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Artificial Intelligence Introduction Ms. Amna Khan Text Book Text Book 1: Artificial Intelligence: A Modern Approach 3 edition. Authors: Stuart Russell and Peter Norvig. ISBN-13: 978-0136042594 Text Book 2: Artificial Intelligence: Structures and Strategies for Complex Problem Solving 6th edition. Authors: George F luger. rd Grading What is artificial intelligence? Popular conception driven by science fiction ◦ Robots good at everything except emotions, empathy, appreciation of art, culture, … ◦ … until later in the movie. ◦ Current AI is also bad at lots of simpler stuff! ◦ There is a lot of AI work on thinking about what other agents are thinking Real AI A serious science. General-purpose AI like the robots of science fiction is incredibly hard ◦ Human brain appears to have lots of special and general functions, integrated in some amazing way that we really do not understand at all (yet) Special-purpose AI is more doable (nontrivial) ◦ E.g., chess/poker playing programs, logistics planning, automated translation, voice recognition, web search, data mining, medical diagnosis, keeping a car on the road, … … … … Examples of Artificial Intelligence You’re Using in Daily Life Virtual Personal Assistants Siri, Google Now, and Cortana are all intelligent digital personal assistants on various platforms (iOS, Android, and Windows Mobile). Voice Recognition Enabled : Help find useful information when you ask for it using your voice; you can say “Where’s the nearest mart?”, “What’s on my schedule today?”, “Remind me to call Jerry at eight o’clock,” and the assistant will respond by finding information, relaying information from your phone, or sending commands to other apps. Examples of Artificial Intelligence You’re Using in Daily Life Video Games One of the instances of AI that most people are probably familiar with, video game AI has been used for a very long time—since the very first video games. But the complexity and effectiveness of that AI has increased exponentially over the past several decades, resulting in video game characters that learn your behaviors, respond to stimuli, and react in unpredictable ways. 2014’s Middle Earth: Shadow of Mordor is especially notable for the individual personalities given to each non-player character, their memories of past interaction, and their variable objectives. Examples of Artificial Intelligence You’re Using in Daily Life First-person shooters like Far Cry and Call of Duty also make significant use of AI, with enemies that can analyze their environments to find objects or actions that might be beneficial to their survival; they’ll take cover, investigate sounds, use flanking maneuvers, and communicate with other AIs to increase their chances of victory. As far as AI goes, video games are somewhat simplistic, but because of the industry’s huge market, a great deal of effort and money are invested every year in perfecting this type of AI. Examples of Artificial Intelligence You’re Using in Daily Life Smart Cars You probably haven’t seen someone reading the newspaper while driving to work yet, but self-driving cars are moving closer and closer to reality; Google’s self-driving car project and Tesla’s “autopilot” feature are two examples that have been in the news lately. Google has developed an algorithm that could potentially let self-driving cars learn to drive in the same way that humans do: through experience. Examples of Artificial Intelligence You’re Using in Daily Life Purchase Prediction Large retailers like Target and Amazon stand to make a lot of money if they can anticipate your needs. Amazon’s anticipatory shipping project hopes to send you items before you need them, completely obviating the need for a last-minute trip to the online store. While that technology isn’t yet in place, brick-andmortar retailers are using the same ideas with coupons; when you go to the store, you’re often given a number of coupons that have been selected by a predictive analytics algorithm. Examples of Artificial Intelligence You’re Using in Daily Life Smart Home Devices Many smart home devices now include the ability to learn your behavior patterns and help you save money by adjusting the settings on your thermostat or other appliances in an effort to increase convenience and save energy. For example, turning your oven on when you leave work instead of waiting to get home is a very convenient ability. A thermostat that knows when you’re home and adjusts the temperature accordingly can help you save money by not heating the house when you’re out. Definitions of AI Focus on action avoids philosophical issues such as “is the system conscious” etc. if our system can be more rational than humans in some cases, why not? Systems that think Systems that think like humans rationally Systems that act like humans Systems that act rationally Definitions of AI • We will follow “act rationally” approach – Distinction may not be that important • acting rationally/like a human presumably requires (some sort of) thinking rationally/like a human, • humans much more rational anyway in complex domains Lessons from AI research Clearly-defined tasks that we think require intelligence and education from humans tend to be doable for AI techniques ◦ Playing chess, drawing logical inferences from clearly-stated facts, performing probability calculations in well-defined environments, … ◦ Although, scalability can be a significant issue Complex, messy, ambiguous tasks that come natural to humans (in some cases other animals) are much harder ◦ Recognizing your grandmother in a crowd, drawing the right conclusion from an ungrammatical or ambiguous sentence, driving around the city, … Lessons from AI research Humans better at coming up with reasonably good solutions in complex environments Humans better at adapting/selfevaluation/creativity (“My usual strategy for chess is getting me into trouble against this person… Why? What else can I do?”) Turing Test (Human) judge communicates with a human and a machine over text-only channel, Both human and machine try to act like a human, Judge tries to tell which is which. Numerous variants image from http://en.wikipedia.org/wiki/Turing_test Turing Test on unsuspecting judges It is possible to (temporarily) fool humans who do not realize they may be talking to a bot ELIZA program [Weizenbaum 66] rephrases partner’s statements and questions (~psychotherapist) Turing Test The Computer would need to possess the following capabilities: Natural language processing to enable it to communicate successfully in English (or some other human language) 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. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind. 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. (This may be an unusual use of the word, but you will get used to it.) In this approach, AI is viewed as the study and construction of rational agents. Acting rationally: The rational agent approach Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to reason logically to the conclusion that a given action will achieve one's goals, and then to act on that conclusion. On the other hand, correct inference is not all of rationality, because there are often situations where there is no provably correct thing to do, yet something must still be done. Acting rationally: The rational agent approach There are also ways of acting rationally that cannot be reasonably said to involve inference. For example, pulling one's hand off of a hot stove is a reflex action that is more successful than a slower action taken after careful deliberation. AI as rational agent :advantages First, it is more general than the "laws of thought" approach, because correct inference is only a useful mechanism for achieving rationality, and not a necessary one. Second, it is more amenable to scientific development than approaches based on human behavior or human thought, because the standard of rationality is clearly defined and completely general. Robots vs. Other Intelligent Agents •In AI, artificial agents that have a physical presence in the world are usually known as robots ◦ Robotics is the field primarily concerned with the implementation of the physical aspects of a robot ◦ I.e., perception of and action in the physical environment ◦ Sensors and actuators • Agents without a physical presence: software agents ◦ E.g. desktop assistants, decision support systems, web crawlers, text-based translation systems, intelligent tutoring systems, etc ◦ They also interact with an environment, but not the physical world •Software agents and robots ◦ differ in their interaction with the environment ◦ share all other fundamental components of intelligent behavior 24 Intelligent Agents in the World Knowledge Representation Machine Learning abilities Reasoning + Decision Theory Natural Language Generation Natural Language Understanding + Computer Vision Speech Recognition + Physiological Sensing Mining of Interaction Logs + Robotics + Human Computer /Robot Interaction Representation and Reasoning To use these inputs an agent needs to represent them knowledge One of AI goals: specify how a system can Acquire and represent knowledge about a domain (representation) • Use the knowledge to solve problems in that domain (reasoning) Representation & Reasoning Representation and Reasoning (R&R) System •A representation language to describe ◦ The environment ◦ Problems (questions/tasks) to be solved •Computational reasoning procedures to compute a solution to a problem ◦ E.g., an answer, sequence of actions problem ⟹ representation ⟹ computation⟹ representation ⟹ solution •Choice of an appropriate R&R system depends on various dimensions, e.g. properties of • the environment, the type of problems, the agent, the computational resources, etc. 27 Representational Dimensions Environment Problem Type Static Sequential Deterministic Stochastic We’ll start by Each cell will include a R&R describing system covered in the dimensions courserelated to the problem and environment Then we’ll include in each Then cellwe’ll the various include R&R in each cell systems R&R system coveredcovered in the course,inand the discuss course some more dimensions