A representação do conhecimento de maneira mais fiel possível à realidade é uma meta histórica e não resolvida até o momento na área da Inteligência Artificial. Problemas são resolvidos e decisões são tomadas levando-se em conta diversos tipos de conhecimentos, os quais muitos são tendenciosos, inexatos, ambíguos ou ainda incompletos. A fim de tentar emular a capacidade de representação do conhecimento humano, levando-se em conta as diversas dificuldades inerentes, tem-se construído sistemas computacionais que armazenam o conhecimento das mais diversas formas. Dentro deste contexto, este trabalho propõe um experimento que utiliza duas formas distintas de representação do conhecimento: a simbólica, neste caso BDI, e a probabilística, neste caso Redes Bayesianas. Para desenvolvermos uma prova de conceito desta proposta de representação do conhecimento estamos utilizando exemplos que serão construídos através da tecnologia de programação voltada para agentes. Para tal, foi desenvolvida uma implementação de um Sistema MultiAgente, estendendo o framework Jason através da implementação de um plugin chamado COPA. Para a representação do conhecimento probabilístico, utilizamos uma ferramenta de construção de Redes Bayesianas, também adaptada a este sistema. Os estudos de caso mostraram melhorias no gerenciamento do conhecimento incerto em relação às abordagens de construções de agentes BDI clássicos, ou seja, que não utilizam conhecimento probabilístico. / Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Problems are solved and decisions are made taking into consideration different kinds of knowledge, from which many are biased, inaccurate, ambiguous or still incomplete. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this propose of knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent System was developed, extending the Jason framework through the implementation of a plugin called COPA. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.

A representação do conhecimento de maneira mais fiel possível à realidade é uma meta histórica e não resolvida até o momento na área da Inteligência Artificial. Problemas são resolvidos e decisões são tomadas levando-se em conta diversos tipos de conhecimentos, os quais muitos são tendenciosos, inexatos, ambíguos ou ainda incompletos. A fim de tentar emular a capacidade de representação do conhecimento humano, levando-se em conta as diversas dificuldades inerentes, tem-se construído sistemas computacionais que armazenam o conhecimento das mais diversas formas. Dentro deste contexto, este trabalho propõe um experimento que utiliza duas formas distintas de representação do conhecimento: a simbólica, neste caso BDI, e a probabilística, neste caso Redes Bayesianas. Para desenvolvermos uma prova de conceito desta proposta de representação do conhecimento estamos utilizando exemplos que serão construídos através da tecnologia de programação voltada para agentes. Para tal, foi desenvolvida uma implementação de um Sistema MultiAgente, estendendo o framework Jason através da implementação de um plugin chamado COPA. Para a representação do conhecimento probabilístico, utilizamos uma ferramenta de construção de Redes Bayesianas, também adaptada a este sistema. Os estudos de caso mostraram melhorias no gerenciamento do conhecimento incerto em relação às abordagens de construções de agentes BDI clássicos, ou seja, que não utilizam conhecimento probabilístico. / Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Problems are solved and decisions are made taking into consideration different kinds of knowledge, from which many are biased, inaccurate, ambiguous or still incomplete. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this propose of knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent System was developed, extending the Jason framework through the implementation of a plugin called COPA. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.

A representação do conhecimento de maneira mais fiel possível à realidade é uma meta histórica e não resolvida até o momento na área da Inteligência Artificial. Problemas são resolvidos e decisões são tomadas levando-se em conta diversos tipos de conhecimentos, os quais muitos são tendenciosos, inexatos, ambíguos ou ainda incompletos. A fim de tentar emular a capacidade de representação do conhecimento humano, levando-se em conta as diversas dificuldades inerentes, tem-se construído sistemas computacionais que armazenam o conhecimento das mais diversas formas. Dentro deste contexto, este trabalho propõe um experimento que utiliza duas formas distintas de representação do conhecimento: a simbólica, neste caso BDI, e a probabilística, neste caso Redes Bayesianas. Para desenvolvermos uma prova de conceito desta proposta de representação do conhecimento estamos utilizando exemplos que serão construídos através da tecnologia de programação voltada para agentes. Para tal, foi desenvolvida uma implementação de um Sistema MultiAgente, estendendo o framework Jason através da implementação de um plugin chamado COPA. Para a representação do conhecimento probabilístico, utilizamos uma ferramenta de construção de Redes Bayesianas, também adaptada a este sistema. Os estudos de caso mostraram melhorias no gerenciamento do conhecimento incerto em relação às abordagens de construções de agentes BDI clássicos, ou seja, que não utilizam conhecimento probabilístico. / Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Problems are solved and decisions are made taking into consideration different kinds of knowledge, from which many are biased, inaccurate, ambiguous or still incomplete. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this propose of knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent System was developed, extending the Jason framework through the implementation of a plugin called COPA. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.