Artificial Intelligence Module 1 PDF

Artificial Intelligence Module 1  Reference Book  Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 3rd edition, 2011. 13  Sophia 14  Sophia is a social humanoid...

Artificial Intelligence Module 1  Reference Book  Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach, Prentice Hall, 3rd edition, 2011. 13  Sophia 14  Sophia is a social humanoid robot  Developed - Hanson Robotics  50 facial expressions  Saudi Citizenship  Chatbot – Provide illusion - understand conversation  best categorized as a chatbot with a face  AI methods including face tracking, emotion recognition,  robotic movements generated by deep neural networks  Hold eye contact  Link 1  Link 2 - Gender 15  Moley Robotic Kitchen 16  Can cook over 100 meals  Moley kitchen could essentially cook any downloadable recipe on the internet  Link 1 - Forbes 17  Furhat the social robot 18  Tilts head, smiles, exudes empathy and warmth  Persuades to interact with it as if it were a person,  picking up on our cues to strike up a rapport.  aims to build on our new-found ease talking to voice assistants like Siri (Apple) and Alexa (Amazon)  AI in Reel life 19  Many sci fi movies are based on AI  I, Robot  Three Laws  First Law - A robot may not injure a human being or, through inaction, allow a human being to come to harm.  Second Law - A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.  Third Law - A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. AI (2001) 21 Big hero 6 22  Baymax, the inflatable healthcare robot Chitti the robo 2.0 23  Interesting Links 24  https://www.hackerearth.com/blog/artificial- intelligence/7-artificial-intelligence-based-movie- characters-now-reality/  Artificial Intelligence 25  Homo sapiens-man the wise - our mental capacities are so important to us.  Tried to understand how we think; that is, how a mere handful of stuff can perceive, understand, predict, arid manipulate a world far larger and more complicated than itself.  The field of artificial intelligence, or AI, goes further still: it attempts not just to understand but also to build intelligent entities.  AI is one of the newest sciences.  What is AI? 26 No clear consensus on the definition of AI John McCarthy coined the phrase AI in 1956  http://www.formal.stanford.edu/jmc/whatisai/whatisai. html Q. What is artificial intelligence? A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human or other intelligence, but AI does not have to confine itself to methods that are biologically observable.  27 AI is a collection of hard problems which can be solved by humans and other living things, but for which we don’t have good algorithms for solving. – e. g., understanding spoken natural language, medical diagnosis, circuit design, learning, self-adaptation, reasoning, chess playing, proving math theories, etc.  Russsell & Norvig: a program that – Acts like human (Turing test) – Thinks like human (patterns of thinking steps) – Acts or thinks rationally (logically, correctly)  AI currently encompasses a huge variety of subfields, ranging from general purpose areas, such as learning and perception to such specific tasks as  playing chess,  proving mathematical theorems,  writing poetry, and  diagnosing diseases.  AI systematizes and automates intellectual tasks and is therefore potentially relevant to any sphere of human intellectual activity.  Definitions of artificial intelligence according to eight textbooks are shown in Figure 1.1.  These definitions vary along two main dimensions. Roughly, the ones on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior.  The definitions on the left measure success in terms of fidelity to human performance, whereas the ones on the right measure against an ideal concept of intelligence, which we will call rationality.  A system is rational if it does the "right thing," given what it knows  Historically, all four approaches to AI have been followed.  As one might expect, a tension exists between approaches centered around humans and approaches centered around rationality.'  A human-centered approach must be an empirical science, involving hypothesis and experimental confirmation.  A rationalist approach involves a combination of mathematics and engineering 32  Based on Alan Turing  Trying to decrypt the Enigma machine, which the Nazis use to send coded messages Alan Turing 33  widely considered to be the father of theoretical computer science and AI.  Turing Machine  Turing Test  https://www.britannica.com /biography/Alan-Turing  Imitation Game / Turing test 34  Uses the "Imitation Game"  Usual method Three people play (man, woman, and interrogator) Interrogator determines which of the other two is a woman by asking questions  Example: How long is your hair? Questions and responses are typewritten or repeated by an intermediary  Acting humanly: The Turing Test approach  The Turing Test, proposed by Alan Turing (195O), was designed to provide a satisfactory operational definition of intelligence.  he suggested a test based on indistinguishability from undeniably intelligent entities-human beings.  The computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or not.  Computer Capabilities  The computer would need to possess the following capabilities:  Natural language processing to enable it to communicate successfully in English.  Knowledge representation to store what it knows or hears;  Automated reasoning to use the stored information to answer questions and to draw new conclusions;  Machine learning to adapt to new circumstances and to detect and extrapolate patterns.  Total Turing test  Turing's test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence.  However, the so-called total Turing Test includes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects "through the hatch." To pass the total Turing Test, the computer will need  Computer vision to perceive objects, and  Robotics to manipulate objects and move about.  These six disciplines compose most of AI, and Turing deserves credit for designing a test that remains relevant 50 years later.  Thinking humanly: The cognitive modeling approach  If we are going to say that a given program thinks like a human, we must have some way of determining how humans think.  We need to get inside the actual workings of human minds. There are two ways to do this: through introspection trying to catch our own thoughts as they go by-and through psychological experiments.  The interdisciplinary field of cognitive science brings together computer models from A1 and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind.  Thinking rationally: The "laws of thought" approach  The Greek philosopher Aristotle was one of the first to attempt to codify "right thinking," that is, irrefutable reasoning processes.  His syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises-for example,  "Socrates is a man; all men are mortal; therefore, Socrates is mortal."  These laws of thought were supposed to govern the operation of the mind; their study initiated the field called logic.  By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation.  The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.  There are two main obstacles to this approach.  First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain.  Second, there is a big difference between being able to solve a problem "in principle" and doing so in practice  Acting rationally: The rational agent approach  An agent is just something that acts  But computer agents are expected to have other attributes that distinguish them from mere "programs," such as  operating under autonomous control,  perceiving their environment,  persisting over a prolonged time period,  adapting to change, and  being capable of taking on another's goals.  A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.  The study of AI as rational-agent design has at least two advantages.  First, it is more general than the "laws of thought" approach, because correct inference is just one of several possible mechanisms for achieving rationality.  Second, it is more amenable to scientific development than are approaches based on human behavior or human thought be- cause the standard of rationality is clearly defined and completely general.  Human behavior, on the other hand, is well-adapted for one specific environment and is the product, in part, of a complicated and largely unknown evolutionary process that still is far from producing perfection.  The Foundations of AI  Philosophy (428 B. c.-present)  Can formal rules be used to draw valid conclusions?  How does the mental mind arise from a physical brain?  Where does knowledge come from?  How does knowledge lead to action? understanding how actions are justified can we understand how to build an agent whose actions are justifiable (or rational). Aristotle argued that actions are justified by a logical connection between goals and knowledge of the action's outcome.  Mathematics  (c. 800-present)  What are the formal rules to draw valid conclusions?  What can be computed?  How do we reason with uncertain information? Leap to a formal science required a level of mathematical formalization in three fundamental areas: logic, computation, and probability.  Formal Logic  Idea - philosophers of ancient Greece  Mathematical development - George Boole (1815-1 864),  Boolean logic.  1879, Gottlob Frege extended Boole's logic to include objects and relations,  First-order logic that is used today as the most basic knowledge representation system.  First Algorithm  Euclid's algorithm for computing greatest common denominators  Undecidability & noncomputability are important to an understanding of computation, the notion of intractability has had a much greater impact.  Roughly speaking, a problem is called intractable if the time required to solve instances of the problem grows exponentially with the size of the instances.  The distinction between polynomial and exponential growth in complexity was first emphasized in the mid-1960s  Economics (1776)  How should we make decisions so as to maximize payoff?  How should we do this when others may not go along?  How should we do this when the payoff may be far in the future? Scottish philosopher Adam Smith An Inquiry into the Nature and Causes of the Wealth of Nations. Ancient Greeks and others had made contributions to economic thought Smith was the first to treat it as a science, using the idea that economies can be thought of as consisting of individual agents maximizing their own economic well- being.  Neuroscience (1861-present)  How do brains process information?  Neuroscience is the study of the nervous system, particularly the brain.  The exact way in which the brain enables thought is one of the great mysteries of science.  The brain is somehow involved in thought,  strong blows to the head - mental incapacitation.  Aristotle wrote, "Of all the animals, man has the largest brain in proportion to his size." 49  Psychology (1879-present)  How do humans and animals think and act?  German physicist Hermann von Helmholtz (1821-1 894) and his student Wilhelm Wundt (1 832-1920).  Helmholtz applied the scientific method to the study of human vision, and his Handbook of Physiological Optics is even now described as "the single most important treatise on the physics and physiology of human vision.  COGNITIVE PSYCHOLOGY  The view of the brain as an information-processing device, which is a principal characteristic of cognitive psychology, can be traced back at least to the works of William James.  It is now a common view among psychologists that "a cognitive theory should be like a computer pro- gram" (Anderson, 1980), that is, it should describe a detailed information-processing mechanism whereby some cognitive function might be implemented.  Computer engineering (1940-present)  How can we build an efficient computer?  For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been the artifact of choice.  Control theory and Cybernetics (1948-present)  How can artifacts operate under their own control?  Linguistics (1957-present)  How does language relate to thought?  The history of AI  Frst work - AI - Warren McCulloch and Walter Pitts (1943).  They drew on three sources: knowledge of the basic physiology and function of neurons in the brain; a formal analysis of propositional logic due to Russell and Whitehead; and Turing’s theory of computation.  Proposed a model of artificial neurons in which each neuron is characterized as being “on” or “off,” with a switch to “on” occurring in response to stimulation by a sufficient number of neighboring neurons.  The state of a neuron was conceived of as “factually equivalent to a proposition which proposed its adequate stimulus.”  Hebbian Learning  Any computable function could be computed by some network of connected neurons, and that all the logical connectives (and, or, not, etc.) could be implemented by simple net structures.  McCulloch and Pitts also suggested that suitably defined networks could learn.  Donald Hebb (1949) demonstrated a simple updating rule for modifying the connection strengths between neurons. His rule, now called Hebbian learning, remains an influential model to this day  Early examples of work that - characterized as AI  Alan Turing’s vision -the most influential.  Lectures on the topic as early as 1947 at the London Mathematical Society and articulated a persuasive agenda in his 1950 article “Computing Machinery and Intelligence.”  Introduced the Turing Test, machine learning, genetic algorithms, and reinforcement learning.  The birth of artificial intelligence  Princeton was home to influential figure in AI, John McCarthy.  Worked Dartmouth College, which was to become the official birthplace of the field.  McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intelligence.  They organized a two-month workshop at Dartmouth in the summer of 1956. John McCarthy 57  This was the first official usage of McCarthy’s term artificial intelligence. Perhaps “computational rationality” would have been more precise and less threatening, but “AI” has stuck.  Early enthusiasm, great expectations  The early years of AI were full of successes—in a limited way.  Given the primitive computers and programming tools of the time and the fact that only a few years earlier computers were seen as things that could do arithmetic and no more, it was astonishing whenever a computer did anything remotely clever.  The intellectual establishment, by and large, preferred to believe that “a machine can never do X.”  AI researchers naturally responded by demonstrating one X after another. John McCarthy referred to this period as the “Look, Ma, no hands!” era.  General Problem Solver  Newell and Simon’s early success was followed up with the General Problem Solver, or GPS.  Designed from the start to imitate human problem- solving protocols.  Within the limited class of puzzles it could handle, it turned out that the order in which the program considered subgoals and possible actions was similar to that in which humans approached the same problems.  Thus, GPS was probably the first program to embody the “thinking humanly” approach.  LISP  John McCarthy moved from Dartmouth to MIT and there made three crucial contributions in one historic year: 1958.  defined the high-level language Lisp, - dominant AI programming language - 30 years.  The tool needed, but access to scarce and expensive computing resources - a serious problem.  In response, invented time sharing.  Published “Programs with Common Sense” - the Advice Taker, a hypothetical program that can be seen as the first complete AI system.  designed to use knowledge to search for solutions to problems.  A dose of reality  Terms such as “visible future” can be interpreted in various ways, but Simon also made more concrete predictions: that within 10 years a computer would be chess champion, and a significant mathematical theorem would be proved by machine.  These predictions came true (or approximately true) within 40 years rather than 10.  Simon’s overconfidence was due to the promising performance of early AI systems on simple examples.  In almost all cases, however, these early systems turned out to fail miserably when tried out on wider selections of problems and on more difficult problems.  The first kind of difficulty arose because most early programs knew nothing of their subject matter; they succeeded by means of simple syntactic manipulations.  The second kind of difficulty was the intractability of many of the problems that AI was attempting to solve.  A third difficulty arose because of some fundamental limitations on the basic structures being used to generate intelligent behavior.  Knowledge-based systems: The key to power?  The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions.  Called weak methods because - do not scale up to large or difficult problem instances.  Alternative - use more powerful, domain-specific knowledge that allows larger reasoning steps and can more easily handle typically occurring cases in narrow areas of expertise.  DENDRAL  The DENDRAL program (Buchanan etal.,1969) was an early example of this approach.  The significance of DENDRAL was that it was the first successful knowledge-intensive system: its expertise derived from large numbers of special- purpose rules.  Later systems also incorporated the main theme of McCarthy’s Advice Taker approach—the clean separation of the knowledge (in the form of rules) from the reasoning component.  Expert Systems  The Heuristic Programming Project (HPP) - investigate the extent to which the new methodology of expert systems could be applied to other areas of human expertise.  The next major effort was in EXPERT SYSTEMS the area of medical diagnosis.  Feigenbaum, Buchanan, and Dr. Edward Shortliffe developed MYCIN to diagnose blood infections.  With about 450 rules, MYCIN was able to perform as well as some experts, and considerably better than junior doctors. It also contained two major differences from DENDRAL.  First, unlike the DENDRAL rules, no general theoretical model existed from which the MYCIN rules could be deduced.  They had to be acquired from extensive interviewing of experts, who in turn acquired them from textbooks, other experts, and direct experience of cases.  Second, the rules had to reflect the uncertainty associated with medical knowledge. MYCIN incorporated a calculus of uncertainty called certainty factors which seemed (at the time) to fit well with how doctors assessed the impact of evidence on the diagnosis.  AI becomes an industry  The first successful commercial expert system, R1, began operation at the Digital Equipment Corporation (McDermott, 1982).  The program helped configure orders for new computer systems; by 1986, it was saving the company an estimated $40 million a year.  Overall, the AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988, including hundreds of companies building expert systems, vision systems, robots, and software and hardware specialized for these purposes.  The return of neural networks  In the mid-1980s at least four different groups reinvented the back-propagation algorithm first found in 1969 by Bryson and Ho.  learning problems in computer science and psychology.  As occurred with the separation of AI and cognitive science, modern neural network research has bifurcated into two fields,  one - creating effective network architectures and algorithms and understanding their mathematical properties,  the other - careful modeling of the empirical properties of actual neurons and ensembles of neurons.  AI adopts the scientific method  Recent years have seen a revolution in both the content and the methodology of work in artificial intelligence.  It is now more common to build on existing theories than to propose brand-new ones, to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and to show relevance to real-world applications rather than toy examples.  AI was founded in part as a rebellion against the limitations of existing fields like control theory and statistics, but now it is embracing those fields.  To be accepted, hypotheses must be subjected to rigorous empirical experiments, and the results must be analyzed statistically for their importance.  The field of speech recognition illustrates the pattern.  1970s, a wide variety - rather ad hoc and fragile, and were demonstrated on only a few specially selected examples.  In recent years, approaches based on hidden Markov models(HMMs) have come to dominate the area.  The emergence of intelligent agents  Perhaps encouraged by the progress in solving the subproblems of AI, researchers have also started to look at the “whole agent” problem again.  One of the most important environments for intelligent agents is the Internet.  AI systems have become so common in Web-based applications that the “-bot” suffix has entered everyday language.  The availability of very large data sets  Throughout the 60-year history of computer science, the emphasis has been on the algorithm as the main subject of study.  But some recent work in AI suggests that for many problems, it makes more sense to worry about the data and be less picky about what algorithm to apply.  This is true because of the increasing availability of very large data sources: for example, trillions of words of English and billions of images from the Web or billions of base pairs of genomic sequences.  The state of the art  Robotic vehicles  Speech recognition:  Autonomous planning and scheduling:  Game playing  Spam fighting  Logistics planning  Robotics:  Machine Translation: A computer program automatically translates from Arabic to English, allowing an English speaker to see the headline Intelligent Agents  Outline  Agents and environments  Rationality  PEAS (Performance measure, Environment, Actuators, Sensors)  Environment types  Agent types  Agents  An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators  Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators  Robotic agent: cameras and infrared range finders for sensors; various motors for actuators  Agents and environments  The agent function maps from percept histories to actions:  [f: P*  A]  The agent program runs on the physical architecture to produce f  agent = architecture + program  Vacuum-cleaner world  Percepts: location and contents, e.g., [A,Dirty]  Actions: Left, Right, Suck, No Op A vacuum-cleaner agent  Rational agents  An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful  Performance measure: An objective criterion for success of an agent's behavior  E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.   What is rational at any given time depends on four things:  The performance measure that defines the criterion of success.  The agent’s prior knowledge of the environment.  The actions that the agent can perform.  The agent’s percept sequence to date  Rational agents  Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.   Rational agents  Rationality is distinct from omniscience (all- knowing with infinite knowledge)  Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)  An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)  PEAS  PEAS: Performance measure, Environment, Actuators, Sensors  Must first specify the setting for intelligent agent design  Consider, e.g., the task of designing an automated taxi driver:  Performance measure  Environment  Actuators  Sensors  PEAS  Must first specify the setting for intelligent agent design  Consider, e.g., the task of designing an automated taxi driver:  Performance measure: Safe, fast, legal, comfortable trip, maximize profits  Environment: Roads, other traffic, pedestrians, customers  Actuators: Steering wheel, accelerator, brake, signal, horn  Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard  PEAS  Agent: Medical diagnosis system  Performance measure: Healthy patient, minimize costs, lawsuits  Environment: Patient, hospital, staff  Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)  Sensors: Keyboard (entry of symptoms, findings, patient's answers)  PEAS  Agent: Part-picking robot  Performance measure: Percentage of parts in correct bins  Environment: Conveyor belt with parts, bins  Actuators: Jointed arm and hand  Sensors: Camera, joint angle sensors  PEAS  Agent: Interactive English tutor  Performance measure: Maximize student's score on test  Environment: Set of students  Actuators: Screen display (exercises, suggestions, corrections)  Sensors: Keyboard  Environment types  Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time.  Fully observable environments are convenient because the agent need not maintain any internal state to keep track of the world.  An environment might be partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data—for example, a vacuum agent with only a local dirt sensor cannot tell whether there is dirt in other squares, and an automated taxi cannot see what other drivers are thinking.  If the agent has no sensors at all then the environment is unobservable.  Single agent vs. multiagent:  The distinction between single-agent and multiagent environments may seem simple enough. For example, an agent solving a crossword puzzle by itself is clearly in a single-agent environment, whereas an agent playing chess is in a two agent environment.  For example, in chess, the opponent entity B is trying to maximize its performance measure, which, by the rules of chess, minimizes agent A’s performance measure. Thus, chess is a competitive multiagent environment.  In the taxi-driving environment, on the other hand, avoiding collisions maximizes the performance measure of all agents, so it is a partially cooperative multiagent environment.  The agent-design problems in multiagent environments are often quite different from those in single-agent environments;  for example, communication often emerges as a rational behavior in multiagent environments; in some competitive environments, randomized behavior is rational because it avoids the pitfalls of predictability.  Deterministic vs. stochastic  If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic; otherwise, it is stochastic.  In principle, an agent need not worry about uncertainty in a fully observable, deterministic environment.  If the environment is partially observable, however, then it could appear to be stochastic.  Most real situations are so complex that it is impossible to keep track of all the unobserved aspects; for practical purposes, they must be treated as stochastic.  Eg: Taxi driving  Episodic vs Sequential Environment  Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.  In sequential environments, on the other hand, the current decision could affect all future decisions. Chess and taxi driving are sequential: in both cases, short-term actions can have long-term consequences.  Episodic environments are much simpler than sequential environments because the agent does not need to think ahead.  Static vs. dynamic  If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise, it is static.  Static environments are easy to deal with because the agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time.  If the environment itself does not change with the passage of time but the agent’s performance score does, then we say the environment is semidynamic.  Examples  Taxi driving is clearly dynamic:  the other cars and the taxi itself keep moving while the driving algorithm dithers about what to do next.  Chess, when played with a clock, is semidynamic.  Crossword puzzles are static  Discrete vs. continuous  The discrete/continuous distinction applies to the state of the environment, to the way time is handled, and to the percepts and actions of the agent.  For example, the chess environment has a finite number of distinct states (excluding the clock).  Chess also has a discrete set of percepts and actions.  Taxi driving is a continuous-state and continuous- time problem.  Known vs. Unknown  Strictly speaking, this distinction refers not to the environment itself but to the agent’s (or designer’s) state of knowledge about the “laws of physics” of the environment.  In a known environment, the outcomes (or outcome probabilities if the environment is stochastic) for all actions are given. Obviously, if the environment is unknown, the agent will have to learn how it works in order to make good decisions.  Environment types Chess with Chess without Taxi driving a clock a clock Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No Single agent No No No  The environment type largely determines the agent design  The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent. Task Environments and their characteristics  Agent functions and programs  Job of AI is to design an agent program that implements the agent function - the mapping from percepts to actions.  We assume this program will run on some sort of computing device with physical sensors and actuators—we call this the architecture.  agent = architecture + program.  One agent function (or a small equivalence class) is rational  Aim: find a way to implement the rational agent function concisely. Table Driven Agent  Table-driven agent  Drawbacks:  Must construct a table that contains the appropriate action for every possible percept sequence  Huge table  Take a long time to build the table  No autonomy  Even with learning, need a long time to learn the table entries Agent program for a vacuum-cleaner agent  Agent types  Four basic types in order of increasing generality:   Simple reflex agents  Model-based reflex agents  Goal-based agents  Utility-based agents Simple reflex agents  Simple Reflux agent  The simplest kind of agent is the simple reflex agent. These agents select actions on the basis of the current percept, ignoring the rest of the percept history.  Example: The vacuum agent  “The car in front is braking.” Then, this triggers some established connection in the agent program to the action “initiate braking.”  a connection as a condition–action rule,  if car-in-front-is-braking then initiate-braking. Simple reflex agents  The INTERPRET-INPUT function generates an abstracted description of the current state from the percept,  and the RULE-MATCH function returns the first rule in the set of rules that matches the given state description.  will work only if the correct decision can be made on the basis of only the current percept—that is, only if the environment is fully observable. Model-based reflex agents  Model-based reflex agents  The most effective way to handle partial observability is for the agent to keep track of the part of the world it can’t see now.  That is, the agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state.  Model-based reflex agents  Updating this internal state information as time goes by requires two kinds of knowledge to be encoded in the agent program.  First, we need some information about how the world evolves independently of the agent  For example, that an overtaking car generally will be closer behind than it was a moment ago.  Second, we need some information about how the agent’s own actions affect the world  For example, that when the agent turns the steering wheel clockwise, the car turns to the right.  This knowledge about “how the world works”— whether implemented in simple Boolean circuits or in complete scientific theories—is called a model of the world.  An agent that uses such a model is called a model- based agent. Model-based reflex agents  The interesting part is the function UPDATE-STATE, which is responsible for creating the new internal state description. The details of how models and states are represented vary widely depending on the type of environment and the particular technology used in the agent design.  the box labeled “what the world is like now” represents the agent’s “best guess”.  Uncertainty about the current state may be unavoidable, but the agent still has to make a decision. Goal-based agents  Knowing something about the current state of the environment is not always enough to decide what to do.  The agent needs some sort of goal information that describes situations that are desirable  The agent program can combine this with the model (the same information as was used in the model based reflex agent) to choose actions that achieve the goal.  Sometimes goal-based action selection is straightforward— for example, when goal satisfaction results immediately from a single action.  Sometimes it will be more tricky—for example, when the agent has to consider long sequences of twists and turns in order to find a way to achieve the goal.  Search and planning are the subfields of AI devoted to finding action sequences that achieve the agent’s goals  The goal-based agent’s behavior can easily be changed to go to a different destination, simply by specifying that destination as the goal Utility-based agents  Goals alone are not enough to generate high-quality behavior in most environments.  Goals just provide a crude binary distinction between “happy” and “unhappy” states.  A more general performance measure should allow a comparison of different world states according to exactly how happy they would make the agent.  Because “happy” does not sound very scientific, economists and computer scientists use the term utility instead.  UTILITY FUNCTION  An agent’s utility function is essentially an internalization of the performance measure. If the internal utility function and the external performance measure are in agreement, then an agent that chooses actions to maximize its utility will be rational according to the external performance measure.  Like goal-based agents, a utility-based agent has many advantages in terms of flexibility and learning.  A model-based, utility-based agent. It uses a model of the world, along with a utility function that measures its preferences among states of the world. Then it chooses the action that leads to the best expected utility, where expected utility is computed by averaging overall possible outcome states, weighted by the probability of the outcome. Learning agents  A learning agent can be divided into four conceptual components, as shown in Figure 2.15.  The most important distinction is between the learning element, which is responsible for making improvements, and the performance element, which is responsible for selecting external actions.  The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions.  The learning element uses feedback from the critic on how the agent is doing and determines how the performance element should be modified to do better in the future.  The last component of the learning agent is the problem generator. It is responsible for suggesting actions that will lead to new and informative experiences.  How the components of agent programs work  Roughly speaking, we can place the representations along an axis of increasing complexity and expressive power—atomic, factored, and structured. To illustrate these ideas, it helps to consider a particular agent component, such as the one that deals with “What my actions do.”  This component describes the changes that might occur in the environment as the result of taking an action, and Figure 2.16 provides schematic depictions of how those transitions might be represented.