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Can we conceive machines that can formulate autonomous intentions and make conscious decisions? If so, how would this ability affect their ethical behavior? Some case studies help us understand how advances in understanding artificial consciousness can contribute to creating ethical AI systems.

Humans are highly intelligent, and their brains are associated with rich states of consciousness. We typically assume that animals have different levels of consciousness, and this might be correlated with their intelligence. Very little is known about the relationships between intelligence and consciousness in artificial systems. Most of our current definitions of intelligence describe human intelligence. They have severe limitations when they are applied to non-human animals and artificial systems. To address this issue, this chapter sets out a new interpretation of intelligence that is based on a system’s ability to make accurate predictions. Human intelligence is measured using tests whose results are converted into values of IQ and g-score. This approach does not work well with non-human animals and AIs, so people have been developing universal algorithms that can measure intelligence in any type of system. In this chapter a new universal algorithm for measuring intelligence is described, which is based on a system’s ability to make accurate predictions. Many people agree that consciousness is the stream of colorful moving noisy sensations that starts when we wake up and ceases when we fall into deep sleep. Several mathematical algorithms have been developed to describe the relationship between consciousness and the physical world. If these algorithms can be shown to work on human subjects, then they could be used to measure consciousness in non-human animals and artificial systems.

Synthetic phenomenology typically focuses on the analysis of simplified perceptual signals with small or reduced dimensionality. Instead, synthetic phenomenology should be analyzed in terms of perceptual signals with huge dimensionality. Effective phenomenal processes actually exploit the entire richness of the dynamic perceptual signals coming from the retina. The hypothesis of a high-dimensional buffer at the basis of the perception loop that generates the robot synthetic phenomenology is analyzed in terms of a cognitive architecture for robot vision the authors have developed over the years. Despite the obvious computational problems when dealing with high-dimensional vectors, spaces with increased dimensionality could be a boon when searching for global minima. A simplified setup based on static scene analysis and a more complex setup based on the CiceRobot robot are discussed.

Machine consciousness is not only a technological challenge, but a new way to approach scientific and theoretical issues which have not yet received a satisfactory solution from AI and robotics. We outline the foundations and the objectives of machine consciousness from the standpoint of building a conscious robot.

Recently, there has been considerable interest and effort to the possibility to design and implement conscious robots, i.e., the chance that robots may have subjective experiences. Typical approaches as the global workspace, information integration, enaction, cognitive mechanisms, embodiment, i.e., the Good Old-Fashioned Artificial Consciousness, henceforth, GOFAC, share the same conceptual framework. In this paper, we discuss GOFAC's basic tenets and their implication for AI and Robotics. In particular, we point out the intermediate level fallacy as the central issue affecting GOFAC. Finally, we outline a possible alternative conceptual framework toward robot consciousness.</p>

There are many developed theories and implemented artificial systems in the area of machine consciousness, while none has achieved that. For a possible approach, we are interested in implementing a system by integrating different theories. Along this way, this paper proposes a model based on the global workspace theory and attention mechanism, and providing a fundamental framework for our future work. To examine this model, two experiments are conducted. The first one demonstrates the agent’s ability to shift attention over multiple stimuli, which accounts for the dynamics of conscious content. Another experiment of simulations of attentional blink and lag-1 sparing, which are two well-studied effects in psychology and neuroscience of attention and consciousness, aims to justify the agent’s compatibility with human brains. In summary, the main contributions of this paper are (1) Adaptation of the global workspace framework by separated workspace nodes, reducing unnecessary computation but retaining the potential of global availability; (2) Embedding attention mechanism into the global workspace framework as the competition mechanism for the consciousness access; (3) Proposing a synchronization mechanism in the global workspace for supporting lag-1 sparing effect, retaining the attentional blink effect.

One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot.

Thinking and being conscious are two fundamental aspects of the subject. Although both are challenging, often conscious experience has been considered more elusive (Chalmers 1996). However, in recent years, several researchers addressed the hypothesis of designing and implementing models for artificial conscious-ness—on one hand there is hope of being able to design a model for consciousness, on the other hand the actual implementations of such models could be helpful for understanding consciousness. The traditional field of Artificial Intelligence is now flanked by the seminal field of artificial or machine consciousness. In this chapter I will analyse the current state of the art of models of consciousness and then I will outline an externalist theory of the conscious mind that is compatible with the design and implementation of an artificial conscious being. As I argue in the following, this task can be profitably approached once we abandon the dualist framework of traditional Cartesian substance metaphysics and adopt a process-metaphysical stance. Thus, I sketch an alternative externalist process-based ontological framework. From within this framework, I venture to suggest a series of constraints for a conscious oriented architecture.

The theory of consciousness is a subject that has kept scholars and researchers challenged for centuries. Even today it is not possible to define what consciousness is. This has led to the theorization of different models of consciousness. Starting from Baars’ Global Workspace Theory, this paper examines the models of cognitive architectures that are inspired by it and that can represent a reference point in the field of robot consciousness. Recent Findings Global Workspace Theory has recently been ranked as the most promising theory in its field. However, this is not reflected in the mathematical models of cognitive architectures inspired by it: they are few, and most of them are a decade old, which is too long compared to the speed at which artificial intelligence techniques are improving. Indeed, recent publications propose simple mathematical models that are well designed for computer implementation. Summary In this paper, we introduce an overview of consciousness and robot consciousness, with some interesting insights from the literature. Then we focus on Baars’ Global Workspace Theory, presenting it briefly. Finally, we report on the most interesting and promising models of cognitive architectures that implement it, describing their peculiarities.

The experience of inner speech is a common one. Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness. Here, the widespread psychological models of inner speech are reviewed, and a cognitive architecture for a robot implementing such a capability is outlined in a simplified setup.

The fields of artificial intelligence (AI) and artificial consciousness (AC) have largely developed separately, with different goals and criteria for success and with only a minimal exchange of ideas. In this chapter, we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. We describe our recent efforts to explore this hypothesis computationally and to identify associated computational correlates of consciousness. We then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence.

To approach the creation of artificial conscious systems systematically and to obtain certainty about the presence of phenomenal qualities (qualia) in these systems, we must first decipher the fundamental mechanism behind conscious processes. In achieving this goal, the conventional physicalist position exhibits obvious shortcomings in that it provides neither a plausible mechanism for the generation of qualia nor tangible demarcation criteria for conscious systems. Therefore, to remedy the deficiencies of the standard physicalist approach, a new theory for the understanding of consciousness has been formulated. The aim of the paper is to present the cornerstones of this theory, to outline the conditions for conscious systems derived from the theory, and to address the implications of these conditions for the creation of robots that transcend the threshold of phenomenal awareness. In short, the theory is based on the proposition that the universe is permeated by a ubiquitous field of consciousness that can be equated with the zero-point field (ZPF) of quantum electrodynamics (QED). The ZPF, which is characterized by a spectrum of field modes, plays a crucial role in the edifice of modern physics. QED-based model calculations on cortical dynamics and empirical findings on the neural correlates of consciousness suggest that a physical system can only generate conscious states if it is capable of establishing resonant coupling to the ZPF, resulting in the amplification of selected field modes and the activation of the phenomenal qualities that are assumed to be associated with these modes. Thus, scientifically sound considerations support the conclusion that the crucial condition for generating conscious states lies in a system's capacity to tap into the phenomenal color palette inherent in the ZPF.

The concept of qualia poses a central problem in the framework of consciousness studies. Despite it being a controversial issue even in the study of human consciousness, we argue that qualia can be complementarily studied using artificial cognitive architectures. In this work we address the problem of defining qualia in the domain of artificial systems, providing a model of “artificial qualia”. Furthermore, we partially apply the proposed model to the generation of visual qualia using the cognitive architecture CERA-CRANIUM, which is modeled after the global workspace theory of consciousness. It is our aim to define, characterize and identify artificial qualia as direct products of a simulated conscious perception process. Simple forms of the apparent motion effect are used as the basis for a preliminary experimental setting focused on the simulation and analysis of synthetic visual experience. In contrast with the study of biological brains, the inspection of the dynamics and transient inner states of the artificial cognitive architecture can be performed effectively, thus enabling the detailed analysis of covert and overt percepts generated by the system when it is confronted with specific visual stimuli. The observed states in the artificial cognitive architecture during the simulation of apparent motion effects are used to discuss the existence of possible analogous mechanisms in human cognition processes.