Målet med den här datorbaserade filosofiska utredningen inom kognitionsvetenskap är att utforska intentionalitet i kollektiva beteenden hos artificiella svärmar. Två definitioner av intentionalitet utforskades; som representationer hos agenter och som observerbara attribut hos agenter, även kallat intentional stance. För den representativa definitionen användes en modell av kollektiv intentionalitet som integrerar två olika ståndpunkter, singularståndpunkten och pluralståndpunkten av kollektiv intentionalitet. Modellen har fem villkor för intentionalitet enligt SharedPlans. Genom att använda Belief-Desire-Intention-modellen för intelligenta agenter operationaliserades villkoren till möjliga representationer. En implementation av en målinriktad artificiell svärm i NetLogo analyserades genom att studera hur väl den uppfyllde de operationaliserade villkoren. Fyra av fem villkor var uppfyllda. Flera simuleringar med olika hastighet genomfördes även under observation. Dessa visade att processen kunde delas upp i tre faser med olika egenskaper. Den utforskande fasen hade gemensam intentionalitet centrerad till ett fåtal aktiva individer. Beslutsfasen hade individuella intentioner som kunde stå i konflikt med varandra medan gemensamma intentioner strävade mot samma mål. I flyttfasen var de individuella intentionerna att förhålla sig till varandra, vilket fick gruppen att upplevas som en enhet med intentionen att flytta gruppen. Resultaten visade att intentionalitet kan observeras och analyseras hos den här artificiella svärmen. Däremot har svärmen inte kollektiv intentionalitet utifrån båda ståndpunkterna.

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing.

Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-05-03T18:38:37Z No. of bitstreams: 1 WILLY CARVALHO TIENGO – TESE (PPGCC) 2018.pdf: 4153575 bytes, checksum: 929b905dca8b61fcb0f831264752540f (MD5) / Made available in DSpace on 2018-05-03T18:38:37Z (GMT). No. of bitstreams: 1 WILLY CARVALHO TIENGO – TESE (PPGCC) 2018.pdf: 4153575 bytes, checksum: 929b905dca8b61fcb0f831264752540f (MD5) Previous issue date: 2018 / No Brasil, o transporte rodoviário é responsável por 58% do transporte de carga, que tem os acidentes como um grande problema, pois, em geral, esses ocasionam muitas vítimas, prejuízos econômicos relevantes e em alguns casos danos ambientais decorrentes de derramamento de carga. Estudos apontam que os prejuízos com os acidentes no transporte de carga em 2012 foram de mais de 9 bilhões de reais. Estudo realizado em 2007 pela PAMCARY, corretora de seguros e gestora de riscos, revelou que os eventos que combinam maior frequência e gravidade são tombamento e capotagem. Nesse sentido, esta pesquisa consiste na elaboração de um assistente avançado para motorista que objetiva alertar previamente sobre a velocidade limite da curva, a fim de diminuir os riscos de tombamento. Em outras palavras, consiste em buscar mitigar o problema auxiliando o motorista para que ele mantenha o veículo em uma velocidade segura, por meio de alertas e em prazo adequado, que permitam ao motorista tomar medidas corretivas em caso de estado inseguro. A solução foi desenvolvida a partir de uma arquitetura modular, que funciona da seguinte forma: por meio de sensores (velocidade, GPS e posição do acelerador), associado a mapas digitais, o risco de acidente é controlado constantemente. Com isso, um dispositivo poderia ser embarcado na cabine do veículo para emitir alertas visual e auditivo de risco de tombamento. A solução utiliza o indicador de estabilidade chamado Limiar Estático de Tombamento que, associado à informação a priori de mapas digitais, permite o cálculo do risco de tombamento com diferentes abordagens. No contexto da pesquisa, foram desenvolvidas 04 versões de assistentes. Além disso, foi proposto um arcabouço de simulação microscópica de trânsito baseado no modelo de raciocínio prático denominado de belief-desire-intention (BDI) para permitir o desenvolvimento e a validação de agentes inteligentes para Sistemas Avançados de Assistência ao Motorista de maneira rápida, flexível e fácil. Para avaliar o potencial dos assistentes, foi escolhida a BR-101, estrada federal de Alagoas com mais ocorrências de tombamento. Nessa rodovia, foram simulados 400 veículos para avaliar o desempenho dos assistentes propostos. Em particular, foram investigadas a efetividade, intrusividade, omissão e a segurança para avaliar o desempenho dos assistentes. / In Brazil, highway transportation is responsible for 58% of cargo transport. A relevant problem associated to cargo transport are the accidents, that generally cause an elevated number of victims, relevant economic losses and, in some cases, damages to the environment due to cargo spills, since there are also dangerous products being transported. Researches point out that the cost of accidents in cargo transportation in 2012 was more than BRL 9 billion. A study performed in 2007 by PAMCARY revealed the accidents profile: the events that combine higher frequency and gravity are rollover and tipping (considered here as the same nature). In this study, incompatible speed and fatigue, factors that are related to human actions, were pointed out as main causes of accidents; for another hand, sharp curve and poorly maintained roads are contributing factors to accidents. Therefore, the research proposal consists of the adoption of an assistant for warning in advance of over speed for a specific curve. This may reduce rollover risks. In other words, it would be mitigated the problem by helping the driver to maintain the vehicle in a safe speed, through customized alerts just in time to allow the driver to take corrective maneuvers in case of unsafe state. The solution is a modular architecture, which works as follows: through sensors (speed, GPS and throttle position) associated with digital maps, it is controlled the risk of accident constantly. With that, an embedded device at the vehicle’s cab could to emit visual and sound alerts warning the risk of rollover. In this work, it is proposed the adoption of the stability indicator known as Static Rollover Threshold, which is combined with a priori information from digital maps to allow the calculation of the rollover risk by different approaches. In the context of this research, 04 versions of assistants were developed. In addition, a microscopic traffic simulation framework was proposed based on the practical reasoning model named belief-desire-intention (BDI) to support the development and validation of intelligent agents for Advanced Driver Assistance Systems in a fast, flexible and easy way. To evaluate the assistants’ potential, the BR-101, Federal Highway of Alagoas with more occurrence of rollover, was chosen. On this highway, 400 vehicles were simulated to evaluate the performance of the proposed assistants. The effectiveness, intrusiveness, omission and safety of the assistants were investigated.