A Hybrid Approach to Cognition in Radars

In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results

Spatial representation for planning and executing robot behaviors in complex environments

Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task

Evologic : sistema tutor inteligente para ensino de lógica baseado em computação evolutiva

O uso de sistemas computacionais na educação vem demonstrando ser de grande valia para auxiliar no processo de ensino-aprendizagem. Uma das ferramentas disponíveis são os Sistemas Tutores Inteligentes, que tem o objetivo de dar suporte ao aluno em um determinado conteúdo. Em meados de 1980, surge o Cognitive Tutor®, para testar a arquitetura cognitiva ACT-R, se diferenciando dos Sistemas Tutores Inteligentes por possuir um viés cognitivo, destinado a acompanhar o processo cognitivo atrelado a resolução de problemas. Desde seu surgimento, o Cognitive Tutor® passou por inúmeras atualizações além de novos tutores cognitivos terem surgido. Contudo, todos recaem sobre o problema de disponibilizar um conjunto limitado de exercícios nativos, demandaram um grande esforço do professor para modelar novas atividades para os alunos ou apresentam um processo cognitivo limitado a certas formas de resolver o problema. Nesse sentido, o objetivo desse estudo é propor um Sistema Tutor Inteligente capaz de acompanhar o processo cognitivo do aluno no domínio de Dedução Natural em Lógica Proposicional, permitindo que seja possível identificar a linha de raciocínio dos alunos e que seja possível fornecer feedback de qualidade possibilitando que o aluno tenha flexibilidade na escolha dos exercícios que deseja resolver. Os agentes são modelados no contexto de um Sistema Tutor Inteligente denominado EvoLogic, utilizando uma arquitetura multiagente, suportada por cinco estruturas cognitivas estudadas ao longo dos anos. O sistema é composto por três agentes, destacando-se o agente Pedagógico, que tem a função de identificar as características do processo cognitivo do aluno (Modelo de Aluno), e o agente Especialista baseado em Algoritmos Genéticos. O objetivo deste estudo é a criação do EvoLogic, um Sistema Tutor Inteligente que possa acompanhar o processo cognitivo do aluno, flexibilizando a escolha dos exercícios e possuindo um model tracing adequado ao raciocínio do aluno. Sendo assim, optou-se pela utilização de um algoritmo genético como especialista do domínio de ensino dada a sua grande adaptabilidade a diversos contextos e problemas, facilitando uma possível portabilidade futura para outros domínios de ensino. Para validar o sistema proposto, foi criado um ambiente simulado com dados de um experimento realizado em outro sistema, especialista em Lógica. Os resultados apontam que o agente Especialista foi capaz de obter as diferentes soluções para os problemas estudados e que o agente Pedagógico pode identificar características do processo cognitivo dos alunos e acompanhar seu raciocínio ao resolverem os exercícios.The use of computer systems in education has proven to be valuable to assist in the teachinglearning process. One of the available tools are the Intelligent Tutoring Systems, which aims to support the student in a certain content. In the mid-1960s, the Cognitive Tutor® appeared, to test the cognitive architecture ACT-R, differentiating itself from Intelligent Tutoring Systems in that it has a cognitive bias, designed to follow the cognitive process related to problem solving. Since its inception, Cognitive Tutor® has undergone numerous updates in addition to new cognitive tutors having emerged. However, they all fall on the problem of making a limited set of native exercises available, demanded a great effort from the teacher to model new activities for the students or present a cognitive process limited to certain ways of solving the problem. In this sense, the objective of this study is to propose an Intelligent Tutor System capable of monitoring the student's cognitive process in the domain of Natural Deduction in Propositional Logic, allowing it to be possible to identify the students' line of reasoning and to provide quality feedback enabling that the student has flexibility in choosing the exercises he wants to solve. The agents are modeled in the context of an Intelligent Tutoring System called EvoLogic, using a multi-agent architecture, supported by five cognitive structures studied over the years. The system is composed of three agents, especially the Pedagogical agent, which has the function of identifying the characteristics of the student's cognitive process (Student Model), and the Specialist agent based on Genetic Algorithms. The objective of this study is the creation of EvoLogic, an Intelligent Tutoring System that can follows the student's cognitive process, making the choice of exercises more flexible and having a model tracing consistent to the student's reasoning. Therefore, we opted for the use of a genetic algorithm as a specialist in the teaching domain, given its great adaptability to different contexts and problems, facilitating possible future portability to other teaching domains. To validate the proposed system, a simulated environment was created with data from an experiment carried out in another system, specialized in Logic. The results show that the Specialist agent was able to obtain the different solutions to the studied problems and that the Pedagogical agent can identify characteristics of the students' cognitive process and monitor their reasoning when solving the exercises

A case study of knowledge integration across multiple memories in Soar

Abstract Online perception, behavior, and learning in complex domains require an intelligent agent to quickly and reliably access different types of knowledge. A cognitive architecture, therefore, must implement a diverse set of memories that are optimized for storing, accessing, and learning these different types of knowledge. In this paper, we describe a complex Soar agent that uses and learns multiple types of knowledge while interacting with a human in a real-world domain. Our hypothesis is that a diverse set of memories is required for the different types of knowledge. We first present the agent's processing, highlighting the types of knowledge used for each phase. We then present Soar's memories and identify which memory is used for each type of knowledge. We also analyze which properties of each memory make it appropriate for the knowledge it encodes. These include procedural, semantic, and episodic knowledge, all of which play critical roles in the agent's ability to learn and extend its capabilities. We conclude with a summary of our analysis, and conclude that a diversity of memory systems and knowledge are useful for supporting general, integrated intelligence.