Software Development Crisis

Software development is critically dependent on a number of factors. These factors include techno-logical and anthropic-oriented ones. Software production is a multiple party process; it includes customer and developer parties. Due to different expectations and goals of each side, the human factors become mission-critical. Misconceptions in the expectations of each side may lead to misbalanced production; the product that the developers produce may significantly differ from what the customers expect. This misbalanced vision of the software product may result in a software de-livery crisis. To manage this crisis, the authors recommend using software engineering methods. Software engineering is a discipline which emerged from the so-called “software crisis” in the 1960s: it combines technical and anthropic-oriented “soft” skills. To conquer the crisis, this chapter discusses general architecture patterns for software and hardware systems; it provides instances of particular industries, such as oil and gas and nuclear power production.

Research on Human Cognition for Biologically Inspired Developments

Robotic developments are seen as a next level in technology with intelligent machines, which automate tedious tasks and serve our needs without complaints. But nevertheless, they have to be fair and smart enough to be intuitively of use and safe to handle. But how to implement this kind of intelligence, does it need feelings and emotions, should robots perceive the world as we do as a human role model, how far should the implementation of synthetic consciousness lead and actually, what is needed for consciousness in that context? Additionally in Human-Robot-Interaction research, science mainly makes use of the tool phenomenography, which is exclusively subjective, so how to make it qualify for Artificial Intelligence? These are the heading aspects of this chapter for conducting research in the field of social robotics and suggesting a conscious and cognitive model for smart and intuitive interacting robots, guided by biomimetics.

Mind and Matter

A purely reductionist approach to neuroscience has difficulty in providing intuitive explanations and cost effective methods. Following a different approach, much of the mechanics of the brain can be explained purely by closer study of the relation of the brain to its environment. Starting from the laws of physics, genetics and easily observable properties of the biophysical environment we can deduce the need for dreams and a dopaminergic system. We provide a rough sketch of the various a priori assumptions encoded in the mechanics of the nervous system. This indicates much more can be learnt by studying the statistical priors exploited by the brain rather than its specific mechanics of calculation.

Naturalizing Consciousness Emergence for AI Implementation Purposes

The purpose of this chapter is to delineate a naturalistic approach to consciousness. This bioinspired method does not try to emulate into a 1:1 scale real mechanisms but instead of it, we capture some basic brain mechanisms prone to be implemented into computational frameworks. Consequently, we adopt a functional view on consciousness, considering consciousness as one among other cognitive mechanisms useful for survival purposes in natural environments. Specifically, we wish to capture those mechanisms related to decision-making processes employed by brains in order to produce adaptive answer to the environment, because this is the main reason for the emergence and purpose of consciousness.

Mathematical Models of Desire, Need, Attention, and Will Effort

According to Spinoza, “Love is nothing else but pleasure accompanied by the idea of an external cause”. Author proposes that desire is nothing else but a change of pleasure accompanied by the idea of its cause, that terms ‘desire', ‘want' and their cognates describe change of the pleasantness of the state of a subject (PSS in short) associated with X, that if change of PSS is positive/negative, then X is called desirable/undesirable correspondingly. Both positive and negative desires can be strong, so strength of desire characterizes its magnitude. Need of X is defined here as a cyclical desire of X that gets stronger/weaker with dissatisfaction/satisfaction of its need. Author also explores an idea that the stronger is desire of X by a subject, the more attention this subject pays to X. Distribution of attention and influence on it by the will effort are analyzed in this paper.

The BioDynaMo Project

Computer simulations have become a very powerful tool for scientific research. Given the vast complexity that comes with many open scientific questions, a purely analytical or experimental approach is often not viable. For example, biological systems comprise an extremely complex organization and heterogeneous interactions across different spatial and temporal scales. In order to facilitate research on such problems, the BioDynaMo project aims at a general platform for computer simulations for biological research. Since scientific investigations require extensive computer resources, this platform should be executable on hybrid cloud computing systems, allowing for the efficient use of state-of-the-art computing technology. This chapter describes challenges during the early stages of the software development process. In particular, we describe issues regarding the implementation and the highly interdisciplinary as well as international nature of the collaboration. Moreover, we explain the methodologies, the approach, and the lessons learned by the team during these first stages.

Wild Architecture

In this chapter, the authors focus on cognitive architectures that are developed with the intent to explain human cognition. The authors first describe the mission of cybernetics and early cognitive architectures and recount the popular criticism that these perspectives fail to provide genuine explanations of cognition. Moving forward, the authors propose that there are three pervasive problems that modern cognitive architectures must address: the problem of consciousness, the problem of embodiment, and the problem of representation. Wild Systems Theory (Jordan, 2013) conceptualizes biological cognition as a feature of self-sustaining embodied context that manifests itself at multiple, nested, time-scales. In this manner, Wild Systems Theory is presented as a particularly useful framework for coherently addressing the problems of consciousness, embodiment, and representation.

Integrated Information Theory (IIT) and Artificial Consciousness

This chapter aims to evaluate Integrated Information Theory's claims concerning Artificial Consciousness. Integrated Information Theory (IIT) works from premises that claim that certain properties, such as unity, are essential to consciousness, to conclusions regarding the constraints upon physical systems that could realize consciousness. Among these conclusions is the claim that feed-forward systems, and systems that are not largely reentrant, necessarily will fail to generate consciousness (but may simulate it). This chapter will discuss the premises of IIT, which themselves are highly controversial, and will also address IIT's related rejection of functionalism. This analysis will argue that IIT has failed to established good grounds for these positions, and that convincing alternatives remain available. This, in turn, implies that the constraints upon Artificial Consciousness are more generous than IIT would have them be.

Predictive Regulation in Affective and Adaptive Behaviour

In this chapter, different notions of allostasis (the process of achieving stability through change) as they apply to adaptive behavior are presented. The authors discuss how notions of allostasis can be usefully applied to Cybernetics-based homeostatic systems. Particular emphasis is placed upon affective states – motivational and emotional – and, above all, the notion of ‘predictive' regulation, as distinct from forms of ‘reactive' regulation, in homeostatic systems. The authors focus here on Ashby's ultrastability concept that entails behavior change for correcting homeostatic errors (deviations from the healthy range of essential, physiological, variables). The authors consider how the ultrastability concept can be broadened to incorporate allostatic mechanisms and how they may enhance adaptive physiological and behavioral activity. Finally, this chapter references different Cybernetics frameworks that incorporate the notion of allostasis. The article then attempts to untangle how the given perspectives fit into the ‘allostatic ultrastable systems' framework postulated.