Artificial Intelligence Introduction Dr. Anabia Sohail Instructor Information • Faculty Office 2, last room • https://www.researchgate.net/profile/Anabia-Sohail-2 Class Information • GCR Class Code: qr6cyeu • https://classroom.google.com/c/NDU4NzM4OTQzOTA0 • https://meet.google.com/pvy-yqvw-eso • Classes: Monday, Tuesday, Friday • Lab: Wednesday Marks Distribution Assessment Weightage Mid 25% Final 45% Quizzes 10% Class Activity, Participation, Behaviour 5% Assignments 5% Project 10% Textbook What is AI • Artificial Intelligence (AI) is the study and creation of machines that perform tasks normally associated with intelligence. • “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil) • “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight) • The focus of AI is to deal with the development of algorithms and machines that can perceive, reason and act like humans. Thus, AI find solutions to complex problems in a more human-like fashion. • AI learns by creating machine learning models based on provided inputs and desired outputs. Applications of AI • Web Search Engine • Google • Recommender Systems • Netflix, YouTube, Amazon • Voice Assistant System • Face Tagging • Biometric Identification Applications of AI • Medicine: • Image guided surgery • Image analysis and enhancement Applications of AI • Autonomous Planning & Scheduling: • Autonomous rovers • Autonomous vehicles Applications of AI • Games: • Robotic toys, Deep Blue, Alpha Go What is Artificial Intelligence ? • making computers that think? • the automation of activities we associate with human thinking, like decision making, learning ... ? • the art of creating machines that perform functions that require intelligence when performed by people ? • the study of mental faculties through the use of computational models ? What is Artificial Intelligence ? • the study of computations that make it possible to perceive, reason and act ? • a field of study that seeks to explain and emulate intelligent behaviour in terms of computational processes ? • a branch of computer science that is concerned with the automation of intelligent behaviour ? • anything in Computing Science that we don't yet know how to do properly ? (!) AI – Augmented Intelligence • Current view of AI is augmented AI • IBM Research defines Artificial Intelligence (AI) as Augmented Intelligence, helping experts scale their capabilities as machines do the time-consuming work. • AI can be understand as augmented intelligence • The objective of AI should not be to replace human experts, but rather extend human capabilities and accomplish tasks that neither humans nor machines could do on their own. AI – Augmented Intelligence • Augmented Intelligence is a subsection of AI machine learning that is developed to • Enhance human intelligence • Not to operate independently or to replace the humans • It is designed to do so by improving human decision-making and, by extension, actions taken in response to improved decisions. • In this way, augmented intelligence applications combine human and machine intelligence. It is important in systems where the risk of failure is too great or the AI is not evolved enough to take humans completely out of the equation. • Traditional view of AI is development of autonomous system, operating without the need for human involvement • Augmented Intelligence uses machine learning and deep learning to provide humans with actionable data Pillars of Artificial Intelligence • Human Like • Rational Major Methods to Define ‘Intelligence’ Intelligence • What is an Intelligent Being ? Criteria 1 • Humans • Rationale • Thought process ? Criteria 2 • Right Answer ? • Right Action ? • Thought Process • Action What is Artificial Intelligence ? THOUGHT Systems that think like humans Systems that act BEHAVIOUR like humans HUMAN Systems that think rationally Systems that act rationally RATIONAL Acting humanly: The Turing Test approach • The Turing Test was designed to provide a operational definition of intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks. • Test is that the computer should be interrogated by a human, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. • The computer would need to possess the following capabilities: • Natural language processing to enable it to communicate successfully • knowledge representation to store information provided before or during the interrogation • 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. Acting humanly: The Turing Test approach Thinking humanly: The cognitive modelling approach • If say that a given program thinks like a human, need to determine how humans think. • We need to get inside the actual workings of human minds. • There are two ways to do this: • through introspection • through psychological experiments Thinking rationally: The laws of thought approach • Aristotle’s syllogisms provided patterns for argument structures that always provide correct premises. • A famous example, “Socrates is a man; all men are mortal; therefore, Socrates is mortal.” • Another example – All TVs use energy; Energy always generates heat; therefore, all TVs generate heat.” • These arguments initiated the field called logic. Notations for statements for all kinds of objects were developed and interrelated between them to show logic. Acting rationally: The rational agent approach • Acting rationally means acting so as to achieve one's goals, given one's beliefs. An agent is just something that perceives and acts. Artificial Intelligence “The branch of computer science that is concerned with the automation of intelligent behavior” ~ Luger and Stublefield “The art of creating machines that perform functions that require intelligence when performed by people” ~ Kurzweil Main Constituents: • Perceive • Process/Reason (Rationale) • Actions • Feedback 2019-2023: 31% (7.22 Billion Dollar) Agents and environments • Artificial Intelligence (AI) is the study and creation of machines that perform tasks normally associated with intelligence. • An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. Agents and environments Agent Sensors Environment Percepts ? Actuators Actions • An agent perceives its environment through sensors and acts upon it through actuators (or effectors, depending on whom you ask) • The agent function maps percept sequences to actions • It is generated by an agent program running on a machine Agent Terminology • Percept − It is agent’s perceptual inputs at a given instance. • Percept Sequence − the complete history of everything the agent has perceived. • Behavior of Agent − It is the action that agent performs after any given sequence of percepts. • Agent Function − It is a map from the precept sequence to an action. • Agent program: The agent program runs on the physical architecture to produce f • Performance Measure of Agent − It is the criteria, which determines how successful an agent is. AI as Designing Rational Agents • An agent is an entity that perceives and acts. • A rational agent selects actions that maximize its expected utility. • RATIONALITY • AUTNOMICITY • Characteristics of the sensors, actuators, and environment dictate techniques for selecting rational actions • This course is about: • General AI techniques for many problem types • Learning to choose and apply the technique appropriate for each problem Rational Agent Vaccum Cleaner Agent Vaccum Cleaner Agent Example PEAS Environment • An environment in artificial intelligence is the surrounding of the agent. The agent takes input from the environment through sensors and delivers the output to the environment through actuators. • There are several types of environments: • • • • • • Fully Observable vs Partially Observable Deterministic vs Stochastic Competitive vs Collaborative Single-agent vs Multi-agent Static vs Dynamic Discrete vs Continuous Environment Types • Fully Observable vs Partially Observable • Agent sensor is capable to sense or access the complete state of an agent at each point in time, it is said to be a fully observable environment else it is partially observable. • An environment is called unobservable when the agent has no sensors in all environments. • Example: • Chess – the board is fully observable, so are the opponent’s moves • Driving – the environment is partially observable because what’s around the corner is not know. Environment Types • Deterministic vs Stochastic • Agent’s current state completely determines the next state of the agent, the environment is said to be deterministic. • The stochastic environment is random in nature which is not unique and cannot be completely determined by the agent. • Example: Chess – there would be only a few possible moves for a coin at the current state and these moves can be determined Self Driving Cars – the actions of a self-driving car are not unique, it varies time to time Environment Types • Competitive vs Collaborative • An agent is said to be in a competitive environment when it competes against another agent to optimize the output. • The game of chess is competitive as the agents compete with each other to win the game which is the output. • An agent is said to be in a collaborative environment when multiple agents cooperate to produce the desired output. • When multiple self-driving cars are found on the roads, they cooperate with each other to avoid collisions and reach their destination which is the output desired. Environment Types • Single-agent vs Multi-agent • An environment consisting of only one agent is said to be a singleagent environment. • A person left alone in a maze is an example of the single-agent system. • An environment involving more than one agent is a multi-agent environment. • The game of football is multi-agent as it involves 11 players in each team. Environment Types • Dynamic vs Static • An environment that keeps constantly changing itself when the agent is up with some action is said to be dynamic. • A roller coaster ride is dynamic as it is set in motion and the environment keeps changing every instant. • An idle environment with no change in its state is called a static environment. • An empty house is static as there’s no change in the surroundings when an agent enters. Environment Types • Discrete vs Continuous • If an environment consists of a finite number of actions that can be deliberated in the environment to obtain the output, it is said to be a discrete environment. • The game of chess is discrete as it has only a finite number of moves. The number of moves might vary with every game, but still, it’s finite. • The environment in which the actions performed cannot be numbered ie. is not discrete, is said to be continuous. • Self-driving cars are an example of continuous environments as their actions are driving, parking, etc. which cannot be numbered. Agents The task environment - PEAS • Performance measure • -1 per step; + 10 food; +500 win; -500 die; +200 hit scared ghost • Environment • Pacman dynamics (incl ghost behavior) • Actuators • Left Right Up Down or NSEW • Sensors • Entire state is visible (except power pellet duration) PEAS: Automated taxi • Performance measure • Environment • Actuators • Sensors Image: http://nypost.com/2014/06/21/how-googlemight-put-taxi-drivers-out-of-business/ PEAS: Medical diagnosis system • Performance measure • Environment • Actuators • Sensors Agent Types Simple reflex agents Agent Sensors What the world is like now Environment Condition-action rules What action I should do now Actuators Reflex agents with state Sensors State How the world evolves What my actions do Condition-action rules Agent What action I should do now Actuators Environment What the world is like now Goal-based agents Sensors State What my actions do What it will be like if I do action A Goals What action I should do now Agent Actuators Environment How the world evolves What the world is like now Utility-based agent Environment types Pacman Fully or partially observable Single-agent or multiagent Deterministic or stochastic Static or dynamic Discrete or continuous Known physics? Known perf. measure? Diagnosis Taxi Agent design • The environment type largely determines the agent design • • • • • • • Partially observable => agent requires memory (internal state) Stochastic => agent may have to prepare for contingencies Multi-agent => agent may need to behave randomly Static => agent has time to compute a rational decision Continuous time => continuously operating controller Unknown physics => need for exploration Unknown perf. measure => observe/interact with human principal
