The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.
We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.
Much useful information in news reports is often that which is surprising or unexpected. In other words, we harbour many expectations about the world, and when any of these expectation are violated (i.e. made inconsistent) by news, we have a strong indicator of some information that is interesting for us. In this paper we present a framework for identifying interesting information in news reports by finding interesting inconsistencies. An implemented system based on this framework (1) accepts structured news reports as inputs, (2) translates each report to a logical literal, (3) identifies the story of which the report is a part, (4) looks for inconsistencies between the report, the background knowledge, and a set of expectations, (5) classifies and evaluates those inconsistencies, and (6) outputs news reports of interest to the user together with associated explanations of why they are interesting.
Cell assemblies (CAs) were posited by Hebb almost 60 years ago as the unit of representation in the brain. Recent results in the field of neuroscience indicate that CAs are likely to exist, at least in the mammalian brain. The CABot project uses simulations of CAs formed from individual neurons as a basis for learning and behaviour. This paper proves that a network of CAs, as described by Hebb and as implemented in CABot, is complete with respect to structured program theory. It follows that such a network is capable of executing any procedure that can be written as an algorithm.
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P rogress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of ''Adam,'' which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge. ( JALA 2010;15:33-40) INTRODUCTIONWe wish to automate all aspects of laboratory science, not just the physical experimentation, but also the intellectual aspects of hypothesis formation, experiment planning, and results analysis. A ''Robot Scientist'' is a physically implemented robotic system that applies techniques from artificial intelligence (AI) to execute cycles of automated scientific experimentation. 1 This contrasts with standard laboratory automation that normally focuses on just the physical aspects of experimentation. Our Robot Scientist ''Adam'' executes, with minimal human intervention, a complex combination of operations on yeast cell cultures at medium to high throughput and, moreover, is capable of modifying those operations according to the behavior of the organisms. 2 Again, this contrasts with standard laboratory automation that is normally characterized by medium-or highthroughput execution of a linear sequence of a relatively small number of different operations. 3e5 Automating Scientific DiscoveryAutomation has been integral to many of the changes in human society since the 19th century. The advent of computer science in the mid-20th century has made practical the idea of automating aspects of scientific discovery. Computers were initially used to automate simple linear processes, for example, to collect and process laboratory instrument data, perform astronomical calculations, and create ballistic tables.Later, AI began to be used to automate aspects of planning experiments and analyzing results. Meta-DENDRAL, developed in the 1960s, was the first automated system for scientific hypothesis generation. 9 and IDS 10 were all impressive examples of automated data-driven discovery systems that could discover scientific laws as algebraic equations. A more recent example uses iterative cycles of algorithmic correlation to distil natural laws of geometric and momentum conservation, using data captured from the motion-tracking studies of a range of simple and complex oscillators and pendula. 11 However, none of the systems described fully ''closes the loop;'' they either do not collect their own data, or do not use analyzed res...
A Robot Scientist is a physically implemented system that applies artificial intelligence to autonomously discover new knowledge through cycles of scientific experimentation. Additionally, our Robot Scientist is able to execute experiments that have been requested by human biologists. There arises a multi-objective problem in the selection of batches of trials to be run together on the robot hardware. We describe the use of the jMetal framework to assess the suitability of a number of multi-objective metaheuristics to optimise the flow of experiments run on a Robot Scientist. Experiments are selected in batches, chosen in order to maximise the information gain and minimise the use of resources. The evolutionary multi-objective algorithms evaluated here perform well in finding solutions to this problem, either finding a long, fairly efficient Pareto optimal front, or a shorter, highly efficient Pareto optimal front.
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