1. a) Breadth-first search is a special case of Uniform-cost search when all step costs are equal.

b) Breadth-first search, depth-first search and uniform-cost search are special cases of best-first search.Breadth-first search is best-first search with f(n) = depth(n); Depth-first search is best-first search with f(n) = - depth(n); Uniform-cost search is best-first search with f(n) = g(n).c) Uniform-cost search is a special case of A* Algorithm with h(n) = 0.

2. (2011, 7.a)What can AI systems do?Here are some example applications: Computer vision: face recognition from a large set Robotics: autonomous (mostly) automobile Natural language processing: simple machine translation Expert systems: medical diagnosis in a narrow domain Spoken language systems: ~1000 word continuous speech Planning and scheduling: Hubble Telescope experiments Learning: text categorization into ~1000 topics User modeling: Bayesian reasoning in Windows help (the infamous paper clip) Games: Grand Master level in chess (world champion), checkers, etc.

What cant AI systems do yet? Understand natural language robustly (e.g., read and understand articles in a newspaper) Surf the web Interpret an arbitrary visual scene Learn a natural language Play Go well Construct plans in dynamic real-time domains Refocus attention in complex environments Perform life-long learning.

3. (2011, 7.b) State-Space vs. Plan-Space SearchState-space search: search through graph of nodes representing world states.Plan-space search: search through graph of partial plans. Nodes: partially specified plans Arcs: plan refinement operations Solutions: partial-order plansPartial Plans:Plan: Set of actions organized into some structure.Partial plan: Subset of the actions Subset of the organizational structure Temporal ordering of actions Rationale: what the action achieves in the plan Subset of variable bindingsAdding Actions:Partial plan contains actions: Initial state Goal conditions Set of operators with different variablesReason for adding new actions: To achieve unsatisfied preconditions To achieve unsatisfied goal conditions4. (2012, 11.c) AI is in term of task domains: i) Expert tasks (you might hire a professional consultant to do): Design, engineering, graphics , Art, creativity , Music Analysis, Financial Analysis, Consulting .ii) Formal tasks (logic, constraints) : Board Game-Playing (chess, checkers) , Logic, Calculus, Algebra, Verification, Theorem Proving.iii) Mundane tasks (common things you do every day): Vision, Speech,Natural Language Processing & Generation, Understanding , Reasoning ,Motion.

5. (2012, 11.b) Suppose (x,y) represents the tuple of the contents of 5 litre and 3 litre Jugs repectively. So the following states represent the action to be taken so that at the end the 5 litre jug contains 4 litre of water.(0,3) (3,0) (3,3) (5,1) (0,1) (1,0) (1,3) (4,0). [State the actions taken which are self explanatory]

6. 8-queens problem: Place eight queens on an 8 X 8 chessboard so that no queen attacks another queen.*** A queen attacks another queen if the two are in the same row, column, or diagonal

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Identify data structures to solve the problem:* First pass: Define the chessboard to be an 8 X 8 array*Second pass: Since each queen is in a different row, define the chessboard solution to be an 8-tuple (x1,, x8), where xi is the column for ith queen

Identify explicit constraints:*Explicit constraints using 8-tuple formulation are Si = {1,2,3,4,5,6,7,8}, 1 i 8*Solution space of 88 8! Tuples

Identify implicit constraints:*No two xi can be the same, or all the queens must be in different columns*All solutions are permutations of the 8-tuple {1,2,3,4,5,6,7,8}*Reduces the size of solution space from 88 to 8! tuples*No two queens can be on the same diagonal

The solution above is expressed as an 8-tuple as 4,6,8,2,7,1,3,5