1. CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–1: Introduction 2 nd January. 2012

2. Basic Facts Faculty instructor: Dr. Pushpak Bhattacharyya (www.cse.iitb.ac.in/~pb)www.cse.iitb.ac.in/~pb TAs: Akshat, Yogesh, Ankit and Bibek akshatmalu@cse.iitb.ac.in, yogesh@cse.iitb.ac.in, ankitr@cse.iitb.ac.in, "Bibek Behera" bibek.iitkgp@gmail.combibek.iitkgp@gmail.com Course home page http://www.cse.iitb.ac.in/~cs344-2012 Venue: SIC301, KR Building Slot 3 Associated lab: cs386 (in old s/w lab)

3. Perspective

4. From Wikipedia Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Textbooks define the field as "the study and design of intelligent agents" [1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. [2]intelligence computer scienceintelligent agents [1] [2] John McCarthyJohn McCarthy, who coined the term in 1956, [3] defines it as "the science and engineering of making intelligent machines." [4] [3] [4] AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. [10] [10] The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. [11] General intelligence (or "strong AI") is still a long-term goal of (some) research. [12] [11]strong AI [12]

5. Usual Topics in an AI course Search General Graph Search, A*, Admissibility, Monotonicity Iterative Deepening, α-β pruning, Application in game playing Logic Formal System, axioms, inference rules, completeness, soundness and consistency Propositional Calculus, Predicate Calculus, Fuzzy Logic, Description Logic, Web Ontology Language Knowledge Representation Semantic Net, Frame, Script, Conceptual Dependency Machine Learning Decision Trees, Neural Networks, Support Vector Machines, Self Organization or Unsupervised Learning

6. Other Topics Evolutionary Computation Genetic Algorithm, Swarm Intelligence Probabilistic Methods Hidden Markov Model, Maximum Entropy Markov Model, Conditional Random Field IR and AI Modeling User Intention, Ranking of Documents, Query Expansion, Personalization, User Click Study Planning Deterministic Planning, Stochastic Methods Man and Machine Natural Language Processing, Computer Vision, Expert Systems Philosophical Issues Is AI possible, Cognition, AI and Rationality, Computability and AI, Creativity

7. Disciplines which form the core of AI- inner circle Fields which draw from these disciplines- outer circle. Planning Computer Vision NLP Expert Systems Robotics Search, Reasoning, Learning

8. AI as the forcing function Time sharing system in OS Machine giving the illusion of attending simultaneously with several people Compilers Raising the level of the machine for better man machine interface Arose from Natural Language Processing (NLP) NLP in turn called the forcing function for AI

9. Allied Disciplines PhilosophyKnowledge Rep., Logic, Foundation of AI (is AI possible?) MathsSearch, Analysis of search algos, logic EconomicsExpert Systems, Decision Theory, Principles of Rational Behavior PsychologyBehavioristic insights into AI programs Brain ScienceLearning, Neural Nets PhysicsLearning, Information Theory & AI, Entropy, Robotics Computer Sc. & Engg.Systems for AI

10. Goal of Teaching the course Concept building: firm grip on foundations, clear ideas Coverage: grasp of good amount of material, advances Inspiration: get the spirit of AI, motivation to take up further work Man Machine Synergy

12. A puzzle (Zohar Manna, Mathematical Theory of Computation, 1974) From Propositional Calculus

13. Tourist in a country of truth- sayers and liers Facts and Rules: In a certain country, people either always speak the truth or always lie. A tourist T comes to a junction in the country and finds an inhabitant S of the country standing there. One of the roads at the junction leads to the capital of the country and the other does not. S can be asked only yes/no questions. Question: What single yes/no question can T ask of S, so that the direction of the capital is revealed?

14. Diagrammatic representation S (either always says the truth Or always lies) T (tourist) Capital

15. Deciding the Propositions: a very difficult step- needs human intelligence P: Left road leads to capital Q: S always speaks the truth

16. Meta Question: What question should the tourist ask The form of the question Very difficult: needs human intelligence The tourist should ask Is R true? The answer is “yes” if and only if the left road leads to the capital The structure of R to be found as a function of P and Q

17. A more mechanical part: use of truth table PQS’s Answer R TTYesT TF F FTNoF FF T

18. Get form of R: quite mechanical From the truth table R is of the form (P x-nor Q) or (P ≡ Q)

19. Get R in English/Hindi/Hebrew… Natural Language Generation: non-trivial The question the tourist will ask is Is it true that the left road leads to the capital if and only if you speak the truth? Exercise: A more well known form of this question asked by the tourist uses the X-OR operator instead of the X-Nor. What changes do you have to incorporate to the solution, to get that answer?

20. Answer to the question “S speaks the truth” : “S does not speak the truth” : Left road does not lead to the capital: Left road leads to the capital: YES NO Question: Is it true that the left road leads to the capital if and only if you speak the truth

21. Man Machine division of work (man) Deciding what propositions to use as building blocks (P and Q) Their meaning Their no. (why only 2?) (man) Enforcing the requirement that the answer to the question is yes if and only if the left road leads to the capital (mechanical) setting up the truth table (mechanical) arriving at the formula (man) converting the expression to a human understandable question

22. AI Perspective (post-web) Planning Computer Vision NLP Expert Systems Robotics Search, Reasoning, Learning IR

23. Search: Everywhere

24. Planning (a) which block to pick, (b) which to stack, (c) which to unstack, (d) whether to stack a block or (e) whether to unstack an already stacked block. These options have to be searched in order to arrive at the right sequence of actions. ACBA B C Table

25. Vision A search needs to be carried out to find which point in the image of L corresponds to which point in R. Naively carried out, this can become an O(n2) process where n is the number of points in the retinal images. World Two eye system RL

26. Robot Path Planning searching amongst the options of moving Left, Right, Up or Down. Additionally, each movement has an associated cost representing the relative difficulty of each movement. The search then will have to find the optimal, i.e., the least cost path. O1O1 R D O2O2 Robot Path

27. Natural Language Processing search among many combinations of parts of speech on the way to deciphering the meaning. This applies to every level of processing- syntax, semantics, pragmatics and discourse. The man would like to play. Noun Verb NounVerb Preposition

28. Expert Systems Search among rules, many of which can apply to a situation: If-conditions the infection is primary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract THEN there is suggestive evidence (0.7) that infection is bacteroid (from MYCIN)

29. Search building blocks  State Space : Graph of states (Express constraints and parameters of the problem)  Operators : Transformations applied to the states.  Start state : S 0 (Search starts from here)  Goal state : {G} - Search terminates here.  Cost : Effort involved in using an operator.  Optimal path : Least cost path

30. Examples Problem 1 : 8 – puzzle 8 4 6 5 1 7 2 1 4 7 63 3 5 8 S 2 G Tile movement represented as the movement of the blank space. Operators: L : Blank moves left R : Blank moves right U : Blank moves up D : Blank moves down C(L) = C(R) = C(U) = C(D) = 1

31. Problem 2: Missionaries and Cannibals Constraints The boat can carry at most 2 people On no bank should the cannibals outnumber the missionaries River R L Missionaries Cannibals boat Missionaries Cannibals

32. State : #M = Number of missionaries on bank L #C = Number of cannibals on bank L P = Position of the boat S0 = G = Operations M2 = Two missionaries take boat M1 = One missionary takes boat C2 = Two cannibals take boat C1 = One cannibal takes boat MC = One missionary and one cannibal takes boat

33. C2MC Partial search tree

34. Problem 3 BBWWW B G: States where no B is to the left of any W Operators: 1) A tile jumps over another tile into a blank tile with cost 2 2) A tile translates into a blank space with cost 1