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I propose a physicalist theory of consciousness that is an extension of the theory of noémona species. The proposed theory covers the full consciousness spectrum from animal to machine and its human consciousness base is compatible with the corresponding work of Wundt, James, and Freud. The paper is organized in three sections. In the first, I briefly justify the methodology used. In Sec. 2, I state the inadequacies of the major work on the nature of consciousness and present a definitional system that adequately describes its changing nature and scope. Finally in Sec. 3, I state some of the consequences of the theory and introduce some of its future extensions.

Consciousness is not only a philosophical but also a technological issue, since a conscious agent has evolutionary advantages. Thus, to replicate a biological level of intelligence in a machine, concepts of machine consciousness have to be considered. The widespread internalistic assumption that humans do not experience the world as it is, but through an internal ‘3D virtual reality model’, hinders this construction. To overcome this obstacle for machine consciousness a new theoretical approach to consciousness is sketched between internalism and externalism to address the gap between experience and physical world. The ‘internal interpreter concept’ is replaced by a ‘key-lock approach’. Here, consciousness is not an image of the external world but the world itself. A possible technological design for a conscious machine is drafted taking advantage of an architecture exploiting selfdevelopment of new goals, intrinsic motivation, and situated cognition. The proposed cognitive architecture does not pretend to be conclusive or experimentally satisfying but rather forms the theoretical the first step to a full architecture model on which the authors currently work on, which will enable conscious agents e.g. for robotics or software applications.

The problem of consciousness is one of the mostimportant problems in science as well as in philosophy. Thereare different philosophers and different scientists who define itand explain it differently. As far as our knowledge ofconsciousness is concerned, ‘consciousness’ does not admit of adefinition in terms of genus and differentia or necessary andsufficient condition. In this paper I shall explore the very idea ofmachine consciousness. The machine consciousness has offeredcausal explanation to the ‘how’ and ‘what’ of consciousness, butthey failed to explain the ‘why’ of consciousness. Theirexplanation is based on the ground that consciousness is causallydependent on the material universe and that of all, consciousnessphenomena can be explained by mapping the physical universe.Again, this mechanical/epistemological theory of consciousnessis essentially committed to scientific world view, which cannotavoid metaphysical implication of consciousness.

Kevin O’Regan argues that seeing is a way of exploring the world, and that this approach helps us understand consciousness. O’Regan is interested in applying his ideas to the modeling of consciousness in robots. Hubert Dreyfus has raised a range of objections to traditional approaches to artificial intelligence, based on his reading of Heidegger. In light of this, I explore here ways in which O’Regan’s approach meets these Heideggerian considerations, and ways in which his account is more Heideggerian than that of Dreyfus. Despite these successes, O’Regan leaves out any role for emotion. This is an area where a Heideggerian perspective may offer useful insights into what more is needed for the sense of self O’Regan includes in his account in order for a robot to feel.

I compare a ‘realist’ with a ‘social–relational’ perspective on our judgments of the moral status of artificial agents (AAs). I develop a realist position according to which the moral status of a being—particularly in relation to moral patiency attribution—is closely bound up with that being’s ability to experience states of conscious satisfaction or suffering (CSS). For a realist, both moral status and experiential capacity are objective properties of agents. A social relationist denies the existence of any such objective properties in the case of either moral status or consciousness, suggesting that the determination of such properties rests solely upon social attribution or consensus. A wide variety of social interactions between us and various kinds of artificial agent will no doubt proliferate in future generations, and the social–relational view may well be right that the appearance of CSS features in such artificial beings will make moral role attribution socially prevalent in human–AA relations. But there is still the question of what actual CSS states a given AA is capable of undergoing, independently of the appearances. This is not just a matter of changes in the structure of social existence that seem inevitable as human–AA interaction becomes more prevalent. The social world is itself enabled and constrained by the physical world, and by the biological features of living social participants. Properties analogous to certain key features in biological CSS are what need to be present for nonbiological CSS. Working out the details of such features will be an objective scientific inquiry.

Functionalism of robot pain claims that what is definitive of robot pain is functional role, defined as the causal relations pain has to noxious stimuli, behavior and other subjective states. Here, the author proposes that the only way to theorize role-functionalism of robot pain is in terms of type-identity theory. The author argues that what makes a state pain for a neuro-robot at a time is the functional role it has in the robot at the time, and this state is type identical to a specific circuit state. Support from an experimental study shows that if the neural network that controls a robot includes a specific 'emotion circuit', physical damage to the robot will cause the disposition to avoid movement, thereby enhancing fitness, compared to robots without the circuit. Thus, pain for a robot at a time is type identical to a specific circuit state.

The stated aim of adherents to the paradigm called biologically inspired cognitive architectures (BICA) is to build machines that address "the challenge of creating a real-life computational equivalent of the human mind".(From the mission statement of the new BICA journal.) In contrast, practitioners of machine consciousness (MC) are driven by the observation that these human minds for which one is trying to find equivalents are generally thought to be conscious. (Of course, this is controversial because there is no evidence of consciousness in behavior. But as the hypothesis of the consciousness of others is commonly used, a rejection of it has to be considered just as much as its acceptance.) In this paper, it is asked whether those who would like to build computational equivalents of the human mind can do so while ignoring the role of consciousness in what is called the mind. This is not ignored in the MC paradigm and the consequences, particularly on phenomenological treatments of the mind, are briefly explored. A measure based on a subjective feel for how well a model matches personal experience is introduced. An example is given which illustrates how MC can clarify the double-cognition tenet of Strawson's cognitive phenomenology.

An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture developed to control non-trivial hexapod walking in an unpredictable environment (Walknet) while using insect-like navigation (Navinet). In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and – via internal simulation of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we focus on the question to what extent properties of phenomena to be characterized on a different level of description as for example consciousness can be found in this minimally cognitive system. Adopting a monist view, we argue that the phenomenal aspect of mental phenomena can be neglected when discussing the function of such a system. Under this condition, reaCog is discussed to be equipped with properties as are bottom-up and top-down attention, intentions, volition, and some aspects of Access Consciousness. These properties have not been explicitly implemented but emerge from the cooperation between the elements of the network. The aspects of Access Consciousness found in reaCog concern the above mentioned ability to plan ahead and to invent and guide (new) actions. Furthermore, global accessibility of memory elements, another aspect characterizing Access Consciousness is realized by this network. reaCog allows for both reactive/automatic control and (access-) conscious control of behavior. We discuss examples for interactions between both the reactive domain and the conscious domain. Metacognition or Reflexive Consciousness is not a property of reaCog. Possible expansions are discussed to allow for further properties of Access Consciousness, verbal report on internal states, and for Metacognition. In summary, we argue that already simple networks allow for properties of consciousness if leaving the phenomenal aspect aside.

Data assimilation is naturally conceived as the synchronization of two systems, “truth” and “model”, coupled through a limited exchange of information (observed data) in one direction. Though investigated most thoroughly in meteorology, the task of data assimilation arises in any situation where a predictive computational model is updated in run time by new observations of the target system, including the case where that model is a perceiving biological mind. In accordance with a view of a semi-autonomous mind evolving in synchrony with the material world, but not slaved to it, the goal is to prescribe a coupling between truth and model for maximal synchronization. It is shown that optimization leads to the usual algorithms for assimilation via Kalman Filtering under a weak linearity assumption. For nonlinear systems with model error and sampling error, the synchronization view gives a recipe for calculating covariance inflation factors that are usually introduced on an ad hoc basis. Consciousness can be framed as self-perception, and represented as a collection of models that assimilate data from one another and collectively synchronize. The combination of internal and external synchronization is examined in an array of models of spiking neurons, coupled to each other and to a stimulus, so as to segment a visual field. The inter-neuron coupling appears to enhance the overall synchronization of the model with reality.

Although many models of consciousness have been proposed from various viewpoints, they have not been based on learning activities in a whole system with the capabilities of autonomous adaptation. We have been investigating a simplified system using artificial neural nodes to clarify the functions and configuration needed for learning in a system that autonomously adapts to the environment. We demonstrated that phenomenal consciousness is explained using a method of "virtualization" in the information system and that learning activities in a whole system adaptation are related to consciousness. However, we have not sufficiently clarified the learning activities of such a system. Consciousness is basically modeled as a system-level learning activity to modify both its own configuration and states in autonomous adaptation through investigating learning activities as a whole system. The model not only explains the time delay in Libet's experiment, but is also positioned as an improved model of Global Workspace Theory (GWT).

In this paper, it will be argued that common sense knowledge has not a unitary structure. It is rather articulated at two different levels: a deep and a superficial level of common sense. The deep level is based on know-how procedures, on metaphorical frames built on imaginative bodily representations, and on a set of adaptive behaviors. Superficial level includes beliefs and judgments. They can be true or false and are culture dependent. Deep common sense is unavailable for any fast change, because it depends more on human biology than on cultural conventions. The deep level of common sense is characterized by a sensorimotor representational format, while the superficial level is largely made by propositional entities. This difference can be considered as a constraint for machine consciousness design, insofar this latter should be based on a reliable model of common sense knowledge.

Efforts to create computational models of consciousness have accelerated over the last two decades, creating a field that has become known as artificial consciousness. There have been two main motivations for this controversial work: to develop a better scientific understanding of the nature of human/animal consciousness and to produce machines that genuinely exhibit conscious awareness. This review begins by briefly explaining some of the concepts and terminology used by investigators working on machine consciousness, and summarizes key neurobiological correlates of human consciousness that are particularly relevant to past computational studies. Models of consciousness developed over the last twenty years are then surveyed. These models are largely found to fall into five categories based on the fundamental issue that their developers have selected as being most central to consciousness: a global workspace, information integration, an internal self-model, higher-level representations, or attention mechanisms. For each of these five categories, an overview of past work is given, a representative example is presented in some detail to illustrate the approach, and comments are provided on the contributions and limitations of the methodology. Three conclusions are offered about the state of the field based on this review: (1) computational modeling has become an effective and accepted methodology for the scientific study of consciousness, (2) existing computational models have successfully captured a number of neurobiological, cognitive, and behavioral correlates of conscious information processing as machine simulations, and (3) no existing approach to artificial consciousness has presented a compelling demonstration of phenomenal machine consciousness, or even clear evidence that artificial phenomenal consciousness will eventually be possible. The paper concludes by discussing the importance of continuing work in this area, considering the ethical issues it raises, and making predictions concerning future developments.

Artificial intelligence, the "science and engineering of intelligent machines", still has yet to create even a simple "Advice Taker" [McCarthy, 1959]. We have previously argued [Waser, 2011] that this is because researchers are focused on problem-solving or the rigorous analysis of intelligence (or arguments about consciousness) rather than the creation of a "self" that can "learn" to be intelligent. Therefore, following expert advice on the nature of self [Llinas, 2001; Hofstadter, 2007; Damasio, 2010], we embarked upon an effort to design and implement a self-understanding, self-improving loop as the totality of a (seed) AI. As part of that, we decided to follow up on Richard Dawkins' [1976] speculation that "perhaps consciousness arises when the brain's simulation of the world becomes so complete that it must include a model of itself" by defining a number of axioms and following them through to their logical conclusions. The results combined with an enactive approach yielded many surprising and useful implications for further understanding consciousness, self, and "free-will" that continue to pave the way towards the creation of safe/moral autopoiesis.

The proponents of machine consciousness predicate the mental life of a machine, if any, exclusively on its formal, organizational structure, rather than on its physical composition. Given that matter is organized on a range of levels in time and space, this generic stance must be further constrained by a principled choice of levels on which the posited structure is supposed to reside. Indeed, not only must the formal structure fit well the physical system that realizes it, but it must do so in a manner that is determined by the system itself, simply because the mental life of a machine cannot be up to an external observer. To illustrate just how tall this order is, we carefully analyze the scenario in which a digital computer simulates a network of neurons. We show that the formal correspondence between the two systems thereby established is at best partial, and, furthermore, that it is fundamentally incapable of realizing both some of the essential properties of actual neuronal systems and some of the fundamental properties of experience. Our analysis suggests that, if machine consciousness is at all possible, conscious experience can only be instantiated in a class of machines that are entirely different from digital computers, namely, timecontinuous, open analog dynamical systems.

Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical operational architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual–theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain–mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the phenomenal level of brain organization. In this context the problem of producing man-made “machine” consciousness and “artificial” thought is a matter of duplicating all levels of the operational architectonics hierarchy (with its inherent rules and mechanisms) found in the brain electromagnetic field. We hope that the conceptual–theoretical framework described in this paper will stimulate the interest of mathematicians and/or computer scientists to abstract and formalize principles of hierarchy of brain operations which are the building blocks for phenomenal consciousness and thought.

I argue here that consciousness can be engineered. The claim that functional consciousness can be engineered has been persuasively put forth in regards to first-person functional consciousness; robots, for instance, can recognize colors, though there is still much debate about details of this sort of consciousness. Such consciousness has now become one of the meanings of the term phenomenal consciousness (e.g., as used by Franklin and Baars). Yet, we extend the argument beyond the tradition of behaviorist or functional reductive views on consciousness that still predominate within cognitive science. If Nagel-Chalmers-Block-style non-reductive naturalism about first-person consciousness (h-consciousness) holds true, then, eventually we should be able to understand how such consciousness operates and how it gets produced (this is not the same as bridging the explanatory gap or solving Chalmers’s hard problem of consciousness). If so, the consciousness it involves can in principle be engineered.

This paper argues that conscious attention exists not so much for selecting an immediate action as for focusing learning of the action-selection mechanisms and predictive models on tasks and environmental contingencies likely to affect the conscious agent. It is perfectly possible to build this sort of system into machine intelligence, but it is not strictly necessary unless the intelligence needs to learn and is resource-bounded with respect to the rate of learning vs. the rate of relevant environmental change. Support of this theory is drawn from scientific research and AI simulations, and a few consequences are suggested with respect to self consciousness and ethical obligations to and for AI.

The paper proposes the design approach as a blueprint for building a sentient artificial agent capable of exhibiting humanlike attributions of consciousness. The paper also considers whether if such an artificial agent is ever built, how it will be indistinguishable from a human being? Well, it is glowingly evident that the evolution of artificial intelligence is guided by us, humans, whose own mental evolution have been shaped by the passing years in the course of the phenomenology of adaptation and survival (Darwinian). Yet, the evolution of synthetic minds powered by artificial cognition seems to be quite fast. Yes, the artificial mind in robots, if we accept the analogy 'mind' in its fullest sense, that day is not very far when the mental embodiment of consciousness in machines would become reality. But prior to such a feat becoming reality, rhetoric debates have been taking shape as of, how to decode and cipher consciousness in machines, a phenomenon considered as often as 'nonentity', then, what would be the true essence of such an artificial consciousness? This paper discusses these aspects and attempts to throw some new light on the design and developmental aspects of artificial consciousness.

In recent years, a classic problem regarding designing of artificial minds embedded with synthetic consciousness has resurfaced in the tune of; 1) building a machine or robots that would closely mimic human behavior, and 2) the problem of embodiment of consciousness in artificial forms in such entities. These two problems boil down to the pure consideration as well of standardization of another aspect- the design concepts; of whether they would look-alike human beings in artificial flesh and skin, or rather be designed entirety as original architecture having shape-implicit forms of embodied cognition which could stand as true peers of human race. The first problem is to deal with the art and science of imitating human behavior, whereas, the subsequent problems should specifically deal with the predicament of abstraction and embodiment of mental attributions primarily, consciousness in machines. Whilst the final dilemma could be the consideration of some standard design models that would likely reflect the nature of such embodied consciousness. In such endeavor, I discuss both the design approach to imitate human abilities in machines as well, the modeling of human consciousness in robots within some relational framework for orientation of mental attributions in such sense that would satisfy evolution of robot consciousness.