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CS 621 Artificial Intelligence Lecture 1 – 28/07/05 Prof. Pushpak Bhattacharyya Introduction 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 1 AI Introduction Language Processing Expert System Planning Search Reasoning Learning Knowledge Representation Vision Robotics 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 2 Thinking Ability in Machines “Can machines think” 320 BC Aristotle – Syllogism (Disjunctive Reasoning) ex: All men are mortal. Shakespeare is a man. Shakespeare is mortal. Inductive Reasoning – specific general (difficult for machine) ex: Crows in Bhopal are black. Crows in Mumbai are black. All crows are black. Abductive Learning - p q if Q is true then P is true in absence of any other info. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 3 Turing Machine Finite state control Infinite tape Church-Turing Hypothesis Anything that is computable, is computable by a TM & viceversa. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 4 Turing Test Human Computer Interrogator has to find which is a machine and which is a human by asking questions to both of them. If the machine is able to fool the interrogator to behave like a human, then that machine passes the Turing Test. Interrogator 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 5 Machines Gene/DNA: A string of bits which is a coded instruction. ANIMATE THEORY: Intelligence is producable only on carbon base. Silicon intelligence is not possible. PHYSICAL SYMBOL HYPOTHESIS: (opposed to Animate theory): Intelligence emerges from manipulating symbols & nothing else is needed. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 6 Levels in AI Knowledge level (Disambiguation) ● Symbol level (e.g. Lisp program) ● Signal level (e.g. Neural Net) ● AI Science (Cognitive Sc, Psychology, social science, physics Sci + Engg (theory + code) Engineering 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 7 Search 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 8 Search is Fundamental and Ubiquitous Fundamental in all of AI. In planning : A B C B A C Table At every stage: multiple possibilities, search for the best possible sequence of options. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 9 In Vision R World 28-07-05 L Two eye system Left eye retina & right eye retina have pixels activated. Search for corresponding points: points in the left eye should correspond to points in the right eye – O(n2) algorithm. Prof. Pushpak Bhattacharyya, IIT Bombay 10 In Language Processing The man would like to play. Noun Verb Prep Verb Noun Verb System has to search amongst the possibilities to get the correct meaning. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 11 Abstractions of Search State state space ● Operators transform states ● Cost associated operations ● Start node ● Goal node (S) ● Minimal cost path ● 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 12 Classical Example 8-puzzle 4 2 5 1 2 3 3 1 6 4 5 6 7 7 8 8 Goal Start (S0) State: Matrix repersentation of the board. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 13 Operators Here operators are movements of blank space. MU 4 3 8 28-07-05 2 5 1 6 MR 7 ML Prof. Pushpak Bhattacharyya, IIT Bombay 4 3 8 2 1 4 3 8 2 1 4 3 8 2 1 5 6 7 5 6 7 5 6 7 14 Cost Cost of each operation = 1 for this problem. Which path leads to the goal – Uninformed search (Breadth first, Depth first) ● Heuristic Search (A* algorithm) ● 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 15 Search All searchs are instantiations of “General Graph Search” (GGS) algo. Input: Output: The state space graph (implicit/explicit). The path to the goal node. Data Structures: 1. Open list (OL) 2. Closed list (CL) 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 16 Example Start A 1 3 B 4 D C 6 2 E 9 8 F State space graph 28-07-05 3 OL CL :A : OL CL :BCD :A OL CL :CDE :AB OL CL :DEF :ABC OL CL :EFG :ABCD G Prof. Pushpak Bhattacharyya, IIT Bombay 17 KNOWLEDGE REPRESENTATION & INFERENCING USING PREDICATE CALCULUS 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 18 Example Example: John, Jack & Jill are members of Alpine club. Every member if the club is either a mountain climber or a skier. All skiers like snow. No mountain climber likes rain. Jack dislikes whatever John likes, and likes whatever John dislikes. John likes rain and snow. Is there a member who is a mountain climber but not a skier. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 19 Knowledge Representation 1. member (John, Alpine) 2. member (Jack, Alpine) 3.member (Jill, Alpine) sk(x)] 4. x [member(x, Alpine) → mc(x) 5. x [sk(x) → like(x, snow)] 6. x [mc(x) → ~like(x, rain)] 7. x [like(John, x) → ~like(John, x)] 8. x [~like(John, x) → like(Jack, x)] 9. like(John, rain) 10. like(John, snow) Ques: x [member(x, Alpine) 28-07-05 mc(x) Prof. Pushpak Bhattacharyya, IIT Bombay ~sk(x)] 20 Inference Strategy - RESOLUTION Basic Idea: REFUTATION of the goal Proof by contradiction Suppose the goal is false. Then show contradiction in the knowledge base. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 21 Steps in Inferencing Convert all expressions, including the falsified goal, into clauses. 1. member (John, Alpine) 2. member (Jack, Alpine) 3. member (Jill, Alpine) 4. ~ member(x1, Alpine) ν mc(x1) ν sk(x1) 5. ~ sk(x2) ν like(x2,snow) 6. ~ mc(x3) ν ~ like(x3,rain) 7. ~ like(John, x4) ν ~ like(Jack, x4) 8. like(John, x5) ν like(Jack, x5) 9. like(John, rain) 10. like(John, snow) 11. ~ member(x6, Alpine) ν ~ mc(x6) ν sk(x6) 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 22 Run Resolution By unification find value for x6 Theory of resolution: given P & ~P ν Q Resolvents we can obtain Q (Resolute) 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 23 Inverted Tree Diagram ~P ν Q P Q Aim C1 C2 Indicates contradiction null clause 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 24 Goal with Negation ~[ x{(member (x, Alpine) Λ mc(x) Λ ~ sk(x))}] x[~ member (x, Alpine) ν ~ mc(x) ν sk(x)] 11. ~ member(x6, Alpine) ν ~ mc(x6) ν sk(x6) 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 25 Monotonic Logic Every step of resolution increases the knowledge base monotonically. Non-monotonic logic which used default reasoning. NEGATION BY FAILURE 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 26 Modus Ponens & Modus Tolens Modus Ponen: P & P →Q gives Q Modus Tolens: ~Q and P →Q gives ~P 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 27 Prolog Predicate calculus (HORN Clause) + Resolution HORN Clauses: All the implications have a single literal as consequent. A(antecedent) → B(consequent) B is a single literal, never contain any operator. Moreover B has to be a positive literal. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 28 Summary • AI is a fascinating discipline, needing input from many branches of knowledge. • Scaling up and robustness are the needs of today’s world. • Web has introduced new challenges to the field. • Language processing and machine learning have assumed great importance. • In this lecture we took a look at two core areas: search and reasoning, which will be developed further. • Will delve into other areas as the course progresses. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 29 Reading Material • Basic Text Books – Russell S, Norvig P (1995) Artificial Intelligence: A Modern Approach, Prentice Hall Series in Artificial Intelligence. Englewood Cliffs, New Jersey – Nilsson N. J. (1980) Principles of Artificial Intelligence, Morgan Kaufmann Publishers Inc. • Journals – AI, IEEE SMC, Machine Learning, Computational Linguistics • Proceedings of – AAAI, ECAI, IJCAI, ICML, ACL, COLING etc. etc. 28-07-05 Prof. Pushpak Bhattacharyya, IIT Bombay 30