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Main articles: Strong AI and AI-complete

Most researchers hope that their work will eventually be incorporated into a machine with general

intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them.[13] A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.[76][77]

Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.[78]

2.

2.A EXPLAIN STATE SPACE REPRESENTATION FOR WATER JUG PROBLEM

• The state space representation forms the basis of most of the AI methods.

• Its structure corresponds to the structure of problem solving in two important ways:

– It allows for a formal definition of a problem as the need to convert some given situation into some desired situation using a set of permissible operations.

– It permits us to define the process of solving a particular problem as a combination of known techniques (each represented as a rule defining a single step in the space) and search, the general technique of exploring the space to try to find some path from current state to a goal state.

– Search is a very important process in the solution of hard problems for which no more direct techniques are available.

–

Example playing chess

• To build a program that could “play chess”, we could first have to specify the starting position of the chess board, the rules that define the legal moves, and the board positions that represent a win for one side or the other.

• In addition, we must make explicit the previously implicit goal of not only playing the legal game of chess but also winning the game, if possible,

•

….. Playing chess

• The starting position can be described as an 8by 8 array where each position contains a symbol for appropriate piece.

• We can define as our goal the check mate position.

• The legal moves provide the way of getting from initial state to a goal state. • They can be described easily as a set of rules consisting of two parts:

– A left side that serves as a pattern to be matched against the current board position. – And a right side that describes the change to be made to reflect the move

• However, this approach leads to large number of rules 10120 board positions !! • Using so many rules poses problems such as:

– No person could ever supply a complete set of such rules.

– No program could easily handle all those rules. Just storing so many rules poses serious difficulties.

Defining chess problem as State Space search

• We need to write the rules describing the legal moves in as general a way as possible. • For example:

– White pawn at Square( file e, rank 2) AND Square( File e, rank 3) is empty AND Square(file e, rank 4) is empty, then move the pawn from Square( file e, rank 2) to Square( file e, rank 4).

• In general, the more succintly we can describe the rules we need, the less work we will have to do to provide them and more efficient the program.

Water Jug Problem

• The state space for this problem can be described as the set of ordered pairs of integers (x,y) such that x = 0, 1,2, 3 or 4 and y = 0,1,2 or 3; x represents the number of gallons of water in the 4-gallon jug and y represents the quantity of water in 3-gallon jug

• The start state is (0,0) • The goal state is (2,n)

Production rules for Water Jug Problem

Sl No Current state Next State Descritpion

1 (x,y) if x < 4 (4,y) Fill the 4 gallon jug 2 (x,y) if y <3 (x,3) Fill the 3 gallon jug 3 (x,y) if x > 0 (x-d, y) Pour some water

out of the 4 gallon jug

4 (x,y) if y > 0 (x, y-d) Pour some water out of the 3-gallon jug

5 (x,y) if x>0 (0, y) Empty the 4 gallon jug

6 (x,y) if y >0 (x,0) Empty the 3 gallon jug on the ground 7 (x,y) if x+y >= 4

and y >0

(4, y-(4-x)) Pour water from the 3 –gallon jug into the 4 –gallon jug until the 4- gallon jug is full

Production rules

8 (x, y) if x+y >= 3 and x>0

(x-(3-y), 3) Pour water from the 4- gallon jug into the 3- gallon jug until the 3- gallon jug is full 9 (x, y) if x+y <=4 and

y>0

(x+y, 0) Pour all the water from the 3-gallon jug into the 4-gallon jug 10 (x, y) if x+y <= 3

and x>0

(0, x+y) Pour all the water from the 4-gallon jug into the 3-gallon jug

11 (0,2) (2,0) Pour the 2 gallons

from 3-gallon jug into the 4-gallon jug

12 (2,y) (0,y) Empty the 2 gallons in

the 4-gallon jug on the ground

To solve the water jug problem

• Required a control structure that loops through a simple cycle in which some rule whose left side matches the current state is chosen, the appropriate change to the state is made as described in the corresponding right side, and the resulting state is checked to see if it corresponds to goal state. • One solution to the water jug problem

• Shortest such sequence will have a impact on the choice of appropriate mechanism to guide the search for solution.

Gallons in the 4- gallon jug Gallons in the 3- gallon jug Rule applied 0 0 2 0 3 9 3 0 2 3 3 7 4 2 5 or 12 0 2 9 0r 11 2 0 
 Ü

2.B DIFFERENTIATE BETWEEN DEPTH FIRST VERSUS BREADTH FIRST SEARCH METHOD

Breadth-first search (BFS) and depth-first search (DFS) are two distinct orders in which to visit the vertices and edges of a graph. BFS radiates out from a root to visit vertices in order of their distance from the root. Thus closer nodes get visited first.

DFS prefers to visit undiscovered vertices immediately, so the search trees tend to be deeper rather than balanced as with BFS. Notice that the DFS consists of three ``Hamiltonian'' paths, one in each component -- while the BFS tree has far more degree-3 nodes, reflecting balance.

3.

3.A WHAT IS HEURISTICS? EXPLAIN ANY HEURISTIC SEARCH METHOD

A branch of artificial intelligence, which uses heuristics -- common-sense rules drawn from experience -- to solve problems. This is in contrast to algorithmic programming, which is based on mathematically provable procedures. Heuristic programming is characterized by programs that are self-learning; they get better with experience. Heuristic programs do not always reach the very best result but usually produce a good result. Many expert systems use heuristic programming.

A heuristic is a method that

• might not always find the best solution

• but is guaranteed to find a good solution in reasonable time. • By sacrificing completeness it increases efficiency.

• Useful in solving tough problems which o could not be solved any other way.

o solutions take an infinite time or very long time to compute. Heuristic Search methods Generate and Test Algorithm

1. generate a possible solution which can either be a point in the problem space or a path from the initial state.

2. test to see if this possible solution is a real solution by comparing the state reached with the set of goal states.

3. if it is a real solution, return. Otherwise repeat from 1.

This method is basically a depth first search as complete solutions must be created before testing. It is often called the British Museum method as it is like looking for an exhibit at random. A heuristic is needed to sharpen up the search. Consider the problem of four 6-sided cubes, and each side of the cube is painted in one of four colours. The four cubes are placed next to one another and the problem lies in arranging them so that the four available colours are displayed whichever way the 4 cubes are viewed. The problem can only be solved if there are at least four sides coloured in each colour and the number of options tested can be reduced using heuristics if the most popular colour is hidden by the adjacent cube.

The classic example of heuristic search methods is the travelling salesman problem.

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization studied in operations research and theoretical computer science. Given a list of cities and their pairwise distances, the task is to find a shortest possible tour that visits each city exactly once.

The problem was first formulated as a mathematical problem in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved.

The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as DNA

sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. In many applications, additional constraints such as limited resources or time windows make the problem considerably harder.

In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether any tour is shorter than L) belongs to the class of NP-complete problems. Thus, it is likely that the worst case running time for any algorithm for the TSP increases exponentially with the number of cities.