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This chapter reviews recent advances in computational cognitive reasoning and their underlying algorithmic foundations. It summarises the neural-symbolic approach to cognition and computation. Neural-symbolic systems integrate two fundamental phenomena of intelligent behaviour: reasoning and the ability to learn from experience. The chapter illustrates how to represent, learn and compute several expressive forms of symbolic knowledge using neural networks. The goal is to provide computational models with integrated reasoning capabilities, where the neural networks offer the machinery for cognitive reasoning and learning while symbolic logic offers explanations to the neural models facilitating the necessary interaction with the world and other systems. [ABSTRACT FROM AUTHOR]

The integration of knowledge representation, reasoning and learning into a robust and computationally effective model is a key challenge in Artificial Intelligence. Temporal models are fundamental to describe the behaviour of computing and information systems. In addition, acquiring the description of the desired behaviour of a system is a complex task in several AI domains. In this paper, we evaluate a neural framework capable of adapting temporal models according to properties, and also learning through observation of examples. In this framework, a symbolically described model is translated into a recurrent neural network, and algorithms are proposed to integrate learning, both from examples and from properties. In the end, the knowledge is again symbolically represented, incorporating both initial model and learned specification, as shown by our case study. The case study illustrates how the integration of methodologies and principles from distinct AI areas can be relevant to build robust intelligent systems. [ABSTRACT FROM AUTHOR]

As software production achieves a growing importance in the embedded systems world, quality evaluation of embedded software and its impact on physical properties of embedded systems becomes increasingly relevant. Although there are tools for embedded software design that improve software specification and verification, we are still short of a tool that supports the designer's decisions on the best design strategy regarding low level, physical characteristics like performance, energy, and memory footprint, which are critical in the embedded domain. In this paper, we provide an analysis of the correlation between software quality metrics and physical metrics for embedded software. By means of experiments, we investigate the impact of software engineering best practices on embedded software and show that software quality metrics can be used to guide design decisions toward improving physical properties of embedded systems. [ABSTRACT FROM AUTHOR]