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Reviewing recent closely related developments at the crossroads of biomedical engineering, artificial intelligence and biomimetic technology, in this paper, we attempt to distinguish phenomenological consciousness into three categories based on embodiment: one that is embodied by biological agents, another by artificial agents and a third that results from collective phenomena in complex dynamical systems. Though this distinction by itself is not new, such a classification is useful for understanding differences in design principles and technology necessary to engineer conscious machines. It also allows one to zero-in on minimal features of phenomenological consciousness in one domain and map on to their counterparts in another. For instance, awareness and metabolic arousal are used as clinical measures to assess levels of consciousness in patients in coma or in a vegetative state. We discuss analogous abstractions of these measures relevant to artificial systems and their manifestations. This is particularly relevant in the light of recent developments in deep learning and artificial life.

Companion or ‘pet’ robots can be expected to be an important part of a future in which robots contribute to our lives in many ways. An understanding of emotional interactions would be essential to such robots’ behavior. To improve the cognitive and behavior systems of such robots, we propose the use of an artificial topological consciousness that uses a synthetic neurotransmitter and motivation, including a biologically inspired emotion system. A fundamental aspect of a companion robot is a cross-communication system that enables natural interactions between humans and the robot. This paper focuses on three points in the development of our proposed framework: (1) the organization of the behavior including inside-state emotion regarding the phylogenetic consciousness-based architecture; (2) a method whereby the robot can have empathy toward its human user’s expressions of emotion; and (3) a method that enables the robot to select a facial expression in response to the human user, providing instant human-like ‘emotion’ and based on emotional intelligence (EI) that uses a biologically inspired topological online method to express, for example, encouragement or being delighted. We also demonstrate the performance of the artificial consciousness based on the complexity level and a robot’s social expressions that are designed to enhance the users affinity with the robot.

A discussion on how artificial machines with natural intelligence would be safe or not is made based on scientific, philosophical and theological arguments. The finite or infinite nature of the universe is discussed and the implications analyzed. The concepts of destiny and free will are considered, with implications on what it would mean to create an artificial consciousness and how it would be possible to give it or deny it its free will. Computer experiments are carried out based on cellular automata and the results considered. A thorough discussion follows and a conclusion is reached.

Currently, the rapid development of non-industrial robots that are designed with artificial intelligence (AI) methods to improve the robotics system is to have them imitate human thinking and behavior. Therefore, our works have focused on studying and investigating the application of brain-inspired technology for developing the conscious behavior robot (Conbe-I). We created the hierarchical structure model, which is called “Consciousness-Based Architecture: CBA” module, but it has limitation in managing and selecting the behavior that only depends on the increase and decrease of the motivation levels. Consequently, in this paper, we would like to introduce the dynamic behavior selection model based on emotional states, which develops by Self-organizing map learning and Markov model in order to define the relationship between the behavioral selection and emotional expression model. We confirm the effectiveness of the proposed system with the experimental results.

Traditional approaches model consciousness as the outcome either of internal computational processes or of cognitive structures. We advance an alternative hypothesis – consciousness is the hallmark of a fundamental way to organise causal interactions between an agent and its environment. Thus consciousness is not a special property or an addition to the cognitive processes, but rather the way in which the causal structure of the body of the agent is causally entangled with a world of physical causes. The advantage of this hypothesis is that it suggests how to exploit causal coupling to envisage tentative guidelines for designing conscious artificial agents. In this paper, we outline the key characteristics of these causal building blocks and then a set of standard technologies that may take advantage of such an approach. Consciousness is modelled as a kind of cognitive middle ground and experience is not an internal by-product of cognitive processes but the external world that is carved out by means of causal interaction. Thus, consciousness is not the penthouse on top of a 50 stores cognitive skyscraper, but the way in which the steel girders snap together from bottom to top.

In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach – Global Workspace Theory – in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the “Artificial Consciousness” traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%.

This paper presents the idea that using computers to simulate complex virtual environments can give rise to artificial consciousness within those environments. Currently, limitations in creating artificial consciousness may be imposed by material compounds that enable the transmission of signals through artificial systems such as robots. Virtual environments, on the other hand, provide the necessary tools for surpassing such limitations. I also argue that using virtual reality tools to enable complex interaction between humans and AI (artificial intelligence) within virtual environments is the most reasonable way to obtain artificial consciousness by appealing to the nature of human bias.

This is a proof of the strong AI hypothesis, i.e. that machines can be conscious. It is a phenomenological proof that pattern-recognition and subjective consciousness are the same activity in different terms. Therefore, it proves that essential subjective processes of consciousness are computable, and identifies significant traits and requirements of a conscious system. Since Husserl, many philosophers have accepted that consciousness consists of memories of logical connections between an ego and external objects. These connections are called "intentions." Pattern recognition systems are achievable technical artifacts. The proof links this respected introspective philosophical theory of consciousness with technical art. The proof therefore endorses the strong AI hypothesis and may therefore also enable a theoretically-grounded form of artificial intelligence called a "synthetic intentionality," able to synthesize, generalize, select and repeat intentions. If the pattern recognition is reflexive, able to operate on the set of intentions, and flexible, with several methods of synthesizing intentions, an SI may be a particularly strong form of AI. Similarities and possible applications to several AI paradigms are discussed. The article then addresses some problems: The proof's limitations, reflexive cognition, Searles' Chinese room, and how an SI could "understand" "meanings" and "be creative."

We present the software implementation of our Qualia Modeling Framework (QMF), a computational cognitive model based on the dual-process theory, which theorizes that reasoning and decision making rely on integrated experiences from two interactive minds: the autonomous mind, without the agent’s conscious awareness, and the reflective mind, of which the agent is consciously aware. In the QMF, artificial qualia are the vocabulary of the conscious mind, required to reason over conceptual memory, and generate cognitive inferences. The autonomous mind employs pattern-matching, for fast reasoning over episodic memories. An ACT-R model with conventional declarative memory represents the autonomous mind. A second ACT-R model, with an unconventional implementation of declarative memory utilizing a hypernetwork theory based model of qualia space, represents the reflective mind. Using real-world, non-trivial, data sets, our cognitive model achieved classification accuracy comparable to, or greater than, analogous machine learning classifiers kNN and DT, while providing improvements in flexibility by allowing the Target Attribute to be identified or changed any time during training and testing. We advance the BICA challenge by providing a generalizable, efficient, algorithm which models the phenomenal structure of consciousness as proposed by a contemporary theory, and provides an effective decision aid in complex environments where data are too broad or diverse for a human to evaluate without computational assistance.

Although the thermal grill illusion has been the topic of previous research, many mysteries still remain regarding psychological determinants, neurophysiological mechanisms and so on. Also, the illusion cannot be simulated by information science and robotics. This study focuses on a very simple but interesting experiment called Hot and Cold Coils, which is known as a typical example of the thermal grill illusion. The authors aim to explain the thermal grill illusion by proposing a new and bold assumption called the conflict of concepts, and demonstrate how to construct a model by using an artificial consciousness module called the Module of Nerves for Advanced Dynamics (MoNAD). A simple experimental apparatus was prepared to prove the existence of the thermal grill illusion, and consists of a parallel arrangement of bars with an alternating pattern of cold and warmth at 20°C and 40°C. The authors conclude with the belief that many complex perceptions of humanity can be simulated through the use of neural networks, and that this can help us to deeply study the cognitive processes of human perception.

Human consciousness is a target of research in multiple fields of knowledge, that presents it as an important characteristic to better handle complex and diverse situations. Artificial consciousness models have arose, together with theories that attempt to model what we understand about consciousness, in a way that could be implemented an artificial conscious being. The main motivations to study artificial consciousness are related to the creation of agents more similar to human beings, in order to build more efficient machines. This paper presents an experiment using the Global Workspace Theory and the LIDA Model to build a "conscious" mobile robot in a virtual environment, using the LIDA framework as a implementation of the LIDA Model. The main objective is to evaluate if it is possible to use conscience as implemented by the LIDA framework to simplify decision making processes during navigation of a mobile robot subject to interaction with people, as part of a cicerone robot development.

This paper aims to develop the research based on a pet robot and its artificial consciousness. We propose the animal behavior and emotion using the artificial neurotransmitter and motivation. This research still implements the communication between human and a pet robot respecting to a social cognitive and interaction. Thus, the development of cross-creature communication is crucial for friendly companionship. This system focuses on three points. The first that is the organization of the behavior and emotion model regarding the phylogenesis. The second is the method of the robot that can have empathy with user expression. The third is how the robot can socially perform its expression to human for encouragement or being delighted based on its own emotion and the human expression. This paper eventually presents the performance and the experiment that the robot using cross-perception and cross-expression between animal robot and social interaction of human communication based on the consciousness based architecture (CBA).

This note reports on interdisciplinary approaches to model consciousness, an aspect of self-awareness in particular, aiming at artificial consciousness that can be mounted on an autonomous and mobile robot. For self-awareness to emerge, the self-identification process plays an important role. Self-awareness would emerge when self-locating in a self-created map in robot navigation; when solving self-related problems in (a self-related version of) the frame problem; and when a singularity arises in mapping the reference point in mathematical mappings.

This paper critically assesses the anti-functionalist stance on consciousness adopted by certain advocates of integrated information theory (IIT), a corollary of which is that human-level artificial intelligence implemented on conventional computing hardware is necessarily not conscious. The critique draws on variations of a well-known gradual neuronal replacement thought experiment, as well as bringing out tensions in IIT's treatment of self-knowledge. The aim, though, is neither to reject IIT outright nor to champion functionalism in particular. Rather, it is suggested that both ideas have something to offer a scientific understanding of consciousness, as long as they are not dressed up as solutions to illusory metaphysical problems. As for human-level AI, we must await its development before we can decide whether or not to ascribe consciousness to it.

It is suggested that some limitations of current designs for medical AI systems (be they autonomous or advisory) stem from the failure of those designs to address issues of artificial (or machine) consciousness. Consciousness would appear to play a key role in the expertise, particularly the moral expertise, of human medical agents, including, for example, autonomous weighting of options in (e.g.,) diagnosis; planning treatment; use of imaginative creativity to generate courses of action; sensorimotor flexibility and sensitivity; empathetic and morally appropriate responsiveness; and so on. Thus, it is argued, a plausible design constraint for a successful ethical machine medical or care agent is for it to at least model, if not reproduce, relevant aspects of consciousness and associated abilities. In order to provide theoretical grounding for such an enterprise we examine some key philosophical issues that concern the machine modelling of consciousness and ethics, and we show how questions relating to the first research goal are relevant to medical machine ethics. We believe that this will overcome a blanket skepticism concerning the relevance of understanding consciousness, to the design and construction of artificial ethical agents for medical or care contexts. It is thus argued that it would be prudent for designers of MME agents to reflect on issues to do with consciousness and medical (moral) expertise; to become more aware of relevant research in the field of machine consciousness; and to incorporate insights gained from these efforts into their designs.

Auditory perception is an essential part of environment perception, in which the saliency detection is not only the fundamental basis but also an efficient way of achieving this task. For artificial machines, intelligent perception approach of sound is required to provide awareness as the initiatory step of artificial consciousness. In this paper, a novel salient environment sound detection framework for machine awareness is proposed. The framework is based on the heterogeneous saliency features from both image and acoustic channels. To improve the efficiency of proposed framework, (1) a global informative saliency estimation approach is initially proposed based on short-term Shannon entropy; (2) a series of auditory saliency detection methods is presented to obtain the spectral and temporal saliency features from power spectral density and mel-frequency cepstral coefficients, respectively; (3) a computational bio-inspired inhibition of return model is proposed for saliency verification to improve the accuracy of detection; (4) a heterogeneous saliency feature fusion approach is introduced to form the final auditory saliency map by combining the acoustic and image saliency features together. Environmental sounds which collected from real world are applied to verify the superiority of the proposed framework. The results show that, the proposed framework is more effective for the detection of the overlapped salient sounds, and is more robust to the background noise compared with the conventional approach.

This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD). Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

One model for creating artificial consciousness is replicating every fine detail of the brain on computers and setting the model in motion. Consciousness has been experimentally demonstrated to be a much more fragmented experience than we think it to be, perhaps we only need snippets of ourselves to feel conscious. Perhaps consciousness is nothing less and nothing more than story, and all we need do to continue to feel conscious is maintain identity through computer-based narrative. Applied nanotechnology has generated uncountable applications in electronics, pharmacology, and materials engineering. The best approach to life extension and consciousness expansion might lie in our own marvelously complex and entire bodies, meshed with and augmented by tiny bionan machines that become a part of us, rather than the opposite vision of humans migrating into a machine substrate.

The goal of this chapter is to present an overview of the work in AI on emotions and machine consciousness, with an eye toward answering these questions. Starting with a brief philosophical perspective on emotions and machine consciousness to frame the work, the chapter first focuses on artificial emotions, and then moves on to machine consciousness – reflecting the fact that emotions and consciousness have been treated independently and by different communities in AI. The chapter concludes by discussing philosophical implications of AI research on emotions and consciousness.

Machine consciousness is a young research field, yet inspired by oldest intellectual disciplines like philosophy of mind. Specifically, the mind–body problem has been approached since ancient times and different accounts have been proposed along the centuries. While none of these accounts, like different forms of dualism, have been seen as useful working hypotheses in the domain of machine consciousness, their influence might have shaped the orientation of this research field towards a frantic search for an illusory and unachievable bridge for the explanatory gap. In his book, Consciousness and Robot Sentience, Haikonen seems to claim back the predominant position that engineering should have in a domain, where we are supposed to deliver pragmatic solutions. In this regard, Haikonen is actually bridging the gap between the philosophical discourse and the practical engineering approach. This is a remarkable movement as Haikonen is essentially claiming that his cognitive architecture is a proof of the inexistence of such a thing as a mind–body problem. In this book review, I analyze the implications, limitations, and prospects of this engineering stance, looking at the main contributions and those aspects that might require further explanation.