Intentional dialogues in multi-agent systems based on ontologies and argumentation

Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence

MAIDS - a Framework for the Development of Multi-Agent Intentional Dialogue Systems

This paper introduces a framework for programming highly sophisticated multi-agent dialogue systems. The framework is based on a multi-part agent belief base consisting of three components: (i) the main component is an extension of an agent-oriented programming belief base for representing defeasible knowledge and, in partic- ular, argumentation schemes; (ii) an ontology component where existing OWL ontologies can be instantiated; and (iii) a theory of mind component where agents keep track of mental attitudes they ascribe to other agents. The paper formalises a structured argumentation-based dialogue game where agents can “digress” from the main dialogue into subdialogues to discuss ontological or theory of mind issues. We provide an example of a dialogue with an ontological digression involving humans and agents, including a chatbot that we developed to support bed allocation in a hospital; we also comment on the initial evaluation of that chatbot carried out by domain experts. That example is also used to show that our framework supports all features of recent desiderata for future dialogue systems.This research was partially funded by CNPq, CAPES, FCT CEECIND /01997/2017 and UIDB/00057/2020

Expansão da Fronteira Agropecuária e Desmatamento na Região de Alta Floresta/MT: alternativas para o desenvolvimento sustentável.

A fronteira, mais que um limite fixo traçado em um mapa, é um espaço que guarda especificidades e revela-se excepcionalmente dinâmico e contraditório. Historicamente, o avanço da fronteira agropecuária sobre as áreas florestais da Amazônia Legal resulta em destruição da biodiversidade e conflitos agrários. Como consequência direta deste processo está o desmatamento, cuja principal força encontra-se na pecuária. Por isso, é necessário encontrar saídas capazes de colocar um freio na destruição, por meio de iniciativas e atividades econômicas que se reproduzam em uma outra lógica, que não esteja exclusivamente centrada na venda da madeira, na expansão da monocultura e no estímulo à pecuária. Assim, este artigo tem como objetivo apresentar algumas proposições que aliam desenvolvimento econômico e conservação ambiental para a região do município de Alta Floresta, ao norte do estado de Mato Grosso, localizado em área de fronteira agropecuária

INTELIGÊNCIA ARTIFICIAL NO APOIO À TOMADA DE DECISÕES NO DIREITO TRIBUTÁRIO

No Brasil, a área de direito tributário tem enfrentado sérios problemas devido ao atual modelo de aplicação da lei no país. Os casos de aplicação da lei tributária, em particular, têm uma grande parcela de responsabilidade pelas altas taxas de congestionamento judicial. O grande volume de ações judiciais vencidas dificulta a eficácia da justiça. Um dos pontos identificados como gargalo para a conclusão de tais processos legais, mais especificamente nos processos fiscais relacionados ao imposto municipal, é a identificação dos Avisos de Recebimento (AR) que comprovam que os devedores receberam informações sobre as dívidas que devem. Somente quando os tribunais estão na posse de um AR devidamente assinado, o processo pode avançar; quando uma entrega de aviso falhou, é necessário tentar novamente a entrega do aviso de dívida e, de fato, pode ser necessário encontrar meios alternativos de entrar em contato com o devedor. Para ajudar a acelerar a resolução de tais processos, estão sendo desenvolvidos aplicativos baseados em técnicas de inteligência artificial. Tendo em mente que a área do direito geralmente tem alguma resistência à adoção de sistemas automatizados, está sendo trabalhado em uma abordagem explicável que pode mostrar ao usuário qual raciocínio foi usado para chegar à conclusão sugerida, o que permite que os especialistas jurídicos tomem uma decisão final. Uma ontologia com conceitos relacionados ao direito tributário no Brasil está sendo desenvolvida. Essa ontologia será utilizada por um agente inteligente capaz de raciocinar sobre seus conceitos, extrair informações de documentos usando o Processamento de Linguagem Natural (PLN) e identificar padrões de raciocínio em processos utilizando a mineração de argumentos.CEECIND/01997/2017, UIDB/00057/20

Explaining Semantic Reasoning Using Argumentation

Multi-Agent Systems (MAS) are popular because they provide a paradigm that naturally meets the current demand to design and implement distributed intelligent systems. When developing a multi- agent application, it is common to use ontologies to provide the domain- specific knowledge and vocabulary necessary for agents to achieve the system goals. In this paper, we propose an approach in which agents can query semantic reasoners and use the received inferences to build expla- nations for such reasoning. Also, thanks to an internal representation of inference rules used to build explanations, in the form of argumenta- tion schemes, agents are able to reason and make decisions based on the answers from the semantic reasoner. Furthermore, agents can communi- cate the built explanation to other agents and humans, using computational or natural language representations of arguments. Our approach paves the way towards multi-agent systems able to provide explanations from the reasoning carried out by semantic reasoners

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