Modern Approaches to Software Engineering in the Compositional Era

The amazing evolution fuelled by the introduction of the computational element has already changed our lives and continues to do so. Initially, the fast advancement in hardware partially enabled an appreciation for software potency. This meant that engineers had to have a better command over this field that was crucial in the solution of current and future problems and requirements. However, software development has been reported as not adequate, or mature enough. Intelligence can help closing this gap. This chapter introduces the historical and modern aspects of software engineering within the artificial intelligence perspective. Also an illustrative example is included that demonstrates a rule-based approach for the development of fault management systems.

Moral Emotions for Autonomous Agents

In this chapter we raise some of the moral issues involved in the current development of robotic autonomous agents. Starting from the connection between autonomy and responsibility, we distinguish two sorts of problems: those having to do with guaranteeing that the behavior of the artificial cognitive system is going to fall within the area of the permissible, and those having to do with endowing such systems with whatever abilities are required for engaging in moral interaction. Only in the second case can we speak of full blown autonomy, or moral autonomy. We illustrate the first type of case with Arkin’s proposal of a hybrid architecture for control of military robots. As for the second kind of case, that of full-blown autonomy, we argue that a motivational component is needed, to ground the self-orientation and the pattern of appraisal required, and outline how such motivational component might give rise to interaction in terms of moral emotions. We end suggesting limits to a straightforward analogy between natural and artificial cognitive systems from this standpoint.

Balance Modelling and Implementation of Flow Balance for Application in Air Traffic Management

This work describes a decision making support system with Graph Theory and Artificial Intelligence methodologies applied to the Brazilian Air Traffic Flow Management. It consists of a flow management model based on graphs with heuristic adaptations for the dynamic regulation of the air traffic flow. The model lays the foundation of the architecture of the Flow Balancing Model (FBM) which integrates the Distributed Decision Support System applied to the Tactical Management of the Traffic Flow (SISCONFLUX), under development, and has the objective of improving the national airspace management. The FBM was proposed to give support to the system in operation at the First Air Defence and Air Traffic Control Integrated Centre (CINDACTA I), by providing additional information to the process applied by the controllers, in order to mitigate the workload and improve the results of their actions. Using flow maximization techniques adapted from Graph Theory, FBM was developed as a model of analysis which determines the separation time between departures from terminals integrating the Brasilia Flight Information Region (FIR-BS), and distributes the slack capacity along the controlled airspace, in order to prevent or reduce traffic congestion in various sectors of FIR-BS. The FBM gives support to traffic flow regulation, assisting the controllers and other units within the SISCONFLUX.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

Distributed Intelligence for Constructing Economic Models

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

Machine Learning in Personalized Anemia Treatment

This chapter presents application of reinforcement learning to drug dosing personalization in treatment of chronic conditions. Reinforcement learning is a machine learning paradigm that mimics the trialand- error skill acquisition typical for humans and animals. In treatment of chronic illnesses, finding the optimal dose amount for an individual is also a process that is usually based on trial-and-error. In this chapter, the author focuses on the challenge of personalized anemia treatment with recombinant human erythropoietin. The author demonstrates the application of a standard reinforcement learning method, called Q-learning, to guide the physician in selecting the optimal erythropoietin dose. The author further addresses the issue of random exploration in Q-learning from the drug dosing perspective and proposes a “smart” exploration method. Finally, the author performs computer simulations to compare the outcomes from reinforcement learning-based anemia treatment to those achieved by a standard dosing protocol used at a dialysis unit.

Computerised Decision Support for Women’s Health Informatics

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Diagnostic Support Systems and Computational Intelligence

Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that promise to increase the efficiency of physicians during their clinical practice. This chapter provides a brief overview of the use of computational intelligence methods in the design and development of DSSs aimed at the differential diagnosis of hepatic lesions from Computed Tomography (CT) images. Furthermore, examples of DSSs developed by our research team for supporting the diagnosis of focal liver lesions from non-enhanced CT images are presented.

Hybrid Emotionally Aware Mediated Multiagency

Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial intelligence aims at constructing agents that can emulate thinking processes, but artificial life still lacks emotions and all the consequences that come from them. This work introduces an emotionally aware framework geared towards multi-agent societies. Basing our model on the shoulders of solid foundations created by pioneers who first explored the coupling of emotions and agency, we extend their ideas to include inter-agent interaction and virtual genetics as key components of an agent’s emotive state. We also introduce possible future applications of this framework in consumer products as well as research endeavors.

Artificial Intelligence in Software Engineering

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.