Planning
The planning problem
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Eg. Buying a book
Action->have (ISBN No.), Buy(ISBN No.)
Eg. Delivery a set of overnight packages
Nearly decomposable
The language of planning problem
• STRIPS language
Representation of states
Closed world assumptions
• Any condition that are not mentioned are
assumed to be false
Representation of goals
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A goal is a partially specified state
Eg.
The state rich A famous
A miserable satisfies the goal rich A famous
Representation of actions
• An action is specified in the terms of the
preconditions that must hold before it can be
executed and the effects that ensure when it I
executed.
Expressiveness and extensions
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Simpler and efficient algorithms
Eg. Air cargo problem
10 airports, 5 planes
Fly (p, from, to) schema into 10X5X5=250 purely
proposition actions.
• Action Description Language (ADL)
Planning Domain Description
Language (PDDL)
• Standard syntax called PDDL
• Allows sub-languages for STRIPS, ADL,
hierarchical task networks
• STRIPS and ADL are adequate for real domains
Examples of planning problems
• Air cargo transport
2. The space tire problem
3. The blocks world
Planning with state-space search
Forward state-space search
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Initial state
Actions
Goal test
Step cost
Backward state-space search
Heuristics for state-space search
• Two approaches:
1. Relaxed problem
2. Sub-goal independence
Partial order planning
• Problem decomposition (works on sub-goals independently)
• Any planning algorithm that can place two actions into a plan
without specifying which comes first is called a POP.
POP agorithm
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4 components
Set of actions
Set of ordering constraints
Set of casual links
Set of open preconditions