117,617 research outputs found
From prescriptive programming of solid-state devices to orchestrated self-organisation of informed matter
Get PDFAchieving real-time response to complex, ambiguous, high-bandwidth data is impractical with conventional programming. Only the narrow class of compressible input-output maps can be specified with feasibly sized programs. Present computing concepts enforce formalisms that are arbitrary from the perspective of the physics underlying their implementation. Efficient physical realizations are embarrassed by the need to implement the rigidly specified instructions requisite for programmable systems. The conventional paradigm of erecting strong constraints and potential barriers that narrowly prescribe structure and precisely control system state needs to be complemented with a new approach that relinquishes detailed control and reckons with autonomous building blocks. Brittle prescriptive control will need to be replaced with resilient self-organisation to approach the robustness and efficiency afforded by natural systems. Structure-function self-consistency will be key to the spontaneous generation of functional architectures that can harness novel molecular and nano materials in an effective way for increased computational power
Molecular Information Technology
Get PDFMolecular materials are endowed with unique properties of unrivaled potential for high density integration of computing systems. Present applications of molecules range from organic semiconductor materials for low-cost circuits to genetically modified proteins for commercial imaging equipment. To fully realize the potential of molecules in computation, information processing concepts that relinquish narrow prescriptive control over elementary structures and functions are needed, and self-organizing architectures have to be developed. Investigations into qualitatively new concepts of information processing are underway in the areas of reaction-diffusion computing, self-assembly computing, and conformation-based computing. Molecular computing is best considered not as a competitor for conventional computing, but as an opportunity for new applications. Microrobotics and bioimmersive computing are among the domains likely to benefit from advances in molecular computing. Progress will depend on both novel computing concepts and innovations in materials. This article reviews current directions in the use of bulk and single molecules for information processing
Exploration and Exploitation in an Artificial Experimenter
No full textAn artificial experimenter is a computational implementation of the decision making processes a laboratory experimenter will make. Artificial experimenter's analyse the available data, propose hypotheses to represent the behaviours investigated and design experiments to evaluate or improve those hypotheses. In doing so they perform active discovery. A key problem faced is deciding when to perform experiments that exploit the information held within the current hypotheses to evaluate them and when to perform experiments that explore the parameter space to discover features of the behaviour being investigated not yet identified. As resources in physical experimentation are extremely limited, addressing this trade-off is critical to obtaining a representative model of the system under investigation. To achieve this, a Bayesian notion of surprise has been used to effectively manage the transition between exploration and exploitation in simulated and physical experimental trials
Towards Algorithms for Autonomous Experimentation
No full text1 Introduction Modelling biological systems, is impaired by the cost of experimentally obtaining the data required to build the models. The resources available are typically very limited compared to large experimental parameter spaces, so have to be used efficiently. Similarly, in engineering with biological systems such as is found in synthetic biology and molecular computation, models of the phenomena to be harnessed are required. These models can only be obtained experimentally. As a consequence, for engineering with biological systems to become more prevalent, tools and techniques are required to assist in the experimentation performed to develop these models. Here we focus on one such tool, a computational system capable of autonomously investigating an experimental parameter space to identify phenomena that exist within. We call this computational system an autonomous experimentation system. Autonomous experimentation systems try to capture the efficiency of experimentalists, who are able to successfully navigate a seemingly boundless space of potential experiments. Autonomous experimentation systems develop hypotheses, plan experiments and perform experiments, in a closed loop manner without human interaction. As a foundation for our approach to autonomous experimentation, we draw on ideas from the philosophy of science. 2 Experimentation Experimentation should work to disprove hypotheses [1]. A hypothesis gives a possible explanation for some observed phenomena. Hypothesis lead experimentation can benefit from considering many hypotheses simultaneously, so as to allow for different explanations for a particular phenomenon to be developed without prejudice [2, 1]. While human scientists are limited in the number of different working hypotheses that they can realistically contemplate and visualise at any one time, a computer has no such limitation and could compare many thousand different hypotheses simultaneously. The hypotheses that are developed through experimentation, need not be mechanistic in nature. The investigation of the relationship between cause and effect can be performed experimentally, without developing mechanistic hypotheses. It is known for instance that children display an ability to associate cause with effect from an early age through their early play [3]. The use of cause and effect experimentation in scientific, medical and engineering work, has allowed for developments in these areas without an understanding for why the cause and effect are related. For example, a cure for scurvy was produced long before anyone understood why the cure worked [4]. 3 From Experimentation to Autonomous Experimentation Computational systems capable of scientific discovery are interlinked with artificial intelligence systems. Computational scientific discovery puts into practice artificial intelligence methods and brings real world benefits with it. It is important to note that neither artificial intelligence or autonomous experimentation systems will be able to match the abilities of human creativity. The KEKADA system [5], was an early computational experimentation system that was able to develop hypotheses and plan experiments to investigate an experimental parameter space. The KEKADA system took the approach of performing a broad search of the experimental parameter space through experimentation until a surprising phenomena was found, at which point the system performs more focussed experiments on the surprising phenomena. The hypotheses developed were mechanistic in nature and the system was able to rediscover known phenomena, such as determining the mechanism for how urea is synthesised in the body, and determining the structure of common alcohol, which also showed the systems generality in its hypotheses [5]. The KEKADA systems limitations came to the fore when comparing it to scientists, where scientists have the advantage of being able to employ additional heuristics and so able to solve a significantly larger number of problems. The authors concluded that improvements to the KEKADA system could come through increasing the amount of domain knowledge available to the system. Domain knowledge to developing hypotheses in other computational experimentation systems. It was shown that reaction pathways could be obtained automatically from experimental evidence and domain knowledge [6]. The use of increased domain knowledge was influential in the DENDRAL project, which built an expert system for use in scientific reasoning, and in particular aiding structure elucidation in organic chemistry [7]. The combination of domain knowledge and experimental results through abductive reasoning, was shown to be able to rediscover the function of genes in known roles [8]. The Robot Scientist project largely automated the majority of the physical side of experimentation and was also able to show that the computational system and human scientists could comparatively well interpret experimental results, albeit in the limited domain space in which the Robot Scientist operated [8]. In the technical application of enzymes for molecular computing, such large amounts of domain knowledge do not exist. A different approach for autonomous experimentation therefore has to be taken than those that already exist. One autonomous experimentation system has moved away from the requirements of large amounts of domain knowledge and instead concentrated on searching the experimental parameter space for surprising phenomena, through a fully closed-loop system [9]. Such an approach does not lend itself well to the development of mechanistic hypotheses, which is why we look to investigate causality in our approach. By investigating causality, we are still able to develop hypotheses that are both useful to a scientist and are free from large amounts of domain information. 4 Autonomous Experimentation for Molecular Computing Enzymes can be thought of as a pattern recognizer working at the molecular level. An enzymes ability to process multiple inputs simultaneously, make it a candidate for use in molecular computing [10]. At present enzymes cannot be designed for purpose, therefore experimentation is necessary to develop these models. An example of a typical experiment that our system could perform, is monitoring the activity of an enzyme by spectroscopically measuring the ultra-violet (UV) absorbance of the enzyme. Models can then be made of the interplay between different substances that effect the enzymes activity and the quantities of those substances, with the resulting UV absorbance. Of particular interest are regions of the parameter space where a combination of factors cause a specific change in the enzymes behaviour. Such changes in behaviour are the properties that could be harnessed in molecular computation. Our system will determine these regions of interest for molecular computation. A microfluidic device is under development to allow for a fully autonomous, closed-loop experimentation system. This lab-on-a-chip technology uses only small amounts of chemistry for each experiment, which will make autonomous experimentation more practical in terms of cost. We follow a conceptual design similar to those of previous work [5, 11], as shown in figure 1, where hypotheses are generated, experiments are proposed so as to try and disprove hypotheses, then an automatically chosen experiment is performed, with the results being used to update the working set of hypotheses. Any practical experimentation system has to robustly model a set of data, capable of handling noise on the dependent and independent parameters. Our prototype implementation shows that smoothing splines offer these properties for modelling the type of data we expect. We are currently investigating the use of smoothing splines modified with a Bayesian framework, to develop models with lower dimensional data sets than those we expect to be using in later work. These lower dimensional data sets will allow for easier conceptualisation of the problems of autonomously developing hypotheses and methods for exploring the experimental parameter space
Swarm behavioral sorting based on robotic hardware variation
No full textSwarm robotic systems can offer advantages of robustness, flexibility and scalability, just like social insects. One of the issues that researchers are facing is the hardware variation when implementing real robotic swarms. Identical software cannot guarantee identical behaviors among all robots due to hardware differences between swarm members. We propose a novel approach for sorting swarm robots according to their hardware differences. This method is based on the large number of interactions between robots and the environment. Individual robot’s unique hardware circumstance determines its unique decision and reaction during each robotic controlling step, and these unique local reactions accumulate and contribute to the robot’s global behavior. Accordingly by separating these hardware-triggered global behaviors, swarm robots can be sorted according to their hardware variations
Molecular Computing: from conformational pattern recognition to complex processing networks
No full textNatural biomolecular systems process information in a radically different manner than programmable machines. Conformational interactions, the basis of specificity and self-assembly, are of key importance. A gedanken device is presented that illustrates how the fusion of information through conformational self-organization can serve to enhance pattern processing at the cellular level. The device is used to highlight general features of biomolecular information processing. We briefly outline a simulation system designed to address the manner in which conformational processing interacts with kinetic and higher level structural dynamics in complex biochemical networks. Virtual models that capture features of biomolecular information processing can in some instances have artificial intelligence value in their own right and should serve as design tools for future computers built from real molecules
Design of interacting multi-stable nucleic acids for molecular information processing
No full textDespite an exponential increase in computing power over the past decades, present information technology falls far short of expectations in areas such as cognitive systems and micro robotics. Organisms demonstrate that it is possible to implement information processing in a radically different way from what we have available in present technology, and that there are clear advantages from the perspective of power consumption, integration density, and real-time processing of ambiguous data. Accordingly, the question whether the current silicon substrate and associated computing paradigm is the most suitable approach to all types of computation has come to the fore. Macromolecular materials, so successfully employed by nature, possess uniquely promising properties as an alternate substrate for information processing. The two key features of macromolecules are their conformational dynamics and their self-assembly capabilities. The purposeful design of macromolecules capable of exploiting these features has proven to be a challenge, however, for some groups of molecules it is increasingly practicable. We here introduce an algorithm capable of designing groups self-assembling of nucleic acid molecules with multiple conformational states. Evaluation using natural and artificially designed nucleic acid molecules favours this algorithm significantly, as compared to the probabilistic approach. Furthermore, the thermodynamic properties of the generated candidates are within the same approximation as the customised trans-acting switching molecules reported in the laboratory
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