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"Prescott, Tony"

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Montes, G. A. (2017). Non-ordinary Consciousness for Artificial Intelligence. In M. Mangan, M. Cutkosky, A. Mura, P. F. M. J. Verschure, T. Prescott, & N. Lepora (Eds.), Biomimetic and Biohybrid Systems (Vol. 10384, pp. 348–362). Springer International Publishing. https://doi.org/10.1007/978-3-319-63537-8_29

Humans are active agents in the design of artificial intelligence (AI), and our input into its development is critical. A case is made for recognizing the importance of including non-ordinary functional capacities of human consciousness in the development of synthetic life, in order for the latter to capture a wider range in the spectrum of neurobiological capabilities. These capacities can be revealed by studying self-cultivation practices designed by humans since prehistoric times for developing non-ordinary functionalities of consciousness. A neurophenomenological praxis is proposed as a model for self-cultivation by an agent in an entropic world. It is proposed that this approach will promote a more complete self-understanding in humans and enable a more thoroughly mutually-beneficial relationship between in life in vivo and in silico.

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Arsiwalla, X. D., Herreros, I., & Verschure, P. (2016). On Three Categories of Conscious Machines. In N. F. Lepora, A. Mura, M. Mangan, P. F. M. J. Verschure, M. Desmulliez, & T. J. Prescott (Eds.), Biomimetic and Biohybrid Systems (pp. 389–392). Springer International Publishing. https://doi.org/10.1007/978-3-319-42417-0_35

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.
Lee, S., Meertens, N., Milner, E., & Hauert, S. (2026). A Framework for the Examination of Awareness in Artificial Systems. In A. Jiménez Rodríguez, R. Mestre, C. Chen, A. Mura, E. Barker, P. Verschure, & T. Prescott (Eds.), Biomimetic and Biohybrid Systems (pp. 320–333). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-07448-5_27

Measuring awareness in artificial agents remains an unresolved challenge. We argue that it holds untapped potential for enhancing their design, control, and effectiveness. In this paper, we propose a novel and tractable approach to measure the impact of awareness on system performance, structured around distinct dimensions of awareness – temporal, spatial, metacognitive, self and agentive. Each dimension is linked to specific capacities and tasks. Specifically, we demonstrate our approach through a swarm robotics intralogistics scenario, where we assess the influence of two dimensions of awareness – spatial and self – on the performance of the swarm in a collective transport task. Our results reveal how increased abilities along these awareness dimensions affect overall swarm efficiency. This framework represents an initial step towards quantifying awareness in, and across, artificial systems.

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Last update from database: 2/5/26, 2:01 AM (UTC)