Artificial Intelligence

1. Artificial Intelligence Lecture 1 – AI Background Dr. Muhammad Adnan Hashmi

2. Profile & Coordinates • Profile: • Name: Dr. Muhammad Adnan Hashmi • 2005: BSc (Hons.) in CS – University of the Punjab, Lahore, Pakistan • 2007: MS in Multi-Agent Systems– University Paris 5, Paris, France • 2012: PhD in Artificial Intelligence – University Paris 6, Paris, France. • Coordinates: • Email: adnan.hashmi@ciitlahore.edu.pk

3. Course Textbooks • Primary Book: • Artificial Intelligence: A Modern Approach (AIMA) • Authors: Stuart Russell and Peter Norvig (3rd Ed.) • Advisable that each student should purchase a copy of this book • Reference Book: • Artificial Intelligence (Fourth Edition) by George F Luger

4. Course Objectives • Provide a concrete grasp of the fundamentals of various techniques and branches that currently constitute the field of Artificial Intelligence, e.g., • Search • Knowledge Representation • Autonomous planning • Machine learning • Robotics etc.

5. Outline • Course overview • What is AI? • A brief history of AI • The state of the art of AI

6. Course overview • Introduction and Agents (Chapters 1,2) • Search (Chapters 3,4,5,6) • Logic (Chapters 7,8,9) • Planning (Chapters 11,12) • Learning (Chapters 18,20)

7. What is AI? • Views of AI fall into four categories: • Systems that act like humans • Systems that think like humans • Systems that act rationally • Systems that think rationally • In this course, we are going to focus on systems that act rationally, i.e., the creation, design and implementation of rational agents.

8. Acting Humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning.

9. Thinking Humanly: Cognition • 1960s “Cognitive Revolution": Information-processing psychology replaced prevailing orthodoxy of behaviorism • Requires scientific theories of internal activities of the brain. How to validate? • Cognitive Science: Predicting and testing behavior of human subjects • Cognitive Neuroscience: Direct identification from neurological data • Both approaches are now distinct from AI, and share with AI the following characteristic: • The available theories do not explain (or engender) anything resembling human-level general intelligence.

10. Thinking Rationally: “Laws of Thought" • Normative rather than descriptive • Aristotle: What are correct thought processes? • Several Greek schools developed various forms of logic: • Notation and rules of derivation for thoughts (this may or may not have proceeded to the idea of mechanization) • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or otherwise) that I could have?

11. Acting rationally: rational agent • Rational behavior: doing the right thing • The right thing: the optimal (best) thing that is expected to maximize the chances of achieving a set of goals, in a given situation • Doesn't necessarily involve thinking, but a rational agent should be able to demonstrate it artificially, in moving towards its goal • Aristotle (Nicomachean Ethics): • Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good.

12. Rational Agents • An agent is an entity that perceives and acts • This course is about designing rational/intelligent agents • Abstractly, an agent is a function from percept histories to actions: • f : P* -> A • For any given class of environments and tasks, we seek the agent (or class of agents) with the optimal (best) performance • Caveat: computational limitations make perfect rationality unachievable • So we attempt to design the best (most intelligent) program, under the given resources.

13. AI Prehistory • Philosophy: Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality • Mathematics: Formal representation and proof, Algorithms, Computation, (un)decidability, (in)tractability, probability • Psychology: Adaptation, phenomena of perception and motor control, experimental techniques (with animals, etc.) • Economics: Formal theory of rational decisions • Linguistics: Knowledge representation, grammar • Neuroscience: Plastic physical substrate for mental activity • Control theory: Homeostatic systems, Stability, Simple optimal agent designs.

14. Abridged history of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth: "Artificial Intelligence“ adopted • 1952-69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, • 1965 Robinson's algo for logical reasoning • 1966-73 AI discovers computational complexity Neural network research almost disappears • 1969-79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents.

15. State of the art • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • 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 • Proverb solves crossword puzzles better than most humans.

16. Natural Language Processing • Speech technologies • Automatic speech recognition (ASR) • Text-to-speech synthesis (TTS) • Dialog systems • Language Processing Technologies • Machine Translation • Information Extraction • Informtation Retrieval • Text classification, Spam filtering.

17. Robotics

18. Others.. • Computer Vision: • Object and Character Recognition • Image Classification • Scenario Reconstruction etc. • Game-Playing • Strategy/FPS games, Deep Blue etc. • Logic-based programs • Proving theorems • Reasoning etc.

19. Questions