Computer Science. The English Language Perspective - Беликова (1176925), страница 43
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Answer the following questions:1.2.3.4.How was the term “robot” coined?What are the limitations of industrial robots?Where are mobile robots being used?What approaches does the development of smart robotscall for?5. What are the advantages of swarm robotics overconventional approaches?6. What are the major challenges posed by swarmrobotics?7.
Where can swarm intelligence be of practical assistance?3. Translate into English:Проект TERMES, реализуемый в течение четырех летисследовательской группой самоорганизующихся системГарвардского университета, в основе которого лежитмоделирование поведения колонии термитов, имеетконечную цель в создании масштабируемой системыискусственного интеллекта, в основе которой лежатпростейшие роботы, способные уже сейчас совместнымиусилиями строить башни, пирамиды и другие сооружения,возводя даже дополнительные элементы, позволяющиероботам подниматься выше.254Данный проект имеет абсолютно другой подход корганизации работ, нежели традиционная иерархическаясистема, в которой основной план движется, дробясь намножество мелких задач, от руководителей высшего звеначерезчередуменеджеровиспециалистовкнепосредственным исполнителям. Вместо этого, модельколонии термитов предусматривает выполнение работкаждымроботомобособленно,безвсякогоцентрализованного руководства.
Исследователи объясняют,что роботы действуют при помощи принципа стигмергии(stigmergy), принципа неявных коммуникаций, когдакаждый индивидуум распознает изменения окружающейего среды и корректирует свои собственные планы всоответствии с этими изменениями.Благодаря использованию принципа стигмергии,роботы TERMES могут работать группами от несколькихэкземпляров до нескольких тысяч, выполняя единуюзадачу, но абсолютно не общаясь друг с другом. Отсутствиецентрализованного управления означает, что у системы вцелом имеется крайне высокий уровень надежности, выходиз строя одного экземпляра робота не приводит кнеработоспособности системы, а оставшиеся работыпродолжают работу, не замечая этого факта.
Такой подходпозволяет сделать роботов максимально простыми, ведь имнетребуетсяналичиярадиоилидругогокоммуникационного канала, работающего на иныхпринципах. Роботы TERMES, созданные гарвардскимиисследователями, имеют всего по четыре датчика, по тринезависимыхприводаинесложныймеханизм,позволяющийбрать,переноситьиукладыватьстроительные блоки.4. Give the summary of the text using the key terms.255ARTIFICIAL LIFERead the following words and word combinations and usethem for understanding and translation of the text:manifold - разнообразный, многообразныйblanket term - общий терминdesign space - пространство проектных решений(параметров)generalize - обобщатьto conceive - задумать, замыслить, разработатьtypified - на примереcrossover - кроссинговер (перекрест хромосом)to intervene - вмешиватьсяfull-blown design - полнофункциональная модель(образец)laypeople - непрофессионалыcarbon chemistry - химия углеродных соединенийspecies of prey - хищный видpredator - хищникto validate - подтверждать, проверять правильностьconversely - наоборот, напротив, с другой стороныto remedy - лечить, исправлятьcommitment to the idea - приверженность идееautopoiesis - самосоздание, самовоспроизводствоThe historical and theoretical roots of the field are manifold.These roots include:early attempts to imitate the behavior of humans andanimals by the invention of mechanical automata in thesixteenth century;cybernetics as the study of general principles ofinformational control in machines and animals;computer science as theory and the idea of abstractequivalence between various ways to express the notion256of computation, including physical instantiations ofsystems performing computations;John von Neumann's so-called self-reproducing CellularAutomata;computer science as a set of technical practices andcomputational architectures;artificial intelligence (AI)robotics;philosophy and system science notions of levels oforganization, hierarchies, and emergence of newproperties;non-linear science, such as the physics of complexsystems and chaos theory; theoretical biology, includingabstract theories of life processes; andevolutionary biology.Artificial life is a blanket term used to refer to human attemptsat setting up systems with lifelike properties all biologicalorganisms possess, such as self-reproduction, homeostasis,adaptability, mutational variation, optimization of externalstates, and so on.
The term is commonly associated withcomputer simulation-based artificial life, preferred heavily torobotics because of its ease of reprogramming, inexpensivehardware, and greater design space to explore. Artificial lifeprojects can be thought of as attempts to generalize thephenomenon of life, asking questions like, "what would lifehave looked like if it evolved under radically different physicalconditions?", "what is the logical form of all living systems?", or"what is the simplest possible living system?"The term "artificial life", often shortened to "alife" or "A-Life",was coined in the late 1980s by researcher Christopher Langton,who defined it as "the study of artificial systems that exhibitbehavior characteristic of natural living systems.
It is the questto explain life in any of its possible manifestations, withoutrestriction to the particular examples that have evolved on257earth... the ultimate goal is to extract the logical form of livingsystems."Probably the first person to actively study and write on topicsrelated to A-Life was the noted mathematician John VonNeumann, who was also an early figure in the field of gametheory. In the middle of the 20th century, Von Neumanndelivered a paper entitled "The General and Logical Theory ofAutomata," in which he discussed the concept of a machine thatfollows simple rules and reacts to information in itsenvironment.
Von Neumann proposed that living organismsare just such machines. He also studied the concept of machineself-replication, and conceived the idea that a self-replicatingmachine, or organism, must contain within itself a list ofinstructions for producing a copy of itself. This was severalyears before James Watson and Francis Crick, with the help ofRosalind Franklin and Maurice Wilkins, discovered thestructure of DNA.The field was expanded by the development of cellularautomata as typified in John Conway’s Game of Life in the1970s, which demonstrated how simple components interactingaccording to a few specific rules could generate complexemergent patterns.
This principle is used to model the flockingbehavior of simulated birds, called “boids”.The development of genetic algorithms by John Holland addedselection and evolution to the act of reproduction. Thisapproach typically involves the setting up of numerous smallprograms with slightly varying code, and having them attempta task such as sorting data or recognizing patterns. Thoseprograms that prove most “fit” at accomplishing the task areallowed to survive and reproduce. In the act of reproduction,biological mechanisms such as genetic mutation and crossoverare allowed to intervene.
A rather similar approach is found inthe neural network, where those nodes that succeed better atthe task are given greater “weight” in creating a compositesolution to the problem.258A more challenging but interesting approach to AL is to createactual robotic “organisms” that navigate in the physical ratherthan the virtual world. Roboticist Hans Moravec of the StanfordAI Laboratory and other researchers have built robots that candeal with unexpected obstacles by improvisation, much aspeople do, thanks to layers of software that process perceptions,fit them to a model of the world, and make plans based ongoals. But such robots, built as full-blown designs, share few ofthe characteristics of artificial life. As with AI, the bottom-upapproach offers a different strategy that has been called “fast,cheap, and out of control”—the production of numerous small,simple, insectlike robots that have only simple behaviors, butare potentially capable of interacting in surprising ways.
If ameaningful genetic and reproductive mechanism can beincluded in such robots, the result would be much closer to trueartificial life.Artificial life is still a very new discipline, having been foundedonly in the late 1980s, and is still very much underdevelopment. Like other new fields, it has been the subject ofsome criticism. Based on its abstract nature, artificial life hastaken time to be understood and accepted by the mainstream;papers on the topic have only recently been put into prominentscientific publications like Nature and Science. As with any newdiscipline, researchers need time to select the most fruitfulresearch paths and translate their findings into terms otherscientists and laypeople can understand and appreciate.
Thefield of artificial life is one that seems poised to grow as the costof computing power continues to drop.Artificial life may be labeled software, hardware, or wetware,depending on the type of media researchers work with.Software artificial life is rooted in computer science andrepresents the idea that life is characterized by form, or forms oforganization, rather than by its constituent material. Thus, "life"may be realized in some form (or media) other than carbon259chemistry, such as in a computer's central processing unit, or ina network of computers, or as computer viruses spreadingthrough the Internet.
One can build a virtual ecosystem and letsmall component programs represent species of prey andpredator organisms competing or cooperating for resources likefood.The difference between this type of artificial life and ordinaryscientific use of computer simulations is that, with the latter, theresearcher attempts to create a model of a real biological system(e.g., fish populations of the Atlantic Ocean) and to base thedescription upon real data and established biological principles.The researcher tries to validate the model to make sure that itrepresents aspects of the real world. Conversely, an artificial lifemodel represents biology in a more abstract sense; it is not areal system, but a virtual one, constructed for a specificpurpose, such as investigating the efficiency of an evolutionaryprocess of a Lamarckian type (based upon the inheritance ofacquired characters) as opposed to Darwinian evolution (basedupon natural selection among randomly produced variants).Such a biological system may not exist anywhere in the realuniverse.
As Langton emphasized, artificial life investigates"the biology of the possible" to remedy one of the inadequaciesof traditional biology, which is bound to investigate how lifeactually evolved on Earth, but cannot describe the bordersbetween possible and impossible forms of biological processes.For example, an artificial life system might be used todetermine whether it is only by historical accident thatorganisms on Earth have the universal genetic code that theyhave, or whether the code could have been different.It has been much debated whether virtual life in computers isnothing but a model on a higher level of abstraction, or whetherit is a form of genuine life, as some artificial life researchersmaintain.
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