AI Unit 4 PDF - 18-08-2024

COURSE MATERIAL ARTIFICIAL SUBJECT INTELLIGENCE(AM20APC501 ) UNIT 4 COURSE B.TECH DEPARTMENT CSE(AI & ML) SEMESTER 3-1 PREPARED BY...

COURSE MATERIAL ARTIFICIAL SUBJECT INTELLIGENCE(AM20APC501 ) UNIT 4 COURSE B.TECH DEPARTMENT CSE(AI & ML) SEMESTER 3-1 PREPARED BY S. Mrudula (Faculty Name/s) Version V-2 PREPARED / REVISED DATE 18-08-2024  TABLE OF CONTENTS – UNIT 4 S. NO CONTENTS PAGE NO. 1 COURSE OBJECTIVES 3 2 PREREQUISITES 3 3 SYLLABUS 3 4 COURSE OUTCOMES 4 5 CO - PO/PSO MAPPING 4 6 LESSON PLAN 4 7 ACTIVITY BASED LEARNING 4 8 LECTURE NOTES 5 Robotics: Introduction 2.1 5 Robot Hardware 2.2 10 Robotic Perception 2.3 12 Planning to move 2.4 14 planning uncertain movements 2.5 15 Moving, Robotic software architectures 2.6 16 application domains 2.7 20 Philosophical foundations: Weak AI 2.8 23 2.9 Strong AI, Ethics and Risks of AI 25 Agent Components 2.10 28 Agent Architectures 2.11 31 2.12 Are we going in the right direction, What if AI does succeed 34 9 PRACTICE QUIZ 35 10 ASSIGNMENTS 36 11 PART A QUESTIONS & ANSWERS (2 MARKS QUESTIONS) 36 12 PART B QUESTIONS 37 13 SUPPORTIVE ONLINE CERTIFICATION COURSES 37 14 REAL TIME APPLICATIONS 38 15 CONTENTS BEYOND THE SYLLABUS 38 16 PRESCRIBED TEXT BOOKS & REFERENCE BOOKS 38 17 MINI PROJECT SUGGESTION 38 1. Course Objectives The objectives of this course is to 1. To understand To learn the basics of designing intelligent agents that can solve general purpose problems 2. To represent and process knowledge, plan and act, reason under uncertainty and 3. To learn from experiences. 2. Prerequisites Students should have knowledge on 1. Database Management Systems 2. Data Warehousing and Mining 3. Syllabus UNIT IV Robotics: Introduction, Robot Hardware, Robotic Perception, Planning to move, planning uncertain movements, Moving, Robotic software architectures, application domains Philosophical foundations: Weak AI, Strong AI, Ethics and Risks of AI, Agent Components, Agent Architectures, Are we going in the right direction, What if AI does succeed. 4. Course outcomes: 1. Student must be able to understand fundamental of artificial intelligence (AI) and its  foundations 2. Student must be able to analyze principles of AI in solutions that require problem solving, inference, perception and learning. 3. Student must be able to design various applications of AI techniques in artificial neural networks and other machine learning models 4. Student must be able to demonstrate scientific method to models of machine learning 5. Co-PO / PSO Mapping Machine PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 P10 PO11 PO12 PSO1 PSO Tools 3 2 2 3 2 CO1 3 2 2 3 2 CO2 3 2 3 3 3 3 CO3 3 2 3 3 3 3 CO4 6. Lesson Plan Lecture Weeks Topics to be covered References No. Robotics: Introduction 1 T2 Robot Hardware T2 2 1 Robotic Perception T2 3 Planning to move T2 4 2 planning uncertain movements T2 5 Moving, Robotic software architectures T2 6 application domains T2 7  Philosophical foundations: Weak AI T2 8 Strong AI, Ethics and Risks of AI T2 9 Agent Components T2 10 Agent Architectures T2 11 3 Are we going in the right direction T2 12 What if AI does succeed T2 13 7. Activity Based Learning 1. Implementing the propositional and first order logics Model using Python 8. Lecture Notes Robotics: Introduction Introduction To Robots What is the first thing that comes to mind when you think of a robot? For many people it is a machine that imitates a human—like the androids in Star Wars, Terminator and Star Trek: The Next Generation. However much these robots capture our imagination, such robots still only inhabit Science Fiction. People still haven't been able to give a robot enough 'common sense' to reliably interact with a dynamic world. However, Rodney Brooks and his team at MIT Artificial Intelligence Lab are working on creating such humanoid robots. The type of robots that you will encounter most frequently are robots that do work that is too dangerous, boring, onerous, or just plain nasty. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. In fact, there are over a million of these type of robots working for us today. Some robots like the Mars Rover Sojourner and the upcoming Mars Exploration Rover, or the underwater robot Caribou help us learn about places that are too dangerous for us to go. While other types of robots are just plain fun for kids of all ages. Popular toys such as Teckno, Polly or AIBO ERS-220 seem to hit the store shelves every year around Christmas time. And as much fun as robots are to play with, robots are even much more fun to build. In Being Digital, Nicholas Negroponte tells a wonderful story about an eight year old, pressed during a televised premier of MITMedia Lab's LEGO/Logo work at Hennigan School. A zealous anchor, looking for a cute sound bite, kept asking the child if he was having fun playing with LEGO/Logo. Clearly exasperated, but not wishing to offend, the child first tried to put her off. After her third attempt to get him to talk about fun, the child, sweating under the hot television lights, plaintively looked into the camera and answered, "Yes it is fun, but it's hard fun." But what exactly is a robot? As strange as it might seem, there really is no standard definition for a robot. However, there are some essential characteristics that a robot must have and this might help you to decide what is and what is not a robot. It will also help you to decide what features you will need to build into a machine before it can count as a robot. A robot has these essential characteristics:  Sensing First of all your robot would have to be able to sense its surroundings. It would do this in ways that are not unsimilar to the way that you sense your surroundings. Giving your robot sensors: light sensors (eyes), touch and pressure sensors (hands), chemical sensors (nose), hearing and  sonar sensors (ears), and taste sensors (tongue) will give your robot awareness of its environment.  Movement A robot needs to be able to move around its environment. Whether rolling on wheels, walking on legs or propelling by thrusters a robot needs to be able to move. To count as a robot either the whole robot moves, like the Sojourner or just parts of the robot moves, like the Canada Arm.  Energy A robot needs to be able to power itself. A robot might be solar powered, electrically powered, battery powered. The way your robot gets its energy will depend on what your robot needs to do.  Intelligence A robot needs some kind of "smarts." This is where programming enters the pictures. A programmer is the person who gives the robot its 'smarts.' The robot will have to have some way to receive the program so that it knows what it is to do. So what is a robot? Well it is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. Designing, building, programming and testing a robots is a combination of physics, mechanical engineering, electrical engineering, structural engineering, mathematics and computing. In some cases biology, medicine, chemistry might also be involved. A study of robotics means that students are actively engaged with all of these disciplines in a deeply problem-posing problem-solving environment. ROBOT HARDWARE the agent architecture—sensors, effectors, and processors— as given, and we have concentrated on the agent program. The success of real robots depends at least as much on the design of sensors and effectors that are appropriate for the task. 25.2.1 Sensors Sensors are the perceptual interface between PASSIVE SENSOR robot and environment. Passive sensors, such as cameras, are true observers of the environment: they capture signals that are generated by other sources in the environment. Active sensors, such as sonar, send energy into the environment. They rely on the fact that this energy is reflected back to the sensor. Active sensors tend to provide more information than passive sensors, but at the expense of increased power consumption and with a danger of interference when multiple active sensors are used at the same time. Whether active or passive, sensors can be divided into three types, depending on whether they sense the environment, the robot’s location, or the robot’s internal configuration. Range finders are sensors that measure the distance to nearby objects. In the early days of robotics, robots were commonly equipped with sonar sensors. Sonar sensors emit directional sound waves, which are reflected by objects, with some of the sound making it back into the sensor. The time and intensity of the returning signal indicates the distance to nearby objects. Sonar is the technology of choice for autonomous underwater vehicles. Stereo vision relies on multiple cameras to image the environment from slightly different viewpoints, analyzing the resulting parallax in these images to compute the range of surrounding objects. For mobile ground robots, sonar and stereo vision are now rarely used, because they are not reliably accurate. Most ground robots are now equipped with optical range finders. Just like sonar sensors, optical range sensors emit active signals (light) and measure the time until a reflection of thissignal arrives back at the sensor. Figure (a) shows a time of flight camera. This cameraacquires range images like the one shown in Figure (b) at up to 60 frames per second. Other range sensors use laser beams and special 1-pixel cameras that can be directed using complex arrangements of mirrors or rotating elements. These sensors are called scanning lidars (short for light detection and ranging). Scanning lidars tend to provide longer rangesthan time of flight cameras, and tend to perform better in bright daylight. Effectors Effectors are the means by which robots move and change the shape of their bodies. To understand the design of effectors, it will help to talk about motion and shape in the abstract,using the concept of a degree of freedom (DOF) We count one degree of freedom for eachindependent direction in which a robot, or one of its effectors, can move. For example, a rigid mobile robot such as an AUV has six degrees of freedom, three for its (x, y, z) location in space and three for its angular orientation, 2 known as yaw, roll, and pitch. These six degrees define the kinematic state or pose of the robot. The dynamic state of a robot includes thesesix plus an additional six dimensions for the rate of change of each kinematic dimension, thatis, their velocities. For nonrigid bodies, there are additional degrees of freedom within the robot itself. Forexample, the elbow of a human arm possesses two degree of freedom. It can flex the upper arm towards or away, and can rotate right or left. The wrist has three degrees of freedom. Itcan move up and down, side to side, and can also rotate. Robot joints also have one, two,or three degrees of freedom each. Six degrees of freedom are required to place an object, such as a hand, at a particular point in a particular orientation. The arm in Figure 25.4(a)  Figure (a) The Stanford Manipulator, an early robot arm with five revolute joints (R) and one prismatic joint (P), for a total of six degrees of freedom. (b) Motion of a nonholo- nomic four-wheeled vehicle with front-wheel steering. For mobile robots, the DOFs are not necessarily the same as the number of actuated ele- ments. Consider, for example, your average car: it can move forward or backward, and it can turn, giving it two DOFs. In contrast, a car’s kinematic configuration is three-dimensional: on an open flat surface, one can easily maneuver a car to any (x, y) point, in any orientation. Thus, the car has three effective degrees of freedom but two control-lable degrees of freedom. We say a robot is nonholonomic if it has more effective DOFs SVCE TIRUPATI than controllable DOFs and holonomic if the two numbers are the same. Holonomic robotsare easier to control—it would be much easier to park a car that could move sideways as well as forward and backward—but holonomic robots are also mechanically more complex. Most robot arms are holonomic, and most mobile robots are nonholonomic. (a) Mobile manipulator plugging its charge cable into a wall outlet. Image courtesy of Willow Garage, Ⓧ c 2009. (b) One of Marc Raibert’s legged robots in motion. (a) displays a two-armed robot. This robot’s arms use springs to compensate for gravity, and they provide minimal resistance to external forces. Such a design minimizes the physical danger to people who might stumbleinto such a robot. This is a key consideration in deploying robots in domestic environments. (b). This robot is dynamicallystable, meaning that it can remain upright while hopping around. A robot that can remain upright without moving its legs is called statically stable. A robot is statically stable if its center of gravity is above the polygon spanned by its legs. (a) Four-legged dynamically-stable robot “Big Dog.” Image courtesy Boston Dynamics, Ⓧ c 2009. (b) 2009 RoboCup Standard Platform League competition, showing the winning team, B-Human, from the DFKI center at the University of Bremen. Throughout the match, B-Human outscored their opponents 64:1. Their success was built on probabilistic state estimation using particle filters and Kalman filters; on machine-learning models for gait optimization; and on dynamic kicking moves. Image courtesy DFKI, Ⓧ c 2009. ROBOTIC PERCEPTION Perception is the process by which robots map sensor measurements into internal representa-tions of the environment. Perception is difficult because sensors are noisy, and the environ- ment is partially observable, unpredictable, and often dynamic. In other words, robots have all the problems of state 1|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI estimation (or filtering) As a rule of thumb, good internal representations for robots have three properties: they contain enough information for the robot to make good decisions, they are structured so that they can be updated efficiently, and they are natural in the sense that internal variables correspond tonatural state variables in the physical world. We saw that Kalman filters, HMMs, and dynamic Bayes nets can repre- sent the transition and sensor models of a partially observable environment, and we described both exact and approximate algorithms for updating the belief state—the posterior probabil- ity distribution over the environment state variables. Robot perception can be viewed as temporal inference from sequences ofactions and measurements, as illustrated by this dynamic Bayes network. Localization and mapping Localization is the problem of finding out where things are—including the robot itself. Knowledge about where things are is at the core of any successful physical interaction withthe environment. For example, robot manipulators must know the location of objects they seek to manipulate; navigating robots must know where they are to find their way around. To keep things simple, let us consider a mobile robot that moves slowly in a flat 2D world. Let us also assume the robot is given an exact map of the environment. The pose of such a mobile robot is defined by its two Cartesian coordinates with values x and y and its heading with value θ, as illustrated in Figure 25.8(a). If we arrange those three values in a vector, then any T particular state is given by Xt = (xt, yt, θt). So far so good. (a) A simplified kinematic model of a mobile robot. The robot is shown as a circle with an interior line marking the forward direction. The state xt consists of the (xt, yt) position (shown implicitly) and the orientation θt. The new state xt+1 is obtained by an update in position of vtΔt and in orientation of ωtΔt. Also shown is a 2|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI landmark at (xi, yi) observed at time t. (b) The range-scan sensor model. Two possible robot poses are shown for a given range scan (z1, z2, z3, z4). It is much more likely that the pose on the left generated the range scan than the pose on the right. Monte Carlo localization Other types of perception Not all of robot perception is about localization or mapping. Robots also perceive the tem- perature, odors, acoustic signals, and so on. Many of these quantities can be estimated using variants of dynamic Bayes networks. All that is required for such estimators are conditionalprobability distributions that characterize the evolution of state variables over time, and sen-sor models that describe the relation of measurements to state variables. It is also possible to program a robot as a reactive agent, without explicitly reasoning about probability distributions over states 3|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI Machine learning in robot perception Machine learning plays an important role in robot perception. This is particularly the case when the best internal representation is not known. One common approach is to map high- dimensional sensor streams into lower-dimensional spaces using unsupervised machine learn-ing methods. Such an approach is called low- dimensional embedding. Machine learning makes it possible to learn sensor and motion models from data, while si- multaneously discovering a suitable internal representations. Another machine learning technique enables robots to continuously adapt to broad changes in sensor measurements. Picture yourself walking from a sun-lit space into a dark neon-lit room. Clearly things are darker inside. But the change of light source also affects allthe colors: Neon light has a stronger component of green light than sunlight. Yet somehow we seem not to notice the change. If we walk together with people into a neon-lit room, we don’t think that suddenly their faces turned green. Our perception quickly adapts to the newlighting conditions, and our brain ignores the differences. Methods that make robots collect their own training data (with labels!) are called self-supervised. In this instance, the robot uses machine learning to leverage a short-range sensor that works well for terrain classification into a sensor that can see much farther. That allowsthe robot to drive faster, slowing down only when the sensor model says there is a change inthe terrain that needs to be examined more carefully by the short-range sensors. W EAK AI: CAN M ACHINES ACT I NTELLIGENTLY ? whether AI is impossible depends on how it is defined. we de-fined AI as the quest for the best agent program on a given architecture. With this formulation, AI is by definition possible: for any digital architecture k with k bits of program storage thereare exactly 2 agent programs, and all we have to do to find the best one is enumerate and test them all. This might not be feasible for large k, but philosophers deal with the theoretical, not the practical. Our definition of AI works well for the engineering problem of finding a good agent, given an architecture. Therefore, we’re tempted to end this section right now, answering the title question in the affirmative. But philosophers are interested in the problem of compar- ing two architectures—human and machine. Furthermore, they have traditionally posed the question not in terms of maximizing expected utility but rather as, “Can machines think?” Alan Turing, in his famous paper “Computing Machinery and Intelligence” (1950), sug- gested that instead of asking whether machines can think, we should ask whether machines can pass a behavioral intelligence test, which has come to be called the Turing Test. The test is for a program to have a 4|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI conversation (via online typed messages) with an interrogator forfive minutes. The interrogator then has to guess if the conversation is with a program or a person; the program passes the test if it fools the interrogator 30% of the time. The argument from disability The “argument from disability” makes the claim that “a machine can never do X.” As exam-ples of X, Turing lists the following: Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behavior as man, do something really new It is clear that computers can do many things as well as or better than humans, including things that people believe require great human insight and understanding. This does not mean, of course, that computers use insight and understanding in performing these tasks—those arenot part of behavior, and we address such questions elsewhere—but the point is that one’s first guess about the mental processes required to produce a given behavior is often wrong. It is also true, of course, that there are many tasks at which computers do not yet excel (to put it mildly), including Turing’s task of carrying on an open-ended conversation. The mathematical objection It is well known, through the work of Turing (1936) and Gödel (1931), that certain math- ematical questions are in principle unanswerable by particular formal systems. Gödel’s in- completeness theorem (see Section 9.5) is the most famous example of this. Briefly, for anyformal axiomatic system F powerful enough to do arithmetic, it is possible to construct a so-called Gödel sentence G(F ) with the following properties: G(F ) is a sentence of F , but cannot be proved within F. If F is consistent, then G(F ) is true. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. It is all too easy to show rigorously that a formal system cannot do X, and then claim that hu-mans can do X using their own informal method, without giving any evidence for this claim.Indeed, it is impossible to prove that humans are not subject to Gödel’s incompleteness theo-rem, because any rigorous proof would require a formalization of the claimed unformalizable human talent, and hence refute itself. So we are left with an appeal to intuition that humans can somehow perform superhuman feats of mathematical insight. This appeal is expressed with arguments such as “we must assume our own consistency, if thought is to be possible atall” (Lucas, 1976). But if anything, humans are known to be inconsistent. This is certainly true for everyday reasoning, but it is also true for careful mathematical thought. A famous example is the four-color map problem. Alfred Kempe published a proof in 1879 that was widely accepted and contributed to his election as a Fellow of the Royal Society. In 1890,however, Percy Heawood pointed out a flaw and the theorem remained unproved until 1977. The argument from informality One of the most influential and persistent criticisms of AI as an enterprise was raised by Tur- ing as the “argument from informality of behavior.” Essentially, this is the claim that humanbehavior is far too complex 5|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI to be captured by any simple set of rules and that because com-puters can do no more than follow a set of rules, they cannot generate behavior as intelligentas that of humans. The inability to capture everything in a set of logical rules is called the qualification problem in AI. 1. Good generalization from examples cannot be achieved without background knowl- edge. They claim no one has any idea how to incorporate background knowledge into the neural network learning process. In fact, that there are techniques for using prior knowledge in learning algorithms. Those techniques, however, rely on the availability of knowledge in explicit form, something that Dreyfus and Dreyfus strenuously deny. In our view, this is a good reason for a serious redesign of current models of neural processing so that they can take advantage of previously learned knowledge in the way that other learning algorithms do. 2. Neural network learning is a form of supervised learning, requiring the prior identification of relevant inputs and correct outputs. Therefore, they claim, it cannot operate autonomously without the help of a human trainer. In fact, learning without a teacher can be accomplished by unsupervised learning and reinforcement learning. 3. Learning algorithms do not perform well with many features, and if we pick a subset of features, “there is no known way of adding new features should the current set proveinadequate to account for the learned facts.” In fact, new methods such as support vector machines handle large feature sets very well. With the introduction of large Web-based data sets, many applications in areas such as language processing (Sha andPereira, 2003) and computer vision (Viola and Jones, 2002a) routinely handle millionsof features. 4. The brain is able to direct its sensors to seek relevant information and to process itto extract aspects relevant to the current situation. But, Dreyfus and Dreyfus claim, “Currently, no details of this mechanism are understood or even hypothesized in a waythat could guide AI research.” In fact, the field of active vision, underpinned by the theory of information value , is concerned with exactly the problem of directing sensors, and already some robots have incorporated the theoretical results obtained S TRONG AI: CAN M ACHINES REALLY T HINK ? Many philosophers have claimed that a machine that passes the Turing Test would still not be actually thinking, but would be only a simulation of thinking. Again, the objection was foreseen by Turing. He cites a speech by Professor Geoffrey Jefferson (1949): Not until a machine could write a sonnet or compose a concerto because of thoughts andemotions felt, and not by the chance fall of symbols, could we agree that machine equalsbrain—that is, not only write it but know that it had written it. Turing calls this the argument from consciousness—the machine has to be aware of its ownmental states and actions. While consciousness is an important subject, Jefferson’s key pointactually relates to phenomenology, or the study of direct experience: the machine has to actually feel emotions. Others focus on intentionality—that is, the question of whether the machine’s purported beliefs, desires, and other representations are actually “about” some- thing in the real world. Turing argues that Jefferson would be willing to extend the polite convention to ma- chines if only he had experience with ones that act intelligently. He cites the following dialog,which has become such a part of AI’s oral tradition that we simply have to include it: HUMAN:In the first line of your sonnet which reads “shall I compare thee to a summer’sday,” would not a “spring day” do as well or better? MACHINE: It wouldn’t scan. HUMAN: How about “a winter’s day.” That would scan all right. 6|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI MACHINE: Yes, but nobody wants to be compared to a winter’s day. HUMAN: Would you say Mr. Pickwick reminded you of Christmas? MACHINE: In a way. HUMAN:Yet Christmas is a winter’s day, and I do not think Mr. Pickwick would mind the comparison. MACHINE: I don’t think you’re serious. By a winter’s day one means a typical winter’sday, rather than a special one like Christmas Mental states and the brain in a vat Physicalist philosophers have attempted to explicate what it means to say that a person—and, by extension, a computer—is in a particular mental state. They have focused in particular on intentional states. These are states, such as believing, knowing, desiring, fearing, and so on,that refer to some aspect of the external world. For example, the knowledge that one is eating a hamburger is a belief about the hamburger and what is happening to it. If physicalism is correct, it must be the case that the proper description of a person’s mental state is determined by that person’s brain state. Thus, if I am currently focused on eating a hamburger in a mindful way, my instantaneous brain state is an instance of the class of mental states “knowing that one is eating a hamburger.” Of course, the specific configurations of all the atoms of my brain are not essential: there are many configurations of my brain, or of other people’s brain, that would belong to the same class of mental states. The key point is that the same brain state could not correspond to a fundamentally distinct mental state, suchas the knowledge that one is eating a banana. The “wide content” view interprets it from the point of view of an omniscient outside observer with access to the whole situation, who can distinguish differences in the world. Under this view, the content of mental states involves both the brain state and the environment history. Narrow content, on the other hand, considers only the brain state. The narrow content of the brain states of a real hamburger-eater and a brain-in-a -vat “hamburger”-“eater” is the same in both cases. Functionalism and the brain replacement experiment The theory of functionalism says that a mental state is any intermediate causal condition between input and output. Under functionalist theory, any two systems with isomorphic causal processes would have the same mental states. Therefore, a computer program could have the same mental states as a person. Of course, we have not yet said what “isomorphic”really means, but the assumption is that there is some level of abstraction below which the specific implementation does not matter. And this explanation must also apply to the real brain,which has the same functional properties. There are three possible conclusions: 1. The causal mechanisms of consciousness that generate these kinds of outputs in normalbrains are still operating in the electronic version, which is therefore conscious. 2. The conscious mental events in the normal brain have no causal connection to behavior,and are missing from the electronic brain, which is therefore not conscious. 3. The experiment is impossible, and therefore speculation about it is meaningless. Biological naturalism and the Chinese Room A strong challenge to functionalism has been mounted by John Searle’s (1980) biological naturalism, according to which mental states are high-level emergent features that are caused by low-level physical processes in the neurons, and it is the (unspecified) properties of the neurons that matter. Thus, mental states cannot be duplicated just on the basis of some pro- gram having the same functional structure with the same input–output behavior; we would require that the program be running on an architecture with the same causal power as neurons. To support his view, Searle describes a hypothetical system that is clearly running a 7|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI programand passes the Turing Test, but that equally clearly (according to Searle) does not understand anything of its inputs and outputs. His conclusion is that running the appropriate program (i.e., having the right outputs) is not a sufficient condition for being a mind. So far, so good. But from the outside, we see a system that is taking input in the formof Chinese sentences and generating answers in Chinese that are as “intelligent” as those in the conversation imagined by 4 Turing. Searle then argues: the person in the room doesnot understand Chinese (given). The rule book and the stacks of paper, being just pieces of paper, do not understand Chinese. Therefore, there is no understanding of Chinese. Hence,according to Searle, running the right program does not necessarily generate understanding. The real claim made by Searle rests upon thefollowing four axioms (Searle, 1990): 1. Computer programs are formal (syntactic). 2. Human minds have mental contents (semantics). 3. Syntax by itself is neither constitutive of nor sufficient for semantics. 4. Brains cause minds. From the first three axioms Searle concludes that programs are not sufficient for minds. In other words, an agent running a program might be a mind, but it is not necessarily a mind justby virtue of running the program. From the fourth axiom he concludes “Any other system capable of causing minds would have to have causal powers (at least) equivalent to those of brains.” From there he infers that any artificial brain would have to duplicate the causal powers of brains, not just run a particular program, and that human brains do not produce mental phenomena solely by virtue of running a program. Consciousness, qualia, and the explanatory gap Running through all the debates about strong AI—the elephant in the debating room, so to speak—is the issue of consciousness. Consciousness is often broken down into aspects such as understanding and self- awareness. The aspect we will focus on is that of subjective experience: why it is that it feels like something to have certain brain states (e.g., while eatinga hamburger), whereas it presumably does not feel like anything to have other physical states(e.g., while being a rock). The technical term for the intrinsic nature of experiences is qualia(from the Latin word meaning, roughly, “such things”). Qualia present a challenge for functionalist accounts of the mind because different qualia could be involved in what are otherwise isomorphic causal processes. Consider, for example, the inverted spectrum thought experiment, which the subjective experience of per-son X when seeing red objects is the same experience that the rest of us experience when seeing green objects, and vice versa. This explanatory gap has led some philosophers to conclude that humans are simply incapable of forming a proper understanding of their own consciousness.Others, notably Daniel Dennett (1991), avoid the gap by denying the existence of qualia,attributing them to a philosophical confusion. THE ETHICS AND RISKS OF DEVELOPING ARTIFICIAL INTELLIGENCE So far, we have concentrated on whether we can develop AI, but we must also consider whether we should. If the effects of AI technology are more likely to be negative than positive, then it would be the moral responsibility of workers in the field to redirect their research. Many new technologies have had unintended negative side 8|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI effects: nuclear fission brought Chernobyl and the threat of global destruction; the internal combustion engine brought air pollution, global warming, and the paving-over of paradise. In a sense, automobiles are robots that have conquered the world by making themselves indispensable. AI, however, seems to pose some fresh problems beyond that of, say, building bridges that don’t fall down: People might lose their jobs to automation. People might have too much (or too little) leisure time. People might lose their sense of being unique. AI systems might be used toward undesirable ends. The use of AI systems might result in a loss of accountability. The success of AI might mean the end of the human race.We will look at each issue in turn. People might lose their jobs to automation. The modern industrial economy has be- come dependent on computers in general, and select AI programs in particular. For example,much of the economy, especially in the United States, depends on the availability of con- sumer credit. Credit card applications, charge approvals, and fraud detection are now done by AI programs. One could say that thousands of workers have been displaced by these AI programs, but in fact if you took away the AI programs these jobs would not exist, because human labor would add an unacceptable cost to the transactions. People might have too much (or too little) leisure time. Alvin Toffler wrote in Future Shock (1970), “The work week has been cut by 50 percent since the turn of the century. It is not out of the way to predict that it will be slashed in half again by 2000.” Arthur C. Clarke (1968b) wrote that people in 2001 might be “faced with a future of utter boredom, where the main problem in life is deciding which of several hundred TV channels to select.” People might lose their sense of being unique. In Computer Power and Human Rea- son, Weizenbaum (1976), the author of the ELIZA program, points out some of the potential threats that AI poses to society. One of Weizenbaum’s principal arguments is that AI research makes possible the idea that humans are automata—an idea that results in a loss of autonomyor even of humanity. AI systems might be used toward undesirable ends. Advanced technologies have often been used by the powerful to suppress their rivals. As the number theorist G. H. Hardywrote (Hardy, 1940), “A science is said to be useful if its development tends to accentuate the existing inequalities in the distribution of wealth, or more directly promotes the destruction of human life.” This holds for all sciences, AI being no exception. Autonomous AI systems are now commonplace on the battlefield; the U.S. military deployed over 5,000 autonomousaircraft and 12,000 autonomous ground vehicles in Iraq (Singer, 2009). The use of AI systems might result in a loss of accountability. In the litigious atmo-sphere that prevails in the United States, legal liability becomes an important issue. When aphysician relies on the judgment of a medical expert system for a diagnosis, who is at fault ifthe diagnosis is wrong? Fortunately, due in part to the growing influence of decision-theoreticmethods in medicine, it is now accepted that negligence cannot be shown if the physician performs medical procedures that have high expected utility, even if the actual result is catas-trophic for the patient. 9|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI The success of AI might mean the end of the human race. Almost any technology has the potential to cause harm in the wrong hands, but with AI and robotics, we have the new problem that the wrong hands might belong to the technology itself. Countless science fiction stories have warned about robots or robot–human cyborgs running amok. If ultraintelligent machines are a possibility, we humans would do well to make sure that we design their predecessors in such a way that they design themselves to treat us well.Science fiction writer Isaac Asimov (1942) was the first to address this issue, with his threelaws of robotics: 1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. A robot must obey orders given to it by human beings, except where such orders wouldconflict with the First Law. A robot must protect its own existence as long as such protection does not conflict withthe First or SecondLaw AGENT COMPONENTS Interaction with the environment through sensors and actuators: For much of thehistory of AI, this has been a glaring weak point. With a few honorable exceptions, AI sys- tems were built in such a way that humans had to supply the inputs and interpret the outputs, Figure A model-based, utility-based agent while robotic systems focused on low-level tasks in which high-level reasoning and plan- ning were largely absent. This was due in part to the great expense and engineering effort required to get real robots to work at all. The situation has changed rapidly in recent years with the availability of ready-made programmable robots. These, in turn, have benefited from small, cheap, high-resolution CCD cameras and compact, reliable motor drives. MEMS (micro- electromechanical systems) technology has supplied miniaturized accelerometers, gy- roscopes, and actuators for an artificial flying insect (Floreano et al., 2009). It may also be possible to combine millions of MEMS devices to produce powerful macroscopic actuators. Keeping track of the state of the world: This is one of the core capabilities required for an intelligent agent. It requires both perception and updating of internal representations. showed how to keep track of atomic state representations, described how to do it for factored (propositional) state representations extended this to first-order logic; and Chapter 15 described filtering algorithms for probabilistic reasoning in uncertain environments. Current filtering and perception algorithms can be combined to do areasonable job of reporting low-level predicates such as “the cup is on the table.” Detectinghigher-level actions, such as “Dr. Russell is having a cup of tea with Dr. Norvig while dis- cussing plans for next week,” is more difficult. Currently it can be done only with the help of annotated examples. 10|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI Projecting, evaluating, and selecting future courses of action: The basic knowledge- representation requirements here are the same as for keeping track of the world; the primary difficulty is coping with courses of action—such as having a conversation or a cup of tea—that consist eventually of thousands or millions of primitive steps for a real agent. It is only by imposing hierarchical structure on behavior that we humans cope at all.how to use hierarchical representations to handle problems of this scale; fur- ther more, work in hierarchical reinforcement learning has succeeded in combining someof these ideas with the techniques for decision making under uncertainty described in. As yet, algorithms for the partially observable case (POMDPs) are using the same atomic state representation we used for the search algorithms It has proven very difficult to decomposepreferences over complex states in the same way that Bayes nets decompose beliefs over complex states. One reason may be that preferences over states are really compiled from preferences over state histories, which are described by reward functions Learning: Chapters 18 to 21 described how learning in an agent can be formulated as inductive learning (supervised, unsupervised, or reinforcement-based) of the functions that constitute the various components of the agent. Very powerful logical and statistical tech- niques have been developed that can cope with quite large problems, reaching or exceeding human capabilities in many tasks—as long as we are dealing with a predefined vocabulary of features and concepts. AGENT ARCHITECTURES It is natural to ask, “Which of the agent architectures should an agent use?” The answer is, “All of them!” We have seen that reflex responses are needed for situations in which time is of the essence, whereas knowledge-based deliberation allows the agent to plan ahead. A complete agent must be able to do both, using a hybrid architecture. One important property of hybrid architectures is that the boundaries between different decision components are not fixed. For example, compilation continually converts declarative in- formation at the deliberative level into more efficient representations, eventually reaching the reflex level For example, a taxi-driving agent that sees an accident ahead must decide in a split second either to brake or to take evasive action. It should also spend that split second thinking about the most important questions, such as whether the lanes to the left and right are clear and whether there is a large truck close behind, rather than worrying about wear and tear on the tires or where to pick up the next passenger. These issues are usually studied under the heading of real-time AI Fig: Compilation serves to convert deliberative decision making into more effi-cient, reflexive mechanisms. Clearly, there is a pressing need for general methods of controlling deliberation, ratherthan specific recipes for 11|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI what to think about in each situation. The first useful idea is to em-ploy anytime algorithms The second technique for controlling deliberation is decision-theoretic metareasoning (Russell and Wefald, 1989, 1991; Horvitz, 1989; Horvitz and Breese, 1996). This method applies the theory of information value to the selection of individual computa-tions. The value of a computation depends on both its cost (in terms of delaying action) andits benefits (in terms of improved decision quality). Metareasoning techniques can be used todesign better search algorithms and to guarantee that the algorithms have the anytime prop-erty. Metareasoning is expensive, of course, and compilation methods can be applied so thatthe overhead is small compared to the costs of the computations being controlled. Metalevelreinforcement learning may provide another way to acquire effective policies for controllingdeliberation Metareasoning is one specific example of a reflective architecture—that is, an archi-tecture that enables deliberation about the computational entities and actions occurring withinthe architecture itself. A theoretical foundation for reflective architectures can be built by defining a joint state space composed from the environment state and the computational stateof the agent itself. ARE WE GOING IN THE RIGHT DIRECTION? The preceding section listed many advances and many opportunities for further progress. Butwhere is this all leading? Dreyfus (1992) gives the analogy of trying to get to the moon by climbing a tree; one can report steady progress, all the way to the top of the tree. In this section, we consider whether AI’s current path is more like a tree climb or a rocket trip. Perfect rationality. A perfectly rational agent acts at every instant in such a way as to maximize its expected utility, given the information it has acquired from the environment. Wehave seen that the calculations necessary to achieve perfect rationality in most environments are too time consuming, so perfect rationality is not a realistic goal. Calculative rationality. This is the notion of rationality that we have used implicitly in de- signing logical and decision-theoretic agents, and most of theoretical AI research has focused on this property. A calculatively rational agent eventually returns what would have been therational choice at the beginning of its deliberation. This is an interesting property for a systemto exhibit, but in most environments, the right answer at the wrong time is of no value. In practice, AI system designers are forced to compromise on decision quality to obtain reason-able overall performance; unfortunately, the theoretical basis of calculative rationality does not provide a well-founded way to make such compromises. Bounded rationality. Herbert Simon (1957) rejected the notion of perfect (or even approx-imately perfect) rationality and replaced it with bounded rationality, a descriptive theory of decision making by real agents. Bounded optimality (BO). A bounded optimal agent behaves as well as possible, given its computational resources. That is, the expected utility of the agent program for a bounded optimal agent is at least as high as the expected utility of any other agent program running onthe same machine. WHAT IF AI D OES S UCCEED ? In David Lodge’s Small World (1984), a novel about the academic world of literary criticism, the protagonist causes consternation by asking a panel of eminent but contradictory literary theorists the following question: “What if you were right?” None of the theorists seems to have 12|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI considered this question before, perhaps because debating unfalsifiable theories is an endin itself. Similar confusion can be evoked by asking AI researchers, “What if you succeed?” We can expect that medium-level successes in AI would affect all kinds of people in their daily lives. So far, computerized communication networks, such as cell phones and theInternet, have had this kind of pervasive effect on society, but AI has not. AI has been at work behind the scenes—for example, in automatically approving or denying credit card transac- tions for every purchase made on the Web—but has not been visible to the average consumer. We can imagine that truly useful personal assistants for the office or the home would have a large positive impact on people’s lives, although they might cause some economic disloca- tion in the short term. Automated assistants for driving could prevent accidents, saving tens of thousands of lives per year. A technological capability at this level might also be appliedto the development of autonomous weapons, which many view as undesirable. Some of thebiggest societal problems we face today—such as the harnessing of genomic information fortreating disease, the efficient management of energy resources, and the verification of treaties concerning nuclear weapons—are being addressed with the help of AI technologies. Finally, it seems likely that a large-scale success in AI—the creation of human-level in- telligence and beyond—would change the lives of a majority of humankind. The very natureof our work and play would be altered, as would our view of intelligence, consciousness, andthe future destiny of the human race. AI systems at this level of capability could threaten hu-man autonomy, freedom, and even survival. For these reasons, we cannot divorce AI research from its ethical consequences In conclusion, we see that AI has made great progress in its short history, but the final sentence of Alan Turing’s (1950) essay on Computing Machinery and Intelligence is still valid today: We can see only a short distance ahead, but we can see that much remains to be done. 9. Practice Quiz 1. 1. What is the name for information sent from robot sensors to robot controllers? a) temperature b) pressure c) feedback d) signal Answer: c 2. Which of the following terms refers to the rotational motion of a robot arm? a) swivel b) axle c) retrograde d) roll Answer: d 13|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI 3.What is the name for space inside which a robot unit operates? a) environment b) spatial base c) work envelope d) exclusion zone Answer: c 4. Which of the following terms IS NOT one of the five basic parts of a robot? a) peripheral tools b) end effectors c) controller d) drive Answer: a 5. Decision support programs are designed to help managers make __________ a) budget projections b) visual presentations c) business decisions d) vacation schedules Answer: c 6. PROLOG is an AI programming language which solves problems with a form of symbolic logic known as predicate calculus. It was developed in 1972 at the University of Marseilles by a team of specialists. Can you name the person who headed this team? a) Alain Colmerauer b) Niklaus Wirth c) Seymour Papert d) John McCarthy Answer: a 7. The number of moveable joints in the base, the arm, and the end effectors of the robot determines_________ a) degrees of freedom b) payload capacity c) operational limits d) flexibility Answer: a 8. Which of the following places would be LEAST likely to include operational robots? a) warehouse b) factory c) hospitals d) private homes Answer: d 9. For a robot unit to be considered a functional industrial robot, typically, how many degrees of freedom would the robot have? a) three b) four c) six d) eight Answer: c 10. Which of the basic parts of a robot unit would include the computer circuitry that could be programmed to determine what the robot would do? a) sensor 14|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI b) controller c) arm d) end effector Answer: b 10.Assignments S.No Question BL CO 1 Explain briefly about Robotics 2 1 2 Write and Explain a Robot Hardware. 2 1 3 What is Robotic Perception? Explain briefly. 2 1 4 Explain about Robotic Software architecture 2 1 11. Part A- Question & Answers S.No Question& Answers BL CO 1 Define FOL. FOL is a first order logic. It is a representational language of 1 1 knowledge which is powerful than propositional logic (i.e.) Boolean Logic. It is an expressive, declarative, compositional language. 2 Define a knowledge Base: Knowledge base is the central component of knowledge base agent 1 1 and it is described as a set of representations of facts about the world 3 Define an inference procedure: An inference procedure reports whether or not a sentence is entiled by knowledge base provided a knowledge base and a sentence. 1 1 An inference procedure ‘i’ can be described by the sentences that it can derive. If i can derive from knowledge base, we can write. KB -- Alpha is derived from KB or i derives alpha from KB 4 Define Ontological commitment. The difference between propositional and first order logic is in the 1 1 ontological commitment. It assumes about the nature of reality 5 Define domain and domain elements. The set of objects is called domain, sometimes these objects are 1 1 referred as domain elements. 12. Part B- Questions S.No Question BL CO 1 Explain and differentiate between weak AI and Strong AI 1 1 2 Explain about Ethics and risk of developing AI? 2 1 3 Explain about agent Components? 2 1 4 Explain about agent Architecture? 2 1 5 What if AI does Suceed? 3 1 15|A I - U N I T - V BTECH_CSM-SEM 31 SVCE TIRUPATI 13. Supportive Online Certification Courses 1. Opportunities in Artificial Intelligence and Enabling Technologies for Internet of Things(IoT) FDP by AICTE – 2 weeks 2. Artificial Intelligence by Python organized by Brain Vision Solutions Pvt Limited– 1 week. 14. Real Time Applications S.No Application CO 1 Artificial intelligence in banking. The banking sector is revolutionized 1 with the use of AI. 2 Artificial intelligence in marketing 1 3 AI in agriculture 1 4 Artificial intelligence in healthcare 1 5 AI in finance. 1 15. Contents Beyond the Syllabus: 1. Knowledge representation using other logic-Structured representation of knowledge. 2. Rule value approach for Knowledge representation 16. Prescribed Text Books & Reference Books , Text Book: 1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education, 2003. 2. David Poole, Alan Mackworth, Randy Goebel,”Computational Intelligence: a logical approach”, Oxford University Press, 2004 17. MINI PROJECT SUGGESTION: 1. Product Review Analysis For Genuine Rating Android Smart. 2. Online AI Shopping With M-Wallet System. 16|A I - U N I T - V BTECH_CSM-SEM 31