artificial intelligence presentation
DESCRIPTION
Artificial Intelligence Presentation. Chapter 4 – Informed Search and Exploration. Overview. Defining a problem Types of solutions The different algorithms to achieve these solutions Conclusion Questions and Answers Session. Defining a problem. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/1.jpg)
Artificial Intelligence Presentation
Chapter 4 – Informed Search and
Exploration
![Page 2: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/2.jpg)
Overview
• Defining a problem
• Types of solutions
• The different algorithms to achieve
these solutions
• Conclusion
• Questions and Answers Session
![Page 3: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/3.jpg)
Defining a problem
A problem is well defined for an agent to solve if:
• There exists a state space, this is a set of all possible states an agent can be in.
• Within the state space there exists an initial state and a goal state.
• There exists a set of actions which an agent can take to progress from one state to
another
• There exists at least one path from the initial state to the goal state, that is to say,
there exists a sequence of actions by which the agent, parting from the initial state,
can assume a number of states that lead to the goal state. (Implicit from points 1 to 3)
• There exists a goal test, this is, a means which allows the agent to know it has
achieved, or not, the goal state
• There exists a cost associated to each path, this is, a numeric value which allows the
agent to compare the optimality between two, or more, paths to the goal state.
• There exists a cost associated with each action, from these in a sequence of actions,
one derives the path cost (For problems with more than one solution)
![Page 4: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/4.jpg)
Types of solutions
There are two types of solutions:
• A solution in which, alongside the goal, the path is also a
constituent of the solution.
Ex: What is the shortest path between reuter A and
reuter B in network X?
• A solution which is only the goal, that is to say, the path
which leads to the solution is irrelevant.
Ex: What is the minimum number of moves needed to
win a chess match?
![Page 5: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/5.jpg)
Types of solutions
Solutions of the first kind, the ideal algorithms are path finding
algorithms, these are algorithms which explore the state-space
systematically, keeping points along the path in memory.
Solutions of the second kind, are typically solutions to
optimization problems and have solution searching algorithms
based simply on the current state. They occupy less memory
and can, given enough time, find solutions which would not be
possible in path finding algorithms, due to memory constraints.
![Page 6: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/6.jpg)
Path Finding algorithms
There are 2 types of path finding algorithms:
• Uniformed search algorithms
These search strategies just generate successors
and analyze whether or not the new state is the goal
state.
• Informed search algorithms
These search strategies have a former knowledge
of which non-goal states are more promising.
![Page 7: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/7.jpg)
Greedy Best-First Search
This algorithm has the following basic process:
• Each node has an f(n) = h(n).
• Select the node with the lowest f(n)
• If f(n) > 0 then expand the node repeat the
process
• Else if f(n) = h(n) == 0, then it is the goal-node
![Page 8: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/8.jpg)
Greedy Best-First Search
![Page 9: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/9.jpg)
A* Search
A* search is similar the the best-first
algorithms however f(n) is not h(n) but
g(n) + h(n), where:
• g(n) is the cost to get to n
• h(n) is the cost from n to the the goal
![Page 10: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/10.jpg)
A* Search
![Page 11: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/11.jpg)
A* Search
A* search is optimal if h(n) is an
admissible heuristic, that is to say, it
never overestimates the cost of the
solution.
![Page 12: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/12.jpg)
A* Search
Disadvantages of A* Search
• Exponential growth in the number of nodes (memory
can fill up quick
• A* must search all the nodes within the goal contour
• Due to memory or time limitations, suboptimal goals
may be the only solution
• Sometimes a better heuristic may not be admissable
![Page 13: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/13.jpg)
Memory bounded heuristic search
In order to reduce the memory footprint of
the previous algorithms, some algorithms
attempt to take further advantages of
Heuristics to improve performance:
• Iterative-Deepening A* (IDA*) Search
• Recursive Best-First Search (RBFS)
• SMA*
![Page 14: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/14.jpg)
Memory bounded heuristic search
To deal with the issue of exponential
memory growth in A*, Iterative
deepening A * (IDA*) was created. This
practically the same as the normal
iterative deepening algorithm, except
that it
![Page 15: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/15.jpg)
IDA* Search
The IDA* is basically the iterative
deepening first depth search, but with
the cutoff at f = g+h
![Page 16: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/16.jpg)
SMA* Search
It follows like A* search, however when
memory reaches it’s limit, the
algorithm drops the worst node.
![Page 17: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/17.jpg)
Recursive Best-First Search (RBFS)
The Recursive best-first search works by:
• Keeping track of options along the fringe
• If the current depth-first exploration
becomes more expensive of best fringe
option, back up to fringe and but update
node costs along the way
![Page 18: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/18.jpg)
Recursive Best-First Search (RBFS)
![Page 19: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/19.jpg)
Effective Branching Factor, b*
The branching factor is such that if a uniform tree of
depth d contains N+1 nodes, then:
N+1 = 1 + b* + (b*)2 + … + (b*)d
The closer b* is to 1, the better the heuristic.
![Page 20: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/20.jpg)
How to come up with new Admissible Heuristics
Simplify problem by reducing restrictions on actions.This is called a relaxed problemThe cost of optimal solution to relaxed problem is an admissible heuristic for original problem, because it is always less expensive than the solution to the original problem
![Page 21: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/21.jpg)
Pattern Databases
Pattern databases made by storing patterns which have actions that are statistically favorable.
Ex:Chess plays in certain states of the board
![Page 22: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/22.jpg)
Local Search algorithms
They only keep track of the current solution (state)Utilize methods to generate alternate solution candidatesThey use a small amount of memoryCan find acceptable solutions in infinite search spaces
![Page 23: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/23.jpg)
Hill Climbing
![Page 24: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/24.jpg)
Simulated Annealing
• Select some initial guess of evaluation function parameters: x0
• Evaluate evaluation function, E(x0)=v
• Compute a random displacement, x’0– The Monte Carlo event
• Evaluate E(x’0) = v’
– If v’ < v; set new state, x1 = x’0– Else set x1 = x’0 with Prob(E,T)
• This is the Metropolis step
• Repeat with updated state and temp
![Page 25: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/25.jpg)
Genetic Algorithms
• Reproduction
• Reuse
• Crossover
• Mutation
![Page 26: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/26.jpg)
Genetic Algorithms
![Page 27: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/27.jpg)
Online Searches
• States and Actions are unknown
apriori
• States are difficult to change
• States can be or impossible difficult
to reverse
![Page 28: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/28.jpg)
Learning in Online Search
• Explore the world
• Build a map
• Mapping of (state, action) to results also called a model relating (state, action) to results
![Page 29: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/29.jpg)
Conclusion
![Page 30: Artificial Intelligence Presentation](https://reader035.vdocuments.mx/reader035/viewer/2022062422/56813494550346895d9b8115/html5/thumbnails/30.jpg)
Questions and Answers