WHAT IS ARTIFICIAL INTELLIGENCE? - School of Computer Science
What is artificial intelligence
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What is artificial intelligence?
shreya chakraborty
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The physical symbol system
•set of entities==> symbols
•All symbols/instances related in some physical way.
•Processes(creation, modification, reproduction and destruction)
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Intelligence requires knowledgeKnowledge-
1. Voluminous
2. Hard to categorize
3. Constantly changing
4. Organisation different to usage
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What is an AI technique?
•Should capture generalisation
•Understood by people who provide it
•Easily modified to correct errors and reflect changes
•Mostly accurate
•Overcome bulk possibilities to produce result
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3 important AI techniques?Search
Use of knowledge
Abstraction
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4 steps to solve a problem•Define problem precisely
•Analyse problem
•Isolate and represent task knowledge necessary
•Choose best problem solving technique
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State space representation
• Basis of AI methods
• Structure:-
• Formal definition for problem
• Explore space trying to find path from current state to goal state
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State space problem
•Define state space (all possible configurations of relevant objects)
•Specify initial state
•Specify goal state
•Specify set of rules that define actions
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Production System•Rules : Applicability ->Operation
•Knowledge/Databases
•Control strategy
•Rule applier
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Control Strategy requirements•It should cause motion
•It should be systematicshreya chakraborty
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Breadth-first
Searchshreya chakraborty
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Depth-first
Searchshreya chakraborty
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Heuristic SearchHeuristic Knowledge incorporated in search
• in rules themselves
• or as a function
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Heuristic function• Problem description
measures(numbers)
• F(x)=g(x)+h’(x)
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Problem characteristics•Decomposable?•Solution steps can be undone?•Problem’s universe predictable?•Good solution obvious?•Desired solution a state or a path?•Large amt of knowledge absolutely required to solve the problem/•Can computer take problem and return solution?
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Issues in Search Program Design
*instead of building entire tree, programs represent trees in rules implicitly, and generate what needs to be explored
*forward vs backward reasoning
*rule matching
*knowledge representation problem and frame problem
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Heuristic Search techniques1. Depth First
2. Breadth Firstshreya chakraborty
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3. Generate and Test
• Generate solution
• Check to see if actually a solution by comparison
• If solution found quit, else repeat all steps
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4. Hill Climbing
• Simple Hill Climbing
• Steepest Ascent Hill Climbing
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5. Best First Search
• Or-graphs
• A* algorithm
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using REDUCE-AND
· Use REDUCE on each immediate subgoal until there are
no more subgoals, or until REDUCE finds a subgoal that
is not satisfied.
· If REDUCE has found a subgoal that is not satisfied,
announce that the goal is not satisfied; otherwise,
announce that the goal is satisfied.
using REDUCE-OR
· Use REDUCE on each subgoal until REDUCE finds a
subgoal that is satisfied.
· If REDUCE has found a subgoal that is satisfied,
announce that the goal is satisfied; otherwise, announce
that the goal is not satisfied.
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6. Problem Reduction
• And-or graphs
• AO* algorithm
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8. Constraint Satisfaction
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8. Constraint Satisfaction
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9.Mean Ends Analysis
To perform means-ends analysis,· Until the goal is reached or no more procedures areavailable,- Describe the current state, the goal state, and
the difference between the two.- Use the difference between the current state
and goal state, possibly with the description ofthe current state or goal state, to select apromising procedure.
- Use the promising procedure and update thecurrent state.
· If the goal is reached, announce success; otherwise,announce failure.
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