiated for different application domains, allowing flexibility in the choice of how agents will deal with conflicting infor- mation in those particular domains

Using Chatbot Technologies to Support Argumentation

Chatbots are extensively used in modern times and are exhibiting increasingly intelligent behaviors. However, being relatively new technologies, there are significant demands for further advancement. Numerous possibilities for research exist to refine these technologies, including integration with other technologies, especially in the field of artificial intelligence (AI), which has received much attention and development. This study aims to explore the ability of chatbot technologies to classify arguments according to the reasoning patterns used to create them. As argumentation is a significant aspect of human intelligence, categorizing arguments according to various argumentation schemes (reasoning patterns) is a crucial step towards developing sophisticated human-computer interaction interfaces. This will enable agents (chatbots) to engage in more sophisticated interactions, such as argumentation processes

Distributed Theory of Mind in Multi-Agent Systems

Theory of Mind is a concept from developmental psychology which elucidates how humans mentalise. More specifically, it describes how humans ascribe mental attitudes to others and how they reason about these mental attitudes. In the area of Artificial Intelligence, Theory of Mind serves as a fundamental pillar in the design of intelligent artificial agents that are supposed to coexist with humans within a hybrid society. Having the ability to mentalise, these artificial agents could potentially exhibit a range of advanced capabilities that underlie meaningful communication, including empathy and the capacity to better understanding the meaning behind the utterances others make. In this paper, we propose a distributed theory of mind approach in multi-agent systems, in which agents and human users share evidence to reach more supported conclusions about each other’s mental attitudes. We demonstrate our approach in a scenario of stress detection, in which personal agents infer whether their users are stressed or not according to the distributed theory of mind approach

An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals

In the last few years, several scientific studies have shown that occupational stress has a significant impact on workers, particularly those in the healthcare sector. This stress is caused by an imbalance between work conditions, the worker’s ability to perform their tasks, and the social support they receive from colleagues and management professionals. Researchers have explored occupational stress as part of a broader study on affective systems in healthcare, investigating the use of biomarkers and machine learning approaches to identify early conditions and avoid Burnout Syndrome. In this paper, a set of machine learning (ML) algorithms was evaluated using statistical data on biomarkers from the Affective Road database to determine whether the use of explanations can help identify stress more objectively. This research integrates explainability and machine learning to aid in the identification of various levels of stress, which has not been previously evaluated for the domain of occupational stress. The Random Forest is the best-performing model for this assignment, followed by k-Nearest Neighbors and Neural Network. Later, explainers were applied to the Random Forest, highlighting feature importance, partial dependencies between characteristics, and a summary of the impact of features on outputs based on their values

Uma Aplicação para Gerenciamento de Motoristas Autônomos: Usufruindo da Escalabilidade Oferecida por Sistemas Multiagentes

Aplicações utilizando sistemas multiagentes ganharam grande interesse da comunidade de inteligência artificial nos últimos anos, devido ao grande potencial do paradigma em tratar aplicações distribuídas com ambientes e entidades dinâmicas. Neste trabalho descrevemos uma aplicação para gerenciamento de motoristas autônomos, baseada no paradigma de sistemas multiagentes. O paradigma de sistemas multiagentes mostra-se bem adequado para o desenvolvimento da aplicação proposta, possuindo característica e facilidades no desenvolvimento de técnicas de escalabilidade, as quais são o foco deste trabalho. Nas simulações realizadas, foram obtidos resultados otimistas com relação ao tempo de espera médio de um cliente por um taxi. Foram obtidos tempos abaixo de 15 minutos para no máximo um passageiro por taxi, e tempos abaixo de 30 minutos para uma taxa de até 5 passageiros por taxi

RV4JaCa—Towards Runtime Verification of Multi-Agent Systems and Robotic Applications

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This is achieved keeping in mind possible safety-critical uses of the MAS, such as robotic applications. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. In this paper, we mainly focus on MAS when used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. The latter may be a problem and undermine the development of the software agents. In this paper, we tackle this problem by proposing and demonstrating the implementation of a framework that aims to control the dialogue flow in a MAS; especially when the MAS communicates with the user through natural language to aid decision-making in a hospital bed allocation scenario