Programmable active matter (PAM) combines information processing and energy transduction. The physical embodiment of information could be the direction of magnetic spins, a sequence of molecules, the concentrations of ions, or the shape of materials. Energy transduction involves the transformation of chemical, magnetic, or electrical energies into mechanical energy. A major class of PAM consists of material systems with many interacting units. These units could be molecules, colloids, microorganisms, droplets, or robots. Because the interaction among units determines the properties and functions of PAMs, the programmability of PAMs is largely due to the programmable interactions. Here, we review PAMs across scales, from supramolecular systems to macroscopic robotic swarms. We focus on the interactions at different scales and describe how these (often local) interactions give rise to global properties and functions. The research on PAMs will contribute to the pursuit of generalised crystallography and the study of complexity and emergence. Finally, we ponder on the opportunities and challenges in using PAM to build a soft-matter brain.

To recognize alien life, we would have to be clear about the defining criteria of ‘life’. Metabolism – in other words, biochemical fine-tuning – is one of these criteria. Three senses of metabolism are distinguished. The weakest allows strong artificial life (A-life): virtual creatures having physical existence in computer electronics, but not bodies, are classed as ‘alive’. The second excludes strong A-life but allows that some non-biochemical A-life robots could be classed as alive. The third, which stresses the body's self-production by energy budgeting and self-equilibrating energy exchanges of some (necessary) complexity, excludes both strong A-life and living non-biochemical robots.

In this paper fhe problem of motion control of a biped is considered. We develop a new method based on multi-agent associated Neural AIGLS (On-line Augmented Integration of Gradient and Last Sguare method) – RSPN (Recursive Stochastic Petri Nets) strategy. This method deals with organization and coordination aspects in an intelligent modeling of human motion. We propose a cooperative multi-agent model. Based on this model, we develop a control kernel named IMCOK (Intelligent Motion COntrol Kernel) which consists of a controller, a coordinator and an executor of different cycles of the motion of the biped. When walking, IMCOK receives messages and sends offers. A Decision Making of Actions (DMA) is developed at the supervisor level. The articulator agents partially planify the motion of the associated non-articulator agents. The system is hybrid and distributed functionally. The learning of the biped is performed using an On-line Augmented Integration of Gradient and Last Sguare Neural Networks based algorithm. In the conflictual situations of sending or receiving messages by the managers of MABS we apply a new strategy: Recursive Stochastic Petri Nets (RSPN). This module is fundamental in the On-line information processing between agents. It allows particularly the Recursive strategy concept. Cognitive agents communicate with reactive (non-articulator) agents in order to generate the motion.