A brief history of liquid computers

Adamatzky, Andrew

doi:10.1098/rstb.2018.0372

Cited by 54 publications

(30 citation statements)

References 183 publications

(251 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Novel models, general enough to include several case studies (even if in abstract terms) will be needed. These might not necessarily be based on living systems, as illustrated by the richness of computational phenomena associated with chemical computers (see [38]). Moreover, the liquid-solid term, which we have used here with no rigorous definition, might become rigorously incorporated using mathematical approaches to collective motion that explicitly account for mobile (self-propelled) entities (see [39], and references therein).…”

Section: Discussionmentioning

confidence: 99%

Liquid brains, solid brains

Solé

Moses

Forrest

2019

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Cognitive networks have evolved a broad range of solutions to the problem of gathering, storing and responding to information. Some of these networks are describable as static sets of neurons linked in an adaptive web of connections. These are ‘solid’ networks, with a well-defined and physically persistent architecture. Other systems are formed by sets of agents that exchange, store and process information but without persistent connections or move relative to each other in physical space. We refer to these networks that lack stable connections and static elements as ‘liquid’ brains, a category that includes ant and termite colonies, immune systems and some microbiomes and slime moulds. What are the key differences between solid and liquid brains, particularly in their cognitive potential, ability to solve particular problems and environments, and information-processing strategies? To answer this question requires a new, integrative framework. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

show abstract

Section: Discussionmentioning

confidence: 99%

Liquid brains, solid brains

Solé

Moses

Forrest

2019

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

show abstract

Section: Robotic Roles Of Intelligent Liquid In Nature or Artificiallmentioning

confidence: 99%

“…Such a device was successfully applied in those earliest stored‐program computers, such as electronic delay storage automatic calculator ( EDSAC ) from England and universal automatic computer ( UNIVAC ) from America. Simultaneously, efforts were made to design and fabricate liquid‐based logic devices in the late 1950s and the early 1960s, which directly relied on specific flowing phenomena, such as the laminar flow, the jet interaction, the wall attachment, and the vortex effect . For instance, a liquid NAND gate was realized with two jet flows, where two nozzles are put in proper angles to ensure the confluence of flows (Figure f) .…”

Section: Robotic Roles Of Intelligent Liquid In Nature or Artificiallmentioning

confidence: 99%

“…Simultaneously, efforts were made to design and fabricate liquid‐based logic devices in the late 1950s and the early 1960s, which directly relied on specific flowing phenomena, such as the laminar flow, the jet interaction, the wall attachment, and the vortex effect . For instance, a liquid NAND gate was realized with two jet flows, where two nozzles are put in proper angles to ensure the confluence of flows (Figure f) . In addition, these logic devices were applied in the programmable sequence control, servo‐control in marine applications, missile and aircraft control, etc.…”

Section: Robotic Roles Of Intelligent Liquid In Nature or Artificiallmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Liquid Integrated Functional Entity: A Basic Way to Innovate Future Advanced Biomimetic Soft Robotics

Liu

Qin

Liu

2019

Advanced Intelligent Systems

View full text Add to dashboard Cite

The liquid is an essential component of natural creatures with profound capacities in assisting homeostatic regulation of physiological functions. However, its participation in more diverse biological behaviors, such as intelligence, motion, energy, etc., is seriously neglected and is undisclosed thus far. Herein, it is considered whether liquid systems can offer pivotal hints in the development of intelligent machines. Starting from this core point, a basic conjecture is proposed that specifically equips a robotic system with liquid that brings about revolutionary design of intelligent systems and plays an elementary role in molding future advanced soft robots. Through a comparative analogy between artificially made machines and natural animals, the basic category of the so-termed Intelligent Liquid Integrated Functional Entity (I-LIFE) is drafted and thus enabled. Then, functions of such unconventional robotic liquid are expounded from five aspects, including motion, energy supply, material performance tuning, sensing, and intelligence, respectively. Typical application examples are also illustrated. Furthermore, a group of intelligent liquids are recommended as candidates for developing future robotic systems. The fundamental scientific and technological challenges lying behind these recommendations are outlined. Finally, prospects of I-LIFE are pointed out, which stimulates the increasing design of a future generation of innovative intelligent robotic systems.

show abstract

“…In fact, several logic gates can be instantiated when two obstacles are placed in the flow Bartlett and Yung (view). These results suggest a new direction for the field of fluid computation (Adamatzky, 2018;Foster and Parker, 1970;Katsikis et al, 2015;Prakash and Gershenfeld, 2007), which has so far not explored the use of convection flows for simple computational logic.…”

mentioning

confidence: 99%

Attractor Landscapes and Information Processing by Convective Obstacle Flows

Bartlett

Yung

2019

The 2019 Conference on Artificial Life

View full text Add to dashboard Cite

We present recent results concerning the attractor landscape, memory, hysteresis and computation that can emerge in simple convective obstacle flows. In these systems a single phase fluid is heated from below and cooled from above. Small obstacles (one or two) are placed on the horizontal mid plane of the system and extract some fraction of the fluid's horizontal or vertical momentum. Horizontal momentum sinks tend to attract convection plumes. Vertical momentum sinks are bistable; the obstacle will either align with a convection cell centre or convection plume depending on initial conditions and the history of the system. The resulting attractor landscape can be exploited to produce a single bit memory or even elementary Boolean logic.

show abstract

A brief history of liquid computers

Cited by 54 publications

References 183 publications

Liquid brains, solid brains

Liquid brains, solid brains

Intelligent Liquid Integrated Functional Entity: A Basic Way to Innovate Future Advanced Biomimetic Soft Robotics

Attractor Landscapes and Information Processing by Convective Obstacle Flows

Contact Info

Product

Resources

About