Introduction to Artificial Intelligence, Summaries of Artificial Intelligence

Download Introduction to Artificial Intelligence and more Summaries Artificial Intelligence in PDF only on Docsity! Introduction to Artificial Intelligence Kalev Kask ICS 271 Fall 2015 271-fall 2015 http://www.ics.uci.edu/~kkask/Fall-2015 CS271/ Course requirements Assignments: • There will be weekly homework assignments, a project, a final. Course-Grade: • Homework will account for 20% of the grade, project 30%, final 50% of the grade. . Discussion: • Optional. Fri 9:00-10:50 ICS 174. 271-fall 2015 Resources on the internet Resources on the Internet • AI on the Web: A very comprehensive list of Web resources about AI from the Russell and Norvig textbook. Essays and Papers • What is AI, John McCarthy • Computing Machinery and Intelligence, A.M. Turing • Rethinking Artificial Intelligence, Patrick H.Winston • AI Topics: http://aitopics.net/index.php 271-fall 2015 Today’s class • What is Artificial Intelligence? • A brief History • State of the art • Intelligent agents 271-fall 2015 Today’s class • What is Artificial Intelligence? • A brief History • Intelligent agents • State of the art 271-fall 2015 What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally“ 271-fall 2015 How to simulate humans intellect and behavior by a machine. Mathematical problems (puzzles, games, theorems) Common-sense reasoning Expert knowledge: lawyers, medicine, diagnosis Social behavior The Turing Test (Can Machine think? A. M. Turing, 1950) • Requires: – Natural language – Knowledge representation – Automated reasoning – Machine learning – (vision, robotics) for full test 271-fall 2015 http://aitopics.net/index.php http://amturing.acm.org/acm_tcc_webcasts.cfm Acting/Thinking Humanly/Rationally • Turing test (1950) • Requires: – Natural language – Knowledge representation – automated reasoning – machine learning – (vision, robotics.) for full test • Methods for Thinking Humanly: – Introspection, the general problem solver (Newell and Simon 1961) – Cognitive sciences • Thinking rationally: – Logic – Problems: how to represent and reason in a domain • Acting rationally: – Agents: Perceive and act 271-fall 2015 Today’s class • What is Artificial Intelligence? • A brief history • State of the art • Intelligent agents 271-fall 2015 Histroy of AI 271-fall 2015  McCulloch and Pitts (1943)  Neural networks that learn  Minsky and Edmonds (1951)  Built a neural net computer  Darmouth conference (1956):  McCarthy, Minsky, Newell, Simon met,  Logic theorist (LT)- Of Newell and Simon proves a theorem in Principia Mathematica-Russel.  The name “Artficial Intelligence” was coined.  1952-1969 (early enthusiasm, great expectations)  GPS- Newell and Simon  Geometry theorem prover - Gelernter (1959)  Samuel Checkers that learns (1952)  McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution  Microworlds: Integration, block-worlds.  1962- the perceptron convergence (Rosenblatt) The Birthplace of “Artificial Intelligence”, 1956 • Darmouth workshop, 1956: historical meeting of the precieved founders of AI met: John McCarthy, Marvin Minsky, Alan Newell, and Herbert Simon. • A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." And this marks the debut of the term "artificial intelligence.“ • 50 anniversery of Darmouth workshop • List of AI-topics 271-fall 2015 State of the art • Game Playing: Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • Robotics vehicles: – 2005 Standford robot won DARPA Grand Challenge, driving autonomously 131 miles along unrehearsed desert trail – Staneley (Thrun 2006). No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) – 2007 CMU team won DARPA Urban Challenge driving autonomously 55 miles in a city while adhering to traffic laws and hazards – Self-driving cars (Google, etc.) • Autonomous planning and scheduling: – During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people – NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft • Speech recognition (e.g. Siri, …) • DARPA grand challenge 2003-2005, Robocup • Machine translation (From English to Arabic, 2007) • Natural language processing: Watson won Jeopardy (Natural language processing), IBM 2011. 271-fall 2015 Robotic links • Deep Blue: http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer) • Robocup Video – Soccer Robocupf • Darpa Challenge – Darpa’s-challenge-video • Watson • http://www.youtube.com/watch?v=seNkjYyG3gI 271-fall 2015 Today’s class • What is Artificial Intelligence? • A brief History • State of the art • Intelligent agents 271-fall 2015 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 271-fall 2015 What’s involved in Intelligence? • Ability to interact with the real world – to perceive, understand, and act – e.g., speech recognition and understanding and synthesis – e.g., image understanding – e.g., ability to take actions, have an effect • Knowledge Representation, Reasoning and Planning – modeling the external world, given input – solving new problems, planning and making decisions – ability to deal with unexpected problems, uncertainties • Learning and Adaptation – we are continuously learning and adapting – our internal models are always being “updated” • e.g. a baby learning to categorize and recognize animals 271-fall 2015 Implementing agents • Table look-ups, Model-based, Goal-oriented, Utility, Learning • Autonomy – All actions are completely specified – no need in sensing, no autonomy – example: Monkey and the banana • Structure of an agent – agent = architecture + program – Agent examples • medical diagnosis • Satellite image analysis system • part-picking robot • Interactive English tutor • cooking agent • taxi driver 271-fall 2015 Rationality Fixed performance measure evaluates the environment sequence — one point per square cleaned up in time T'? — one point per clean square per time step, minus one per move? — penalize for > k dirty squares? A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date Rational 4 omniscient Rational + clairvoyant Rational 4 successful Rational => exploration, learning, autonomy Chapter 2 T 271-fall 2015 Task Environment 271-fall 2015 • Before we design a rational agent, we must specify its task environment: PEAS: Performance measure Environment Actuators Sensors PEAS 271-fall 2015 • Example: Agent = 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 271-fall 2015 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. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) • Episodic (vs. sequential): An agent’s action is divided into atomic episodes. Decisions do not depend on previous decisions/actions. Environment Types 271-fall 2015 • Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does) • Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. How do we represent or abstract or model the world? • Single agent (vs. multi-agent): An agent operating by itself in an environment. Does the other agent interfere with my performance measure? Environment types Solitaire Backgammon _ Internet shopping Taxi Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? 271-fall 2015 Chapter 2  Environment types Single-agent?? Solitaire Backgammon Internet shopping Taxi Observable?? Yes Yes No No Deterministic?? Yes No Partly No Episodic?? No No No No Static?? Discrete?? 271-fall 2015 Chapter? 14  Environment types Solitaire Backgammon Internet shopping Taxi Observable?? Yes Yes No No Deterministic?? Yes No Partly No Episodic? No No No No Static?? Yes Semi Semi No Discrete?? Single-agent?? 271-fall 2015 Chapter 2  Environment types Solitaire Backgammon Internet shopping Taxi Observable?? Yes Yes No No Deterministic?? Yes No Partly No Episodic?? No No No No Static?? Yes Semi Semi No Discrete?? Yes Yes Yes No Single-agent?? 271-fall 2015 Chapter 2 Simple reflex agents example: vacuum cleaner world NO MEMORY Fails if environment is partially observable Model-based reflex agents Model the state of the world by: modeling how the world changes how it’s actions change the world description of current world state •This can work even with partial information •It’s is unclear what to do without a clear goal Goal-based agentsGoals provide reason to prefer one action over the other. We need to predict the future: we need to plan & search