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Artificial Intelligence Course outline. Introduction Problem solving Generic algorithms Knowledge Representation and Reasoning Expert Systems Uncertainty Learning Planning Advanced topics in AI. Artificial Intelligence Problem Solving and Search. Muhammad Ejaz. Problem Solving.

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artificial intelligence course outline
Artificial IntelligenceCourse outline
  • Introduction
  • Problem solving
  • Generic algorithms
  • Knowledge Representation and Reasoning
  • Expert Systems
  • Uncertainty
  • Learning
  • Planning
  • Advanced topics in AI

CIIT Sahiwal

problem solving
Problem Solving
  • The mechanism to find the goal performing some actions to transit from one state to another is known as problem solving.
  • Initial state
  • Operator: description of an action
  • State space: all states reachable from the initial state by any sequence action
  • Path: sequence of actions leading from one state to another
  • Goal test: which the agent can apply to a single state description to determine if it is a goal state
  • Path cost function: assign a cost to a path which the sum of the costs of the individual actions along the path.

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different types of problem
Different types of problem
  • Well structured problems
  • When problem description and its rules are described clearly and unambiguously.
  • Ill structured problem

No extra information about the problem other than the definition

      • No extra information
      • No heuristics (rules)

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well defined problems
Well Defined Problems
  • Finding the maximum from a sequence of integers.
  • Eight puzzle
  • 1,2 or 2,1 puzzle
  • Farmer and goose problem
  • Water pouring problem

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eight puzzle
Eight puzzle

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farmer and goose problem
Farmer and goose problem

W F

~ W

F G

F W F G ~ G F ~

W C W C C ~ G F

G ~ C F ~ W

C F ~ C F W W G

~ G G ~ G C CC

F C

W ~

G W

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think point
Think Point
  • What will be tree representation of Farmer and goose problem?

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searching for solutions
Searching For Solutions
  • Having formulated some problems…how do we solve them?
  • Search through a state space
  • Use a search tree that is generated with an initial state and successor functions that define the state space

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different search techniques
Different search techniques
  • Uninformed Search

Only the information available in the problem definition

    • Also known as blind searching

-Breadth-first search

-Depth-first search

-Depth-limited search

-Iterative deepening search

  • Informed Search

 When sufficient information is provided to reach the goal.

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breadth first search
Breadth-First Search
  • Recall from Data Structures the basic algorithm for a breadth-first search on a graph or tree
  • Expand the shallowest unexpanded node
  • Place all new successors at the end of a FIFO queue

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depth first search
Depth-First Search
  • Recall from Data Structures the basic algorithm for a depth-first search on a graph or tree
  • Expand the deepest unexpanded node
  • Unexplored successors are placed on a stack until fully explored

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depth first search1
Depth-First Search

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depth first search2
Depth-First Search

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depth first search3
Depth-First Search

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depth first search4
Depth-First Search

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depth first search5
Depth-First Search

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depth first search6
Depth-First Search

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depth first search7
Depth-First Search

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depth first search8
Depth-First Search

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depth first search9
Depth-First Search

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depth first search10
Depth-First Search

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depth first search11
Depth-First Search

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depth first search12
Depth-First Search

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