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Formal Description of a Problem In AI, we will formally define a problem as a space of all possible configurations where each configuration is called a state thus, we use the term state space an initial state one or more goal states

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formal description of a problem
Formal Description of a Problem
  • In AI, we will formally define a problem as
    • a space of all possible configurations where each configuration is called a state
      • thus, we use the term state space
    • an initial state
    • one or more goal states
    • a set of rules/operators which move the problem from one state to the next
  • In some cases, we may enumerate all possible states (see monkey & banana problem on the next slide)
    • but usually, such an enumeration will be overwhelmingly large so we only generate a portion of the state space, the portion we are currently examining
the monkey bananas problem
The Monkey & Bananas Problem
  • A monkey is in a cage and bananas are suspended from the ceiling, the monkey wants to eat a banana but cannot reach them
    • in the room are a chair and a stick
    • if the monkey stands on the chair and waves the stick, he can knock a banana down to eat it
    • what are the actions the monkey should take?

Initial state:

monkey on ground

with empty hand

bananas suspended

Goal state:

monkey eating

Actions:

climb chair/get off

grab X

wave X

eat X

missionaries and cannibals
Missionaries and Cannibals
  • 3 missionaries and 3 cannibals are on one side of the river with a boat that can take exactly 2 people across the river
    • how can we move the 3 missionaries and 3 cannibals across the river
    • with the constraint that the cannibals never outnumber the missionaries on either side of the river (lest the cannibals start eating the missionaries!)??
  • We can represent a state as a 6-item tuple:
    • (a, b, c, d, e, f)
      • a/b = number of missionaries/cannibals on left shore
      • c/d = number of missionaries/cannibals in boat
      • e/f = number of missionaries/cannibals on right shore
      • where a + b + c + d + e + f = 6
      • and a >= b unless a = 0, c >= d unless c = 0, and e >= f unless e = 0
  • Legal operations (moves) are
    • 0, 1, 2 missionaries get into boat
    • 0, 1, 2 missionaries get out of boat
    • 0, 1, 2 cannibals get into boat
    • 0, 1, 2 missionaries get out of boat
    • boat sails from left shore to right shore
    • boat sails from right shore to left shore
      • drawing the state space will be left as a homework problem
graphs trees
Graphs/Trees
  • We often visualize a state space (or a search space) as a graph
    • a tree is a special form of graph where every node has 1 parent and 0 to many children, in a graph, there is no parent/child relationship implied
      • some problems will use trees, others can use graphs
  • To the right is an example of representing a situation as a graph
    • on the top is the city of Konigsberg where there are 2 shores, 2 islands and 7 bridges
      • the graph below shows the connectivity
    • the question asked in this problem was: is there a single path that takes you to both shores and islands and covers every bridge exactly once?
      • by representing the problem as a graph, it is easier to solve
      • the answer by the way is no, the graph has four nodes whose degree is an odd number, the problem, finding an Euler path, is only solvable if a graph has exactly 0 or 2 nodes whose degrees are odd
8 puzzle
8 Puzzle

The 8 puzzle search space consists of 8! states (40320)

problem characteristics
Problem Characteristics
  • Is the problem decomposable?
    • if yes, the problem becomes simpler to solve because each lesser problem can be tackled and the solutions combined together at the end
  • Can solution steps be undone or ignored?
    • a game for instance often does not allow for steps to be undone (can you take back a chess move?)
  • Is the problem’s universe predictable?
    • will applying the action result in the state we expect? for instance, in the monkey and banana problem, waving the stick on a chair does not guarantee that a banana will fall to the ground!
  • Is a good solution absolute or relative?
    • for instance, do we care how many steps it took to get there?
  • Is the desired solution a state or a path?
    • is the problem solved by knowing the steps, or reaching the goal?
  • Is a large amount of knowledge absolutely required?
  • Is problem solving interactive?
search
Search
  • Given a problem expressed as a state space (whether explicitly or implicitly)
    • with operators/actions, an initial state and a goal state, how do we find the sequence of operators needed to solve the problem?
    • this requires search
  • Formally, we define a search space as [N, A, S, GD]
    • N = set of nodes or states of a graph
    • A = set of arcs (edges) between nodes that correspond to the steps in the problem (the legal actions or operators)
    • S = a nonempty subset of N that represents start states
    • GD = a nonempty subset of N that represents goal states
  • Our problem becomes one of traversing the graph from a node in S to a node in GD
    • we can use any of the numerous graph traversal techniques for this but in general, they divide into two categories:
      • brute force – unguided search
      • heuristic – guided search
consequences of search
Consequences of Search
  • As shown a few slides back, the 8-puzzle has over 40000 different states
    • what about the 15 puzzle?
  • A brute force search means trying all possible states blindly until you find the solution
    • for a state space for a problem requiring n moves where each move consists of m choices, there are 2m*n possible states
    • two forms of brute force search are: depth first search, breath first search
  • A guided search examines a state and uses some heuristic (usually a function) to determine how good that state is (how close you might be to a solution) to help determine what state to move to
    • hill climbing
    • best-first search
    • A/A* algorithm
    • Minimax
  • While a good heuristic can reduce the complexity from 2m*n to something tractable, there is no guarantee so any form of search is O(2n) in the worst case
forward vs backward search
Forward vs Backward Search
  • The common form of reasoning starts with data and leads to conclusions
    • for instance, diagnosis is data-driven – given the patient symptoms, we work toward disease hypotheses
      • we often think of this form of reasoning as “forward chaining” through rules
  • Backward search reasons from goals to actions
    • Planning and design are often goal-driven
      • “backward chaining”
depth first search
Depth-first Search

Starting at node A, our search gives us:

A, B, E, K, S, L, T, F, M, C, G, N, H, O, P,

U, D, I, Q, J, R

breadth first search
Breadth-First Search

Starting at node A, our search would generate the

nodes in alphabetical order from A to U

dfs with iterative deepening
DFS with Iterative Deepening
  • We might assume that most solutions to a given problem are toward the bottom of the state space
    • the DFS then is superior because it reaches the lower levels much more rapidly
    • however, DFS can get “lost” in the lower levels, spending too much time on solutions that are very similar
  • An alternative is to use DFS but with iterative deepening
    • here, we continue to go down the same branch until we reach some pre-specified maximum depth
      • this depth may be set because we suspect a solution to exist somewhere around that location, or because of time constraints, or some other factor
    • once that depth has been reached, continue the search at that level in a breadth-first manner
      • see figure 3.19 on page 105 for an example of the 8-puzzle with a depth bound at 5
8 queens
8 Queens
  • Can you place 8 queens on a chess board such that no queen can capture another?
    • uses a recursive algorithm with backtracking
    • the more general problem is the N-queens problem (N queens on an NxN chess board)

solve(board, col, row)

if col = n then return true; // success

else

row = 0; placed = false;

while(row < n && !placed)

board[row][col] = true // place the queen

if(cannotCapture(board, col)) placed = true

else

board[row][col] = false; row++

if(row = n)

col--; placed = false; row = 0; // backtrack

and or graphs
And/Or Graphs
  • To this point in our consideration of search spaces, a single state (or the path to that state) represents a solution
    • in some problems, a solution is a combination of states or a combination of paths
    • we pursue a single path, until we reach a dead end in which case we backtrack, or we find the solution (or we run out of possibilities if no solution exists)
    • so our state space is an Or graph – every different branch is a different solution, only one of which is required to solve the problem
  • However, some problems can be decomposed into subproblems where each subproblem must be solved
    • consider for instance integrating some complex function which can be handled by integration by parts
    • such as state space would comprise an And/Or graph where a path may lead to a solution, but another path may have multiple subpaths, all of which must lead to solutions
and or graphs as search spaces
And/Or Graphs as Search Spaces

Integration by parts, as used in the

MACSYMA expert system –

if we use the middle branch, we must

solve all 3 parts (in the final row)

Our Financial Advisor system from chapter 2 – each possible investment

solution requires proving 3 things

solution

We want to know location(fred, Y)

  • As a goal-driven problem, we start with this and find a rule that can conclude location(X, Y), which is rule 7, 8 or 9
  • Rule 8 will fail because we cannot prove warm(Saturday)
  • Rule 9 is applied since day(saturday) is true and ~warm(saturday) is true
  • Rule 9’s conclusion is that sam is in the museum
  • Rule 7 tells us that fred is with his master, sam, so fred is in the museum
Solution
data driven example parsing
We wrap up this chapter by considering an example of syntactically parsing an English sentence

we have the following five rules:

sentence  np vp

np  n

np  art n

vp  v

vp  v np

n is noun

man or dog

v is verb

likes or bites

Art is article

a or the

Parse the following sentence:

The dog bites the man.

Data-driven Example: Parsing