International Journal of Machine Consciousness

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.

It is argued here that the phenomenon of consciousness is nothing more than a special way of a subjective internal appearance of information. To explain consciousness is to explain how this subjective internal appearance of information can arise in the brain. To create a conscious robot is to create subjective internal appearances of information inside the robot. Other features that are often attributed to the phenomenon of consciousness are related to the contents of consciousness and cognitive functions. The internal conscious appearance of these is caused by the mechanism that gives rise to the internal appearances in the first place. A useful conscious robot must have a variety of cognitive abilities, but these abilities alone, no matter how advanced, will not make the robot conscious; the phenomenal internal appearances must be present as well. The Haikonen Cognitive Architecture (HCA) tries to facilitate both internal appearances and cognitive functions. The experimental robot XCR-1 is the first implementation experiment with the HCA.

Functional or access consciousness can be described as an ongoing dynamic process of queries and answers. Whenever we have an awareness of an object or its surroundings, it consists of the dynamic process that answers (implicit) queries like "What is the color or shape of the object?" or "What surrounds this object?" The process of queries and answers is based on a computational architecture that integrates grounding of representations with cognitive productivity. The human brain may be unique in combining grounding and productivity. Because representations have to remain grounded in combinatorial structures underlying the productivity of cognition, they have to remain in situ. Hebbian neuronal assemblies are an example of in situ conceptual representations, although the latter are not just associative. To obtain productivity, in situ representations are embedded in specialized neuronal "blackboards" by which (temporal) combinatorial structures can be formed. In situ representations interact in these blackboards. This interaction initiates the (implicit) query and answer process underlying functional consciousness. In this process, an in situ representation, dominating one blackboard, could begin to dominate other blackboards as well. Viewed in this way, human consciousness derives from the unique ability of the human brain to combine grounding and cognitive productivity.

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).

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 paper explains the necessity, for humanoid robots, of the body schema, as a middleware between motor cognition and motor control. A specific model of body schema is described, based on the Passive Motion Paradigm, which uses force fields for representing goals and internal/external constraints. The integration of the body schema with motor control is discussed in relation with whole body movements. Finally, the integration with sensorimotor cognitive processes is addressed, in the context of learning/discovering the use of tools in skilled behavior.

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.