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CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 1 - Introduction Persons involved Faculty instructor: Dr. Pushpak Bhattacharyya (www.cse.iitb.ac.in/~pb) TAs: Annervaz (annervaz@cse), Avishek (avis@cse), Sapan (sapan@cse) Course home page (to be created) www.cse.iitb.ac.in/~cs344-2008 Venue, Webcast etc.: Rahul Deshmukh, Sachin and others Areas of AI and their interdependencies Search Logic Machine Learning NLP Vision Knowledge Representation Planning Robotics Expert Systems Resources Main Text: Other Main References: Principles of AI - Nilsson AI - Rich & Knight Knowledge Based Systems – Mark Stefik Journals Artificial Intelligence: A Modern Approach by Russell & Norvik, Pearson, 2003. AI, AI Magazine, IEEE Expert, Area Specific Journals e.g, Computational Linguistics Conferences IJCAI, AAAI Allied Disciplines Philosophy Maths Economics Knowledge Rep., Logic, Foundation of AI (is AI possible?) Search, Analysis of search algos, logic Psychology Expert Systems, Decision Theory, Principles of Rational Behavior Behavioristic insights into AI programs Brain Science Learning, Neural Nets Physics Learning, Information Theory & AI, Entropy, Robotics Computer Sc. & Engg. Systems for AI Topics to be covered Search Logic General Graph Search, A* Iterative Deepening, α-β pruning, probabilistic methods Formal System Propositional Calculus, Predicate Calculus Inductive Logic Programming Knowledge Representation Predicate calculus, Semantic Net, Frame Script, Conceptual Dependency, Uncertainty Other Material Classical Machine Learning Computer Vision Learning from examples Decision Trees Analogical Learning Explanation Based Learning AI methods in Computer Vision Planning Rule Based Stochastic Seminars and Projects to be done: suggested areas Web and AI Robotic Algorithms Prediction, Forecasting Brain Science and AI Computer Games Seminar: part of cs344 evaluation Project: part of cs386 evaluation Search Search is present everywhere 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. A B Table C C B A 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. R L Two eye system World 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. R O2 Robot Path O1 D Natural Language Processing search among many combinations of parts of speech on the way to deciphering the meaning. This applies to every level of processingsyntax, semantics, pragmatics and discourse. The Noun man would like to play. PrepositionVerb Noun Verb Verb 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) Algorithmics of Search General Graph search Algorithm S 1 10 3 Graph G = (V,E) A B C 4 5 6 D E 3 2 F 7 G 1) Open List : S (Ø, 0) Closed list : Ø 6) OL : E(B,7), F(D,8), G(D, 9) CL : S, A, B, C, D 2) OL : A(S,1), B(S,3), C(S,10) CL : S 7) OL : F(D,8), G(D,9) CL : S, A, B, C, D, E B(S,3), 3) OL : CL : S, A C(S,10), D(A,6) 4) OL : C(S,10), D(A,6), E(B,7) CL: S, A, B 5) OL : D(A,6), E(B,7) CL : S, A, B , C 8) OL : G(D,9) CL : S, A, B, C, D, E, F 9) OL : Ø CL : S, A, B, C, D, E, F, G GGS Data Structures Key data structures : Open List, Closed list Nodes from open list are taken in some order, expanded and children are put into open list and parent is put into closed list. Assumption: Monotone restriction is satisfied. That is the estimated cost of reaching the goal node for a particular node is no more than the cost of reaching a child and the estimated cost of reaching the goal from the child S n1 C(n1,n2) n2 h(n1) h(n2) g h(n1 ) C(n1 , n2 ) h(n2 ) GGS OL is a queue (BFS) OL is stack (DFS) OL is accessed by using a functions f= g+h (Algorithm A) BFS, DFS – Uninformed / Brute Force Search methods Algorithm A A function f is maintained with each node f(n) = g(n) + h(n), n is the node in the open list Node chosen for expansion is the one with least f value For BFS: h = 0, g = number of edges in the path to S For DFS: h = 0, g = Algorithm A* One of the most important advances in AI g(n) = least cost path to n from S found so far h(n) <= h*(n) where h*(n) is the actual cost of optimal path to G(node to be found) from n “Optimism leads to optimality” S g(n) n h(n) G Search building blocks State Space : Graph of states (Express constraints and parameters of the problem) Operators : Transformations applied to the states. Start state : S (Search starts from here) 0 Goal state : {G} - Search terminates here. Cost : Effort involved in using an operator. Optimal path : Least cost path Examples Problem 1 : 8 – puzzle 4 3 6 1 2 3 2 1 8 4 5 6 5 7 8 7 S G 0 Tile movement represented as the movement of the blank space. Operators: L : Blank moves left C(L) = C(R) = C(U) = C(D) = 1 R : Blank moves right U : Blank moves up D : Blank moves down Problem 2: Missionaries and Cannibals R boat River boat L Missionaries Missionaries Cannibals Cannibals Constraints The boat can carry at most 2 people On no bank should the cannibals outnumber the missionaries State : <#M, #C, P> #M = Number of missionaries on bank L #C = Number of cannibals on bank L P = Position of the boat S0 = <3, 3, L> G = < 0, 0, R > 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 <3,3,L> C2 <3,1,R > MC <2,2,R > <3,3,L> Partial search tree Problem 3 B B B W W W 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 All the three problems mentioned above are to be solved using A* A* A* Algorithm – Definition and Properties f(n) = g(n) + h(n) The node with the least value of f is chosen from the OL. g(n) f*(n) = g*(n) + h*(n), where, g*(n) = actual cost of the optimal path (s, n) h*(n) = actual cost of optimal path (n, g) S s g(n) ≥ g*(n) By definition, h(n) ≤ h*(n) n h(n) goal State space graph G 8-puzzle: heuristics Example: 8 puzzle 1 6 7 1 2 3 3 2 4 5 6 8 7 8 g 2 1 4 4 7 8 3 5 s 5 6 n h*(n) = actual no. of moves to transform n to g h* h2 1. h1(n) = no. of tiles displaced from their destined position. 2. h2(n) = sum of Manhattan distances of tiles from their destined position. h1 Comparison Missionaries and Cannibals Problem 3 missionaries (m) and 3 cannibals (c) on the left side of the river and only one boat is available for crossing over to the right side. At any time the boat can carry at most 2 persons and under no circumstance the number of cannibals can be more than the number of missionaries on any bank Missionaries and Cannibals Problem: heuristics Start state: <3, 3, L> Goal state: <0, 0, R> h1(n) = (no. of m + no. of c) / 2, on the left side h2(n) = no. of m + no. of c – 1 h1(n) ≤ h*(n) and h1(n) ≤ h*(n)