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CSM6120 Introduction to Intelligent SystemsPowerPoint Presentation

CSM6120 Introduction to Intelligent Systems

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### CSM6120Introduction to Intelligent Systems

Search 3

Quick review

- Uninformed techniques

Informed search

- What we’ll look at:
- Heuristics
- Hill-climbing
- Best-first search
- Greedy search
- A* search

Heuristics

- A heuristic is a rule or principle used to guide a search
- It provides a way of giving additional knowledge of the problem to the search algorithm
- Must provide a reasonably reliable estimate of how far a state is from a goal, or the cost of reaching the goal via that state

- A heuristic evaluation function is a way of calculating or estimating such distances/cost

Heuristics and algorithms

- A correct algorithm will find you the best solution given good data and enough time
- It is precisely specified

- A heuristic gives you a workable solution in a reasonable time
- It gives a guided or directed solution

Evaluation function

- There are an infinite number of possible heuristics
- Criteria is that it returns an assessment of the point in the search

- If an evaluation function is accurate, it will lead directly to the goal
- More realistically, this usually ends up as “seemingly-best-search”
- Traditionally, the lowest value after evaluation is chosen as we usually want the lowest cost or nearest

Heuristic evaluation functions

- Estimate of expected utility value from a current position
- E.g. value for pieces left in chess
- Way of judging the value of a position

- Humans have to do this as we do not evaluate all possible alternatives
- These heuristics usually come from years of human experience

- Performance of the game playing program is very dependent on the quality of the function

Heuristic evaluation functions

- Must agree with the ordering a utility function would give at terminal states (leaf nodes)
- Computation must not take long
- For non-terminal states, the evaluation function must strongly correlate with the actual chance of ‘winning’
- The values can be learned using machine learning techniques

Heuristics for the 8-puzzle

- Number of tiles out of place (h1)
- Manhattan distance (h2)
- Sum of the distance of each tile from its goal position
- Tiles can only move up or down city blocks

The 8-puzzle

- Using a heuristic evaluation function:
- h(n) = sum of the distance each tile is from its goal position

Search algorithms

- Hill climbing
- Best-first search
- Greedy best-first search
- A*

Iterative improvement

- Consider all states laid out on the surface of a landscape
- The height at any point corresponds to the result of the evaluation function

Hill-climbing (greedy local)

- Start with current-state = initial-state
- Until current-state = goal-state OR there is no change in current-state do:
- a) Get the children of current-state and apply evaluation function to each child
- b) If one of the children has a better score, then set current-state to the child with the best score

- Loop that moves in the direction of decreasing (increasing) value
- Terminates when a “dip” (or “peak”) is reached
- If more than one best direction, the algorithm can choose at random

Hill-climbing drawbacks

- Local minima (maxima)
- Local, rather than global minima (maxima)

- Plateau
- Area of state space where the evaluation function is essentially flat
- The search will conduct a random walk

- Ridges
- Causes problems when states along the ridge are not directly connected - the only choice at each point on the ridge requires uphill (downhill) movement

Best-first search

- Like hill climbing, but eventually tries all paths as it uses list of nodes yet to be explored
- Start with priority-queue = initial-state
- While priority-queue not empty do:
- a) Remove best node from the priority-queue
- b) If it is the goal node, return success. Otherwise find its successors
- c) Apply evaluation function to successors and add to priority-queue

Best-first search

- Different best-first strategies have different evaluation functions
- Some use heuristics only, others also use cost functions: f(n) = g(n) + h(n)
- For Greedy and A*, our heuristic is:
- Heuristic function h(n) = estimated cost of the cheapest path from node n to a goal node
- For now, we will introduce the constraint that if n is a goal node, then h(n) = 0

Greedy best-first search

- Greedy BFS tries to expand the node that is ‘closest’ to the goal assuming it will lead to a solution quickly
- f(n) = h(n)
- aka “greedy search”

- Differs from hill-climbing – allows backtracking
- Implementation
- Expand the “most desirable” node into the frontier queue
- Sort the queue in decreasing order of desirability

Greedy best-first search

- Complete
- No, GBFS can get stuck in loops (e.g. bouncing back and forth between cities)

- Time complexity
- O(bm) but a good heuristic can have dramatic improvement

- Space complexity
- O(bm) – keeps all the nodes in memory

- Optimal
- No! (A – S – F – B = 450, shorter journey is possible)

Practical 2

- Implement greedy best-first search for pathfinding
- Look at code for AStarPathFinder.java

A* search

- A* (A star) is the most widely known form of Best-First search
- It evaluates nodes by combining g(n) and h(n)

- f(n) = g(n) + h(n)
- Where
- g(n) = cost so far to reach n
- h(n) = estimated cost to goal from n
- f(n) = estimated total cost of path through n

start

g(n)

n

h(n)

goal

A* search

- When h(n) = actual cost to goal, h*(n)
- Only nodes in the correct path are expanded
- Optimal solution is found

- When h(n) < h*(n)
- Additional nodes are expanded
- Optimal solution is found

- When h(n) > h*(n)
- Optimal solution can be overlooked

A* search

- Complete and optimal if h(n) does not overestimate the true cost of a solution through n
- Time complexity
- Exponential in [relative error of h x length of solution]
- The better the heuristic, the better the time
- Best case h is perfect, O(d)
- Worst case h = 0, O(bd) same as BFS

- Space complexity
- Keeps all nodes in memory and save in case of repetition
- This is O(bd) or worse
- A* usually runs out of space before it runs out of time

A* exercise

NodeCoordinatesSL Distance to K

A (5,9) 8.0

B (3,8) 7.3

C (8,8) 7.6

D (5,7) 6.0

E (7,6) 5.4

F (4,5) 4.1

G (6,5) 4.1

H (3,3) 2.8

I (5,3) 2.0

J (7,2) 2.2

K (5,1) 0.0

GBFS exercise

NodeCoordinatesDistance

A (5,9) 8.0

B (3,8) 7.3

C (8,8) 7.6

D (5,7) 6.0

E (7,6) 5.4

F (4,5) 4.1

G (6,5) 4.1

H (3,3) 2.8

I (5,3) 2.0

J (7,2) 2.2

K (5,1) 0.0

To think about...

f(n) = g(n) + h(n)

- What algorithm does A* emulate if we set
- h(n) = -2*g(n)
- h(n) = 0

- Can you make A* behave like Breadth-First Search?

Admissible heuristics

- A heuristic h(n) is admissible if for every node n,
h(n) ≤ h*(n), where h*(n) is the true cost to reach the goal state from n

- An admissible heuristic never overestimates the cost to reach the goal
- Example: hSLD(n) (never overestimates the actual road distance)

- Theorem: If h(n) is admissible, A* is optimal (for tree-search)

Optimality of A* (proof)

- Suppose some suboptimal goal G2has been generated and is in the frontier. Let n be an unexpanded node in the frontier such that n is on a shortest path to an optimal goal G
- f(G2) = g(G2) since h(G2) = 0 (true for any goal state)
- g(G2) > g(G) since G2 is suboptimal
- f(G) = g(G) since h(G) = 0
- f(G2) > f(G) from above

Optimality of A* (proof)

- f(G2) > f(G)
- h(n) ≤ h*(n) since h is admissible
- g(n) + h(n) ≤ g(n) + h*(n)
- f(n) ≤ f(G)
- Hence f(G2) > f(n), and A* will never select G2 for expansion

Heuristic functions

- Admissible heuristic example: for the 8-puzzleh1(n) = number of misplaced tilesh2(n) = total Manhattan distance i.e. no of squares from desired location of each tileh1(S) = ??h2(S) = ??

Heuristic functions

- Admissible heuristic example: for the 8-puzzle h1(n) = number of misplaced tilesh2(n) = total Manhattan distance i.e. no of squares from desired location of each tileh1(S) = 6h2(S) = 4+0+3+3+1+0+2+1 = 14

Heuristic functions

- Dominance/Informedness
- if h1(n) h2(n) for all n (both admissible)then h2 dominates h1 and is better for searchTypical search costs:
- d = 12 IDS = 3,644,035 nodes A*(h1) = 227 nodes A*(h2) = 73 nodes
- d = 24 IDS 54,000,000,000 nodes A*(h1) = 39,135 nodes A*(h2) = 1,641 nodes

Heuristic functions

- Admissible heuristicexample: for the 8-puzzleh1(n) = number of misplaced tilesh2(n) = total Manhattan distance i.e. no of squares from desired location of each tileh1(S) = 6h2(S) = 4+0+3+3+1+0+2+1 = 14

But how do we come up with a heuristic?

Relaxed problems

- Admissible heuristics can be derived from the exact solution cost of a relaxed version of the problem
- If the rules of the 8-puzzle are relaxed so that a tile can move anywhere, then h1(n) gives the shortest solution
- If the rules are relaxed so that a tile can move to any adjacent square, then h2(n) gives the shortest solution
- Key point: the optimal solution cost of a relaxed problem is no greater than the optimal solution cost of the real problem

Choosing a strategy

- What sort of search problem is this?
- How big is the search space?
- What is known about the search space?
- What methods are available for this kind of search?
- How well do each of the methods work for each kind of problem?

Which method?

- Exhaustive search for small finite spaces when it is essential that the optimal solution is found
- A* for medium-sized spaces if heuristic knowledge is available
- Random search for large evenly distributed homogeneous spaces
- Hill climbing for discrete spaces where a sub-optimal solution is acceptable

Summary

- What is search for?
- How do we define/represent a problem?
- How do we find a solution to a problem?
- Are we doing this in the best way possible?

Pathfinding applet

- http://www.stefan-baur.de/cs.web.mashup.pathfinding.html

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