1. State Space Search
Classic AI

2. State Space representation of a problem is a graph
Nodes correspond to problem states
Arcs correspond to steps in a solution process
One node corresponds to an initial state
One node corresponds to a goal state

3. Solution Path
An ordered sequence of nodes from the initial state to the goal state

4. Search Algorithm
Finds a solution path through a state space

5. The Water Jug Problem Suppose we have An empty 4 gallon jug
A source of water
A task: put 2 gallons of water in the 4 gallon jug

6. Representation State Space Node on the graph is an ordered pair (x,y)
X is the contents of the 4 gallon jug
Y is the contents of the 3 gallon jug
Intitial State: (0,0)
Goal State: (2,N) N ε {0, 1, 2, 3}

7. Rules if x < 4, fill x : (x,y)  (4,y)
if y < 3, fill y : (x,y)  (x,3)
if x > 0, empty x : (x,y)  (0,y)
if y > 0, empty y : (x,y)  (x,0)
if (x+y) >= 4 and y > 0
fill the 4 gallon jug from the 3 gallon jug
(x,y)  (4, y – (4 – x))
if (x+y) >= 3 and x > 0
Fill the 3 gallon jug from the 4 gallon jug
(x,y)  (x –(3 – y), 3))
if (x+y) <= 4 and y > 0
Pour the 3 gallon jug into the 4 gallon jug: (x,y)  (x+y), 0)
if (x+y) <= 3 and x > 0
pour the 4 gallon jug into the 3 gallon jug: (x,y)  (0, x + y)

8. Is there a solution path?
Initial State: (0,0)
Goal State: (2,N)

9. Breadth First Search (0,3) 1 2 (0,3) (4,0) (0,3) 6 7 2 (3,0) (4,3)
(1,3)
etc

10. Depth First (0,0) (4,0) 1 2 3 (4,3) 7 (0,3) (3,0) 2 (3,3)
Etc. and without visiting already visited states

11. Backward/Forward Chaining
Search can proceed
From data to goal
From goal to data
Either could result in a successful search path, but one or the other might require examining more nodes depending on the circumstances

12. Data to goal is called forward chaining for data driven search
Goal to data is called backward chaining or goal driven search

13. Examples Water jug was data driven Grandfather problem was goal driven
To make water jug goal driven:
Begin at (2,y)
Determine how many rules could produce this goal
Follow these rules backwards to the start state

14. Object
Reduce the size of the search space

15. Use Goal Driven if
Goal is clearly stated
Many rules match the given facts
For example: the number of rules that conlude a given theorem is much smaller than the number that may be applied to the entire axiom set

16. Use Data Driven If
Most data is given at the outset
Only a few ways to use the facts
Difficult to form an initial hypothesis
For example: DENDRAL, an expert system that finds molecular structure of organic compounds based on spectrographic data. There are lots of final possibilities, but only a few ways to use the initial data
Said another way: initial data constrains search