Artificial Intelligence Course outline

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# Artificial Intelligence Course outline - PowerPoint PPT Presentation

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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Presentation Transcript
Artificial IntelligenceCourse outline
• Introduction
• Problem solving
• Generic algorithms
• Knowledge Representation and Reasoning
• Expert Systems
• Uncertainty
• Learning
• Planning

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### Artificial IntelligenceProblem Solving and Search

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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
• 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
• 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

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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
• What will be tree representation of Farmer and goose problem?

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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
• Uninformed Search

Only the information available in the problem definition

• Also known as blind searching

-Depth-first search

-Depth-limited search

-Iterative deepening search

• Informed Search

 When sufficient information is provided to reach the goal.

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• 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
• 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 Search

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Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal

Depth-First Search

CIIT Sahiwal