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Choosing GPU for mining is now easier than ever. Clear linking rules are abided to meet reference reputability standards. Only authoritative sources like academic associations or journals are used for research references while creating the content. If there's a disagreement of interest behind a referenced study, the reader must always be informed. Hello and welcome to this guide on choosing the best GPU for mining.

Minecraft landmine mod 1-3 2-4 betting system packers vs redskins betting line

Minecraft landmine mod 1-3 2-4 betting system

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What makes the landmines great is that you can craft them to look like nearly any block on the surface. But just below that layer is the full TNT block. Hide your mines under cobblestone, wool or even the shoreline. Most of the blocks are available to disguise your explosives and so long as you know where you placed the mines you should be in for a treat with this mod.

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Zoom kitten blooper. TikTok sale. PS5 restock. That trap is the landmine. It's infamous for being the "noob's trap," but in some situations, they prove to be effective. We've been helping billions of people around the world continue to learn, adapt, grow, and thrive for over a decade. Every dollar contributed enables us to keep providing high-quality how-to help to people like you. Please consider supporting our work with a contribution to wikiHow.

Log in Social login does not work in incognito and private browsers. Please log in with your username or email to continue. No account yet? Create an account. Edit this Article. We use cookies to make wikiHow great. By using our site, you agree to our cookie policy. Cookie Settings. Learn why people trust wikiHow. Download Article Explore this Article Steps. Tips and Warnings. Related Articles. Author Info Last Updated: November 17, Dig where you want to place the mine 2 blocks.

It is a hole that is a block in area and 2 blocks deep. Place TNT on the bottom of the hole. If you want to, you can put more TNT to the side of it. This makes a bigger blast radius and a better chance to make a kill in case the player tries to run away. If you want to make it even harder to someone to escape, make the hole 5 blocks deep.

That way, when the TNT is about to explode, it falls, making the block above it fall and trapping the player inside. Place a block on top of the TNT that matches the surface's background. Put a pressure plate where you put the block. This will ignite the TNT. If you can, use a pressure plate that matches the block or its color. This helps conceal the mine.

It may not work all the time, but mobs and hesitant players may fall for it! You have made a mine. Now make more for a mine field. If you do make a minefield, try to keep them close to each other. This makes it more difficult for other players to get through, though it might not be so economical a waste of resources. Wait for animals, mobs, and players to die in the mine.

The mines could cause a chain reaction if the explosion touches other mines. Some players won't die unless they are new in Minecraft. If you plan on exploding other players, especially ones with strong armor, always double-check on the explosion site. That way, you can get their items or kill them off if they still survive.

FLEXSLIDER 2 OPTIONS BINARY

Instantly share code, notes, and snippets. Code Revisions 1 Stars 3 Forks 2. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. A: [null, "qQ", "wW", "sS", "zZ", null],. B: ["vV", "gG", "hH", "nN", null, null],. C: ["xX", "dD", "fF", "vV", null, null],. L: ["kK", "oO", "pP", ";:", ". N: ["bB", "hH", "jJ", "mM", null, null],.

O: ["iI", "9 ", "0 ", "pP", "lL", "kK"],. Q: [null, "1! V: ["cC", "fF", "gG", "bB", null, null],. Shown here is Turtlebot 2, which has a Yujin Kobuki base, a Kinect sensor and a laptop with a dual core processor and is available as open source hardware with a BSD license. There will always be a need for quality simulators in robotics as every experiment cannot be performed with real-world robots due to extreme logistics and cost considerations. Currently, Gazebo which is a standalone project and also a part of ROS is one of the best and is often accepted as a standard for robotics simulations.

It has two 7-DOF arms and is equipped with a 5-megapixel camera, a tilting laser range finder, and an inertial measurement unit and is powered by two 8-core servers, each of which has 24 gigabytes of RAM, for a total of 48 gigabytes, and are located at the base of the robot. The PR2 can open doors, fold towels, fetch beer and also play billiards. AIBO and similar designs such as the Tekno and the CHiP are anthropomorphised on a dog and often create a false bond of caring and compassion with human beings.

In context, the PARO which is a robotic seal and the Pleo which is a robotic dinosaur are state of the art pet robots and both have a congenial appearance and respond positively to touch and cuddles. The PARO has found application in psychological therapy of the depressed and the elderly and the AIBO has been used in nursing homes as a company for the residents. The image is from commons. LEGO Mindstorms kit is meant for educational robotics.

This affordable kit provides for easy assembling of custom-designed robots and for programming them with user friendly languages. Designed as a walking assistant robot, the ASIMO stands cm, weighs about 50 kgs, can attain maximum speeds of 9 km per hour in running mode and can be controlled by PC, wireless controller, or voice commands.

Nao This iconic mini humanoid was made by the French company, Aldebaran Robotics in as an open source software. Standing at 58 cm and weighing 4. The robot has quite a reputation: it has acted in a ballet, been the most prolific robot in robo soccer, has been employed as a therapeutic robot for children suffering from autism, has been programmed to be the first robot with deontic ethics and also, to a degree it is the first robot to attain sentience, albeit for a very short time.

Segway This battery-powered, self-balancing two-wheeled scooter was developed at the University of Plymouth in collaboration with BAE Systems and Sumitomo Precision Products, and uses gyroscopic sensors and an accelerometer for maintaining balance. While not a robot as per the traditional definitions, the Segway is an early ancestor of autonomous vehicles of the immediate present and the promise of the future.

At a maximum speed of about 12 miles per hour, it is ideal for the golf course and other short tours and it has also been designed into a companion robot []. Robots inspire awe across all ages. Engaging in a conversation with a social robot such as Kismet, whom we will meet again in Chapter 7, or making a robot using LEGO Mindstorms. Both these images are of robots built with the NXT kit. Both images courtesy commons. In academia, readily available open source simulators and software for robot has fueled robotics research in universities and hobbyists, such as MORSE [] shown in Figure 1.

On the other hand we have the the prophecy of singularity and a Terminator-like doomsday scenario where intelligent machines take over the earth, which is a script for various science fiction potboilers. In this section, I look into the fiction, movies and popular opinions on robots, utilities of mobile robots across various domains such as medicine, army, public interface, entertainment etc. In , Asimov ushered in added sophistication.

Arthur C. Clark, Alvin Toffler and Philip K. Dick are near contemporaries of Asimov who gave us timeless classics with robotic characters. With the advent of cinematography, robots came to be synonymous with depiction of futuristic themes in movies. She was a metal cast female android. Robots have added to the intrigue, drama, humour and fun in cinemas. Over these 90 odd years as shown in Figure 1.

Robbie in Forbidden Planet as shown in Figure 1. Fiction with robots often traces some of the popular storylines, one where robots accept human norms and values and try fitting into human society, but are often reminded of their shortcomings. The second is the dystopian scenario where robots and smart AI rebel against human beings and has been the plot for Blade Runner, Terminator and Battle Star Galactica. A third brings to light the the endearing attachment between human and the robot, as seen in, Frank and the Robot and Real Steel.

Robots are mostly shown to be near perfect artificial beings but with lesser appreciation for human emotions and humanly values though Replicants in Blade Runner, Lieutenant Commander Data in Star Trek and Cylons in Battle Star Galactica can be said to be near humans and special psychological tests similar to the Turing test had to be devised in order to distinguish between human and the artificial.

In Chapter 8, it will be discussed that ethical principles for robots cannot be drawn from these three laws. Asimov made some amends to these laws in by introducing a fourth law, the zeroeth law. The chatbots converse using materials from a script which has been taken from a famous debate on human nature between Noam Chomsky and Michel Foucault in Bold and unconventional explorations to use robots in-sync with live human actors and dancers on stage are few but commendable and may be the definition of a newer realm of acting and stage performance.

Made in Weimar Germany, the movie was a science fiction tale set in a dystopian future. Shown here is a commemorative statue of the robot at the Filmpark, outside the Babelsberg studio where the movie was made. Robbie was designed to be a 6 foot 11 inches tall humanoid robot which could turn, twist and move around on wheeled legs.

C3PO along with its companion R2D2 have become household names and attained iconic status. His 3 laws of robotics have attained cult status among science fiction fans and roboticists alike. First formulated in , Asimov later added the zeroeth law in Though popular, the laws have been found to conflict with one another and are lacking in practical applications, Academic opinion and research into robot ethics has been more inclined towards a developmental approach than a codification of laws.

Other than the movie and entertainment industry, mobile robotics is the heart and soul of space research. It is being extensively used in the military and medical surgery and it also bears promise for modern agriculture. A great many modern-day space programmes have a robotic rover, which is unmanned and attempts to chart an unknown heavenly body.

They are usually equipped with both modes of control viz. In the military, use of robots has been hotly debated as trusting a robot with lethal capabilities may prove to be fatal. Unmanned robotic vehicles have been used in times of crisis such as earthquakes, landslides and tsunamis. In medical science, teleoperated robotic surgery units such as the Da-Vinci systems, shown in Figure 1.

The Vox Populii gathered over questionnaires and polls shows how people have responded to robots and their influence on their lives. In a user poll in Plano, Texas, conducted. Teleoperation in advanced medical systems has allowed for surgeries where the surgeon and the patient are on different continents.

His Star Wars companion C3P0 was ranked fourth. In context of human approval and level of comfort with an ensuing human-robot interaction, an online poll by robohub exploring the tradeoff between privacy vs. Bathing robots for the elderly have been considered as a commercial venture by electronics giant Sanyo. Various of these polls dig into dystopian scenarios such as robots taking over human jobs, the Skynet apocalypse, human-AI war etc.

An online poll by The Telegraph in October yielded mixed response as is shown in Figure 1. The threat that robots will take over human jobs is a neo-Luddite nightmare, though it is not baseless. The other side to this debate suggests that advancing technology will lead to a new way of living which will develop newer forms of sentient intelligence. Robots have had effective human interactions as artificial pets, in the role of carer, romantic attachment and lover, organised tour guide, receptionist etc.

Human-robot interaction and robot ethics are avenues of research which find application in the near future. More on these topics is discussed in Chapter Robotics had its history in automata, where smart mechanical manoeuvres were employed for locomotion. Da Vinci in 15th century Italy and Vaucanson in 17th century France are the most well-known accounts.

Tesla developed a radio-controlled boat at the turn of the century. With revolution in microelectronics and theoretical developments in AI, Shakey was the big leap that mobile robotics achieved around the late s. Robots have had immense social impact via movies, educational tools, science fiction and household appliances.

Existential risk from robots is a question being asked more often than ever, and is closely connected with the prophecy of Technological Singularity. NOTES 1. The etymology was coined by Austrian scientist, Manfred Clynes in the s to suggest the need for using AI to enhance human biologial functions to survive in hostile and unfavourable environments.

Flakey was a successor to Shakey. Developed in the mids, it could be controlled using fuzzy logic. Both the Pioneer robot and Erratic robot are motivated from Flakey. Large and the small. The largest robot measuring 8. It has been a chief attraction at Longquan temple in Beiijing since Simulators to study robot navigation is an essential tool in an academic course for robotics.

Nowadays, meta-software platforms as ROS or MOOSE are preferred than standalone simulators, however simple and easy to install softwares have not lost their appeal. Turtles vs. The study should also address the evolution of the technology in the 20 years between these two projects, and also attempt to see the projects from the aims they were designed for. Walter attempted to build models for human cognitive capabilities while Shakey was designed for mobile locomotion.

These two projects had contrasting outcomes, it was very fruitful for the turtles, but it was slow and cumbersome for Shakey. These hypothetical organisms were capable of long-term self sustainment while attending to multiple goals and provided the earliest model for an artificial autonomous agency.

This chapter explores how AI has been used to design robots, the inspirations from the natural world, the contributions from various disciplines and the shortcomings of traditional AI. What constitutes a robot, and how is it different from a machine? In contrast to industrial manipulators, AI robots are meant for navigation and exploration of their local environment, with demonstrable intelligence and many times are meant for a particular task or role, viz.

Therefore 1 robots performing repetitive jobs such as industrial robots and arm maniplators, 2 those which lack in a very clear mandate such as the Martian rovers, 3 those with human appearance as automatons or humanoid robots suitable to the domain of social robotics and 4 futuristic robots made by extending biology, such as androids and cyborgs, are all covered by this definition.

The definition is not limited to the popularly accepted mechatronic design for robots which marries the mechanical to the electronic with a processing unit. However, an engineering point of view tends to constrain the definition and a robot is to have electronics, mechanical hardware and a processing unit. Particular goals for the robot may either be specific, such as line following, light tracking or picking up empty coke cans, or a number of chores which converge towards a predetermined profile such as a military robot, a nurse robot, a domestic help robot or an office assistant robot are discussed in Chapters 7 and 8.

Implicitly it also suggests that autonomous functioning overlaps with intelligent behaviour. As a working definition as per the scope of this chapter, a robot is an autonomous or semi-autonomous agency that undertakes jobs under direct human control, or it is partially autonomous but supervised and groomed by human supervision, or completely autonomously. In later chapters, one will find that this definition is not enough and as we progress towards newer realms of agent-based robotics, the definition will need to be modified.

Early ideas of Artificial Intelligence were suggested by Alan Turing in the late s with hypothetical models which he called the automatic machine, which was later named the Turing machine. This was the bare bones of a central processing unit and helped to design computers in the post-war era. These early concepts were made into a fledgling discipline by the pioneering effort of McCarthy, Minsky, Newell and Simon.

Artificial Intelligence can be partitioned into the following seven subdivisions []. Knowledge representation. How does the robot represent the world? In human. A robot does it using lasers or sonar, a table in the real world will reduce to an array of numbers corresponding to the intensity as perceived by the sensor. Since the onboard microprocessor if not very powerful, these methods approximate the dimensions and reduce objects to assortment of cubes, cuboids etc.

Natural language. Language is unique since such unification of syntactic and semantic structures exists only for human beings and not in animals and is the definitive underpinning of our cultural and social systems. Noted linguist Noam Chomsky opines that language is at the interface of the two prominent cognitive processes: it is sensorimotor for externalisation and more intentional and deliberative for conceptual mental processing. To make robots understand and respond to human voice comes into play only in designing and developing more sophisticated robots which can closely interact with human society.

Natural language processing libraries and chatbots have been very promising and are discussed in Chapter 7. Voice based systems are still being explored into and Siri from Apple, Cortana from Microsoft and Google Now are promising results. Robots are programmed with a number of task specific manoeuvre, but these are not exhaustive and to perform efficiently it must learn from experience.

Popular learning paradigms are cased based approach, artificial neural network, fuzzy logic and evolutionary methods. Nearly all state-of-the-art robot have a learning module. Planning and problem solving. Making plans or algorithmic steps to accomplish a goal and solve the problems encountered in this process is inherent to AI agents, and is often a mark of their performance.

For simple robots, planning is largely motion planning. Inference is to develop a conclusion from incomplete or inaccurate data sets. A robot often encounters inaccurate data from the sensors. In order to encounter this and prevent complete system shutdown the robot has to rely on inference, and ensures continuance of the processes.

Search for a robot usually means a search in the physical space — searching for an object or a goal point, but it can also mean a heuristic search where the robot is searching out solutions in an analytical manner. Vision has become an integral part of robotics. For human beings, vision is unique compared to the other senses and is the trigger for most of our motor actions, the same is true for most of the animal world, so efforts to invent models of intelligence which can manipulate its local environment will have to address vision.

Psychologists contend that vision enables our inner world, and nearly every consequence of our actions is simulated in our inner world prior to acting it out in the real world. Vision has had an important place in AI since early days with the pioneering research of Gibson [] and later Marr [].

Russell and Norvig qualify an agent as an abstract entity which perceives its environment using sensors and acts upon that environment through effectors. Autonomy loosely would mean that no other entity is required to feed its input nor is any required to keep it running. The robots can sense and act to fulfill given and implied goals in a dynamic environment, and they can go on working without any external intervention for substantially long periods of time. This line of thought leads to a classification as suggested by Luck et al.

Every agent is situated, and is a part of the world. It can interact with the world and change the world and also its own perceptions. Caution should be taken to distinguish software agent from just any program. As an illustration, a program which prints a line of text is not an agent, because it works on an input from a user. This output would not effect later programs that are run and it runs once and stops lacking temporal continuity.

While characters in a computer game, viz. It is rewarding to have a quick jog through the various types of definitions that has been suggested for agents. Simon had concluded that in a treacherous and life threatening terrain, a randomly selected path will greatly jeopardise the chance of survival, however there exist hints in the environment which an organism must exploit for survival.

Toda used similar ideas and also equipped the artificial animal with multiple sensors, early ideas of incremental learning and adaptability. Behavioural economics approach, dynamical systems approach and evolutionary approach are some of the design principles probed in the early s, these developments were instrumental in the ANIMAT approach and interconnected the principles of anthropomorphism, autonomy and sensory-motor for the development process.

These artificial creatures were supposed to have the means of collection, locomotion and ability for decision making by virtue of information perceived from the environment. It also incorporated game theory and behavioural psychology to develop the fungus eater as an asymptote for human behaviour. The source of replenishment for these creatures is a type of fungus that is typical to that planet.

These fungus eaters took instructions from the base command and there was no communication among themselves. The fungus eaters would roam around collecting uranium, unloading it in marked containers until they became inactive due to lack of fungi starvation or accident. The fungus eaters are designed as wheeled humanoids, Toda [, ] suggests that the bodily form should be determined after the study of the terrain, gravity, climatic conditions, humidity, temperature, topography etc.

Since the primary job of the fungus eaters is to navigate the terrain and collect uranium and fungus at Taros which is a flat topology, this merits a wheeled model rather than legs. Toda develops detailed designs for the visual sensors or eyes, which should be at the top of the body for maximum visibility. Height of the fungus eaters should be optimal, too tall may not be cost effective and might be unstable, while too short will hinder the process of surveying the terrain effectively.

Addition of an olfactory sensor to smell out the fungus is also recommended. Toda further suggests that f will improve with experience, suggesting learning and memorising techniques. Toda develops stochastic models of the above choice program and melds path planning with the physiological constraints of the fungus eaters. After the modifications, the fungus eater is supposed to have three sensors at its disposal: geiger counter for uranium, olfaction for finding fungus and visual sensors for navigation.

Toda suggests development of programs for coordination between these 3 streams of incoming information. These are early concepts of sensor integration. Much like Walter, Toda also believed that this approach will lead to models of human behaviour and cognition. Therefore, the fungus eater model did not consider emotions and motivations.

After about two decades, first Braitenberg and then Pfeifer designed models of emotion, viz. That does raise question: does autonomy, emotions and a value system mean intelligence? Is fungus eater and later its improvements intelligent? The answer would lead us to look into the typical characteristics of embodied cognition []. Cognition is situated. Unlike the generic notion of cognition, embodied cognition is a situated activity. The agent interacts with a real-world environment, and cognition happens due to perception-action pairings.

Situated cognition involves a continuous process, where perceptual information continues to flow through the sensors which leads to motor action which in turn changes the environment in task-relevant ways. Walking, tightening a screw, switching on a light bulb are some examples of situated cognition.

Creative thought processes such as writing a letter or scripting musical notes are examples. Cognition is time-pressured. Since, embodied cognition is situated, it requires real-time response from the environment. Behaviour-based approach is a remedy to such bottlenecks, and proceeds by generating situation-appropriate action on the fly by considering real-time situated action as the basis for cognitive activity, which appreciably lessens the time pressure.

However, such models of situated cognition cannot be scaled up and therefore never lead to a model for human cognition. The agency off-loads cognitive work onto the environment. Situated agency attempts to use the environment in strategic ways, by manipulating the environment to attend to the job at hand, rather than fully shaping up the system response to the concerned behaviour.

For example, navigation with a compass exploits the magnetic alignment of the planet to enable finding the right direction. Similarly for the task of assembly, the pieces are arranged or used nearly in the order and spatial relationships of the desired finished product.

This off-loading happens because there is usually a limit on the information processing, physical limits on attention and a limited working memory available to the agent. Concepts of psychology and behaviourism confirm this facet, as is seen later in the chapter.

The environment is part of the cognitive system. Cognition is not an activity of the mind but is distributed across the agency and the situation as they interact and is the result of continuous agent-environment interactions. Therefore, the situation and the situated agency are a single system. Similar ideas have been expressed by Uxekull, from a biological point of view, as will be discussed later. Cognition is for action.

Unlike cognition as per traditional AI, embodied cognition is always action oriented. Perception is dynamic, real time and occurs in tandem with motor action. Off-line cognition is body-based. When not situated in the environment, in the decoupled agency, cognitive processes are driven by mental structures which are similar to simulations of sensory processing and motor control.

The concept of the inner word is discussed in detail later in the context of conscious agency. Therefore, the fungus eater can be said to be the most fundamental cognitive behaviour. However this model is incomplete in regards to the psychological state of such an agency and cognitive processing achieved is very low. Biological evolution has witnessed growth from the purely reactive, in single-celled organisms, to cognitive minds as in human beings; therefore our ascent has been from creatures with primary skills of perceptual sensing and motoring, and whose cognition consisted more of immediate, on-line interaction with the environment than cerebral activities.

Therefore, from this argument, sensory-motor processing is a ground level intelligent behaviour. The fungus eater brings to light a set of design principles and guidelines for developing artificial autonomous AI agents []: 1. Embodied AI agents are: a Autonomous; capable of executing required tasks in the real world without human interventions b Self sufficient; they can sustain themselves over extended periods of time, c Embodied; they must be designed as dynamical systems, in the real world.

Ecological niche. The first principle makes sense only once the agent is in ecological niche with the world. AI agents typically lack in universality and the versatality seen in human intelligence, and are often designed for a particular niche. The execution of a behaviour is done in the real world by the capabilities afforded by the agent world interactions.

Similarly, flocking algorithms in multi robot systems are designed considering a sufficiently large flock, and are found to be ineffective with low number of robots. A niche can be seen as relations which strongly tie the agent to the local environment. The concept of a niche is in contrast to classical AI, and instead of computer programs coded by a programmer, intelligence is defined as the interworking of the agent and the local environment. Parallel processes. Observations from biology confirm that cognition happens as an overlap of a number of parallel processes.

These processes run parallel to each other, loosely coupled, asynchronous and they require little or no centralised control. The lack of a centralised control reduces performance to a mere reflexive motion. It has been the subject of debate whether higher level, human-like cognition can truly be developed with the lack of centralised control3.

For human beings, our cognitive abilities are an overlap of the five senses conjoined with memory and reasoning from our brain and clearly we engage in various activities which are more diverse than those of insects, rats etc. However, as I will discuss later this principle has been very successful and led to the development of behaviour-based approach in designing mobile robots.

Chapter 9, this contention will be discussed further in the perspective of artificial consciousness. Implicit values can be developed with supervised learning or by tailoring the sensor response. Sensory-motor coordination.

Primitive levels of cognition as locomotion, suggested in the previous two principles would be manifested using a sensory-motor model. This would mean classification, perception and memory should be viewed as sensory-motor coordinations rather relying on reactive performance than as individual modules. Ecological balance. For every artificial agent to perform optimally, there needs to be a sync between the sensor, processing and actuation.

As an example, a robot which is equipped with a sophisticated motion sensor cannot have low processing power such as primitive PIC microprocessor. Such will add to lag time and bottlenecking, and will deter its performance. Good design, for embodied agents exploits agent world interaction and the ecological niche.

Consider a robot which is tasked to go around a square of 5 meters in an anticlockwise trajectory. The first method will incur error due to friction and other dissipative forces and will start to give wrong trajectories after a few runs, but the second solution to the problem is strongly tied to the local environment and will always give good trajectories, until of course the on-board batteries run down.

This principle is more useful than is apparent, particularly in swarm robotics, which will be discussed in Chapter 6. The next section discusses how motivations from the natural world have shaped agent-based robotics. It was seen that, making an artificial agency play a game of checkers or respond to an IQ test is much easier than replicating the perception and motor skills of a one-year-old baby.

What is the number under the parked car? To solve this rather simple problem one has to turn the image top side down, thus using a human ability, while an algorithmic route fails. Image of the car, CC image at pixbay. This paradox is due to our attempts to recreate biologically intelligent entities via programming routes. The rules for biological systems such as human cognition has evolved through interactions with the environment over billions of years of evolution.

We still do not know how most of these processes work and we resort to approximate mathematical models to replicate them in the artificial domain. Some researchers do point out that the bone of contention is that AI researchers, philosophers and psychologists have yet to converge on how we define intelligence. However, a comparison of a human brain to an artificial intelligence tells us some of the story.

In the human brain, connecting neurons store memories, distributing information and generating thoughts through sensations received broadly from the outside world and often related to previous memory and stimulus response acquired over years of learning; thus providing different responses to any given situation.

Our perception of the outer world is with our sensory organs — which contributes to our mental states. The CPU on the other hand is structured on digital logic, following algorithms. Simple facets of human perception cannot often be tackled with a computational basis and such starts at the very nascent level as a one-year-old baby.

On the other hand, a game of checkers or an IQ test are problems which can be pursued with an algorithmic approach, the rules for which are readily known and can be programmed into an AI agent. Robots are often motivated from mother nature. The representation produced by a unique receptor is marked to a feature sign. The effect net produces muscle impulse patterns and stimulates effector cells to produce effector sign.

This cycle is incremental and enriched with 4 Uexkull. Identifying features in an organisms UMWELT to suit its well being develops with gradual experience, as it explores more of its world. The soap bubble analogy is most appreciable in lower animals limited by a few number of senses, and is not very visible for higher animals and human beings.

These ideas conveyed that the mind and the world are inseparable, because it is the mind that interprets the world for the organism. The female tick is oblivious to most of the things that we human beings find interesting. The life of a tick is concerned with finding a warm-blooded mammal to feed on its blood and to lay its eggs and then die.

The tick is both deaf and blind but has a photosensitive skin and after mating it is guided by the sun to the highest point on a blade of grass or the top of a branch, until its prey, the mammal, comes along. The tick is able to recognise its prey by the smell of its sweat butyric acid from sebaceous follicles typical of all mammals, and then the tick falls towards its prey. Once on the mammal, its next job is to find a warm, hairless spot to feed on, nest and lay eggs.

Fighting fish: Fighting fishes do not recognise their own reflection unless at a minimum of 30 times per second [35]. These fishes prey on fast moving fishes and other sea creatures. Their motor processes are at. The phenomenology is unique to the creature, and helps it to perceive its world. The bat is also a topic of interesting discussion in artificial consciousness, as will be seen in chapter 9.

Bee: It has been observed that bees have a preference for alighting on objects which have broken shapes such as stars and crosses, and are seen to avoid compact forms such as circles and squares. A bee has the primary job of collecting nectar from blossoming flowers. It is worth noting that the bee is probably one of the lowest creatures that has awareness of shape and form, while lower creatures such as the paramecium, mollusk, earthworm, tick, etc.

All higher creatures, such as animals and human beings have an appreciation for shape, form and direction, which shows in their inner worlds. This approach has helped to develop the concept of embodiment and ecology in embedded AI, as will be discussed in the rest of the chapter. Visual perception has a special place in AI. It is usually the strongest of all human perceptions, and forms the most persistent memories.

Vision comprises both sensation, as a reactive stimuli to see and also as a deliberative perception, relating objects and events in the environment to watch. Various theories for vision have been forwarded over. Therefore in traditional view, vision actively constructs our perception of reality based on our environment, known representations and memory, and is more or less silent on dynamics of the environment or the agency.

Since vision cannot be readily manipulated as a sensory-motor pair, researchers have considered other routes to model it and, while image processing is often a favoured tool and is used in robotics research, alternatively simpler and inexpensive robots usually employ non-visual sensors such as sonar and infrared, which provide metric distance information, supplementing in low-level tasks and navigation-centric behaviour.

Gibson [] in the s and Marr [] in the s provided the impetus towards modelling vision as an ecological phenomenon. Gibson suggested that vision is not merely limited to such cognitive processes but often the mechanism to orchestrate motion.

Gibson was critical of both behaviourism and internal representations and developed the concept of optical flow. Similarly Marr rejected image processing and considered vision as information flow than interlinking of isolated standalone phenomenon. Where the environment should not be modelled as a coordinate frame but rather in ecological aspects; medium, substance and surfaces etc. Vision is modelled on the optic array, which is formed of all the rays converging on a given point.

The optic array is different at each point, so for an observer in motion the array changes continuously creating an optical information flow field. The transformations in the optic array sampled by a moving observer simultaneously specify the path of locomotion, rather than the more traditional coordinate frame for start point and end point etc.

The Optic flow contains information about both the layout of the surface and the motion of the agency. For example, 1 surfaces of a certain height, size and inclination afford sitting on by humans, those of a different height and size afford stepping up on and 2 objects moving at a certain speed afford catching.

Others are too fast or too slow etc. It is to be noted that these actions are ubiquitous reactions from human psychology and not learned with experience etc. Perception of these possibilities for motion are essential, and they are contained in the optic array. To start locomotion is to contract muscles so as to allow the forward optic array to flow outward; to stop locomotion is to make this flow stop. Where Finternal is the internal force and f low is the optic flow. Utilising such control laws and extending ecological psychology into robotics has seen promising results in robots interacting in a real-time dynamic environment with obstacles and human beings.

Such a radical theory of vision clearly lacks in quantifying optic flow or internal force, as it is dependent on the context and the agency etc. However, it does hint towards ideas that locomotion is not rote Newtonian mechanics, but is driven by perception which is triggered.

Depending on the relationship between perception and action, the agent is tasked with mapping available information to the control, to realise a desired state for the system 4. The environment provides information and hints to encourage adaptive behaviour 5. Since the agent is a part of the environment, no a priori or real-time 3D map or model is needed Duchon demonstrated robot navigation and obstacle avoidance using the above principles.

Therefore, the environment that is experienced by the agent is not only conditioned by its own agency, but is enacted-in, in such a way that it emerges through the bodily activities of the agent. The experienced world is portrayed and determined by mutual interactions between the physiology of the agent, its sensorimotor circuit and the environment5 as is shown in Figure 2. Therefore, the agent makes its agency in direct interaction with itself and its environment.

Symbolic computation and the informational model is not the essence of cognition, neither can external events dictate the cognitive process. Cognition is contextual, and never happens in abstraction, and it is the adaptive coordination and control of actions achieved by the overlap of embodied and situated cognition.

Lastly, Experience is important to the understanding of cognition and the mind. This approach emphasises the phenomenal character of vision rather than its more traditionally held representational nature. For example, since vision is enabled as sampling of a two-dimensional projection of three-dimensional space the top view of a 2D square and a 3D cube may appear to be the same, but a slight movement towards or away from the object, leads to expansion or contracting of the amount of light entering the retina and therefore the eye will perceive it differently.

Another example is, since each colour dictates the amount of light reflected, each colour patch corresponds to a unique contingency, and thus often conveys psychological meaning, viz. Hence, red is associated with excitation, warmth etc. The coupling relations change with adaptivity dotted arrows. Adapted from Froese and Di Paolo []. As we have seen, vision is strongly tied to action and it is arguably the most important sensing capability of enactive agency.

Behaviourism found great favour between s to Early pioneer were Pavlov, Twitmyer and Thorndike, all working independently. After several such trials, it was observed that the dogs began to salivate after hearing the metronome. The metronome had acquired the property of stimulating salivary secretion. Similar results were reported by Twitmyer.

This modification of an animal behaviour where a biological stimulus is paired with a previously neutral stimulus such as sound or light etc. In the s, Skinner developed operant conditioning, which relied on modifying behaviour by its consequence, either by reinforcement or punishment and not by manipulating a reflex of the Pavlovian conditioning.

Typical Skinner box experiments on rats, as shown in Figure 2. Over time a stimuli worked as. One of the earliest experiments in behaviourism was conducted by Pavlov, where he studied digestion in dogs for his theory of conditioned reflex. The five types of operant conditioning are shown in Figure 2.

Reinforcement can happen in two ways, in positive reinforcement a response is followed by a reward such as, viz. Punishment also has two modes, in positive punishment a response is followed by something unpleasant, and in negative punishment the response removes something pleasant. Both scenarios discourage the response. It is not always easy to discern between punishment and negative reinforcement. Usually, punishment is characterised by modulating fear and engages an aggressive response, and punishment is suppression and the response is seen once when punishment is removed over a long period of time.

In extinction, a previously reinforced response is no longer reinforced with either positive or negative reinforcement, and as a result of not experiencing an expected outcome weakens the response. Skinner believed that operant conditioning can be used to design in an organism extremely complex and rich behaviour.

Behaviourism has directly influenced agent-based robotics, the takeaways are: 1. Behaviourism is primarily concerned with observable behaviour, as opposed to internal events like thinking and emotion. Observable i. The box is a glass-lined enclosure that contains a key, or a bar or a lever that an animal can press in response to a specific stimuli, such as a light or sound signal, which will then release food or water as reinforcement.

Internal events, such as thinking, should be explained through behavioural terms — or eliminated altogether. There is little difference between the learning that takes place in humans and that in other animals. Therefore research can be carried out on animals as well as humans. Behaviour is the result of response to stimulus. Thus, all behaviour, no matter how complex, can be reduced to simple stimulus response models.

All behaviour is learned from the environment. New behaviours are learned through classical or operant conditioning. After Toda, similar models of artificial animals were suggested by various others: Braitenberg, Holland and Brooker and Wilson [, ], who coined the term in the mid s. In particular included paradigms such as, rule-adaptiveness, genetic evolution, emergence and association.

The artificial animal exists in a sea of sensory signals, but at any given instance only some of the signals are significant to motor action while the rest are redundant. The artificial animal is capable of action, which in effect tends to change these signals. The artificial animal acts externally and also through internal operations, so as to best optimise the occurrence of the special signals.

While the first two principles are about the concepts of sensory motor and embodiment, the third enshrines survivability as the most fundamental behaviour and the fourth incorporates conditioned response and rule adaptiveness. Mirroring an animal allows for the exvivo inception [] of behaviour without extraneous influences; thus it allows a scope to engineer these behaviours with precision, flexibility and efficiency in a concerned context that may never be observed in studies with real animals, as will be illustrated with examples in later sections.

A poetic adaptation of the biblical tale of genesis is shown in Figure 2. ANIMAT has been instrumental in probing the natural world to exploit designs from mother nature, to work in tandem with known mathematical models and technology. However, there is no single route to design ANIMATs, and researchers have used various methods to bring their artificial animals to life.

The brachiating robot controller as shown in Figure 2. Nakanishi et. In complete contrast is the gastrobot [] and later the ECOBOT series of robots as its mature avatars, which are designed on the digestive process and the gastrointestinal system in human beings and are aimed to attain energetic autonomy using a microbial fuel cell MFC. In time they develop free will and moral values. Courtesy Antony Beavers, University of Evansville, used with permission.

Image on the right courtesy wikipedia. Research has always tried to bring in newer ideas from mother nature to AI, as opportunism [11] and inner-world [] which are discussed in the later chapters. One of the most interesting and sophisticated ANIMAT research involves neuromorphic brain-based robots, which are modelled on animal brain functions. ANIMAT research in the new millennium has focused on application of neural networks for learning and also to imitate the human nervous system and neuronal processes.

Researchers have utilised the ANIMAT paradigm in various ways, as discussed with case studies in the later paragraphs. The former behaviour is an effort to to identify food while the latter behaviour is an attempt to ingest it. The energy level determines the arousal and satiation level of the artificial cockroach, which in a fully satiated state will not attempt to acquire food. Locomotion is not explicitly represented as a separate behaviour.

Then the artificial cockroach follows the edge of the obstacle in an attempt to reach the food. Therefore, the ordering between these two behaviours is variable and dependent on the environment. The sensors used are the antennae and mouth sensors to extract information from the environment and interact with it.

The artificial cockroach is capable of feeding, locomotion, wandering and edge-following. The lines with darkened circles show inhibition between behaviours. Adapted from Beer [33]. From [] courtesy Elsevier, used with permission. Arkin et al. The design considerations were small body size, light weight, less ground contact and high-energy efficiency to actuate the jump.

They were guided by a semi-empirical relation which holds good for millimeter-to-centimeter-sized insects. Here the ecological niche of various insects and animals has to be exploited in order to model a jumping behaviour. The researchers conclude that with such a model, it does supplements the knowledge of how insects choose their mode of locomotion and evolve to improve their well being. This one-legged gait robot is unique and one of its kind since it entirely runs on lithium batteries.

Gait robots are usually designed with hydraulics, but this one uses a linear elastic actuator in parallel LEAP mechanism as shown. The LEAP has 4 components: encoder, temperature sensor, voice coil actuator in parallel with two compression springs and two servo motors enable the hopping action.

The robot is still under developmental phase and can jump for about seven seconds or nearly 19 hops before tipping over, it lacks an aesthetic appeal and has a mess of wires at its top. Later versions will hope to maintain the balance of the robot for a longer time with more on-board processing power using Odroid, Raspberry Pi, etc.

In nature, these processes work from within the organism, without any sensory input from limbs, other muscles or any other motor control, and are believed to be responsible for chewing, breathing, digesting and locomotion. Such rhythmic motor processes for locomotion have been confirmed for cats, dogs and rabbits and are believed to exist for human locomotion. A CPG can be said to be analogous to a pendulum, producing sinusoids at a constant frequency.

A pattern generator is useful for mimicking known biological motor functions when it involves two or more motor processes such that each process follows the next one in a serial order. Therefore, to devise a CPG solution to a dynamic problem. Image courtesy Elsevier, used with permission. CPG has been used is to design walking in legged robots as an extension of arm robots, and modelled on the principles of inverse kinematic. Bio-inspired designs using CPG is a good alternative for biped and quadruped locomotion and renders ease of design.

Biological processes defy mathematical modelling but can be carefully designed from primitive behaviours. As shown in Figure 2. CPGs source is located close to the head position for the biped and produces periodic motor movements for locomotion. The feedback from the sensors placed at the limbs helps in adapting to the environment by modifying the pattern s generated by the CPG.

ANIMATs is a less expensive, more efficient and ethical alternative than guinea pigs and rabbits for demonstration, teaching and research purposes. Ishii et al. To mimic rat-like dynamics and ergonomics the robot has 14 active degrees of freedom, 12 of which mimic rat behaviour while 2 active wheels are supplied at the hips.

This research attempts to chart the psychological stimulus of stress and mental disorder leading to stress-vulnerability on the homeostasis of rats. Rats, guinea pigs, rabbits and hamsters are often used in lab trials, the results of which are extrapolated to human beings and further human trials are made in similar conditions. To induce stress and depression, the olfactory faculty of rats is severed or made to undergo extreme physical hardship such as long hours of swimming, while other alternatives are genetic modification and environmental stress, however none of these are able to recreate a human-like version of depression.

The development of stress, stigma and depression as often happens in with human beings is recreated in real time among rats in experiments employing the WR-3 robot. The rats in the first group were constantly harassed by the aggressive behaviour of the robot rat, while the rats in the second group were attacked by the robot rat whenever they moved.

A general rule of thumb that worked out to quantify the research was that a depressed rat exhibits lessened mobility. It was found that the highest levels of depression were induced by intermittent attacks on a rat that had a history of being harassed in previous weeks.

Employing such methods to correlate to human conditions has led to attempts to find treatments for sepsis, burns and trauma. Thus tailoring the clinical trials also effectively bringing down the cost of such trials. A further goal of this research is the prospect to extend such trials to the domain of mental disorders such as schizophrenia and anxiety disorder. Studies have show that the rats cautiously approach and interact with their robot counterpart. The researchers propose to study competitive and cooperative behaviour of the iRAT with the real rats.

The advantages of RatSLAM is that it works well with low-resolution and poor-intensity profile visual data. There has been growing enthusiasm for brain-based models and biologically inspired cognitive robotics [56]. The adult human brain is about 1, to 1, g, and has neurons, and the sheer complexity defies any attempts to replicate it. Neither has it yet been possible to measure the timing and flow of information in the animal brain and this adds to the difficulty of any direct attempt at designing robots based on the working of the brain.

OpenWorm Project [81, ] has taken the problem to the citizen roboticist and is a novel attempt to crowd source, design and code in models of neural connections of the brain.

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Include your email address to get a message when this question is answered. By using this service, some information may be shared with YouTube. To make the risk of destruction lower, make the hole deeper.

The TNT should still be below the pressure-plate-block, but it will fall. Helpful 3 Not Helpful 0. Helpful 5 Not Helpful 1. When you chase a player, try to chase them into a minefield. They will be too hesitant to be cautious. However, you should also be cautious or else you will fall for your own traps! Helpful 2 Not Helpful 1. If a player uses pressure plates to open doors, make a mine there. This also applies to any "bait" doors you might make.

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