AI_Session 28 planning graph.pptx

1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 28
by
Asst.Prof.M.Gokilavani
VITS
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2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third
Edition, Stuart Russell and Peter Norvig, Pearson
Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight
(TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny
Winston, Pearson Education.
• Artificial Intelligence, Shivani Goel, Pearson
Education.
• Artificial Intelligence and Expert Systems- Patterson,
Pearson Education.
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3. Topics covered in session 28
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Planning
Classical Planning: Definition of Classical Planning,
Algorithms for Planning with State-Space Search,
Planning Graphs, other Classical Planning
Approaches, Analysis of Planning approaches.
Planning and Acting in the Real World: Time,
Schedules, and Resources, Hierarchical Planning,
Planning and Acting in Nondeterministic Domains,
Multi agent Planning.

4. Planning graphs
• Graph Plan is an algorithm based on planning
graph
• Planning graphs are also used as a source of
heuristics (an estimate of how many steps it
takes to reach the goal)
• Planning graph is an approximation of a
complete tree of all possible actions and their
results Planning graphs work only for
propositional planning problems—ones with no
variables.
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7. Planning graphs
Planning graph is organized into levels.
• Level S0: initial state, consisting of nodes
representing each fluent that holds in S0.
• Level A0: each ground action that might be
applicable in S0 Then alternate Si and Ai.
• Si contains fluent which could hold at time i,
(may be both P and ¬P); literals may show up
too early but never too late.
• Ai contains actions which could have their
preconditions satisfied at i.
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8. Mutex
• A mutex between two actions indicates that it
is impossible to perform these actions in
parallel.
• A mutex between two literals indicates that it
is impossible to have these both literals true at
this stage.
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9. Example
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10. Constructing a planning graph
• All action schemas must be propositionalized
• Generate all possible grounded actions so no
variables are left.
• Start with S0 = all initially true fluents
• Construct Ai = all actions whose preconditions are
satisfied by Si
• Construct Si = all fluents made true by the effects
of the actions in Ai-1
• All levels in Ai have the NO-OP action which
passes all true fluents in Si to Si+1.
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11. Add links:
• Between levels
– From fluents in Si to preconditions of actions in Ai
– From effects of actions in Ai to fluents in Si+1
• Within Levels (mutual exclusion)
– Action Mutex Link: Two actions compete for
resources
– State Mutex Link: Two fluents that cannot both be
true at the same time
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12. Rules for Constructing Mutex Links
Action Mutex:
• Inconsistent Effects: One action negates the effect of the
other (e.g., Eat(Cake) and Bake(Cake) )
• Interference: One of the effects of one action is the negation
of a precondition of the other (e.g., Eat(Cake) and the
persistence of Have(Cake))
• Competing Needs: One of the preconditions of one action is
mutually exclusive with a precondition of the other
• Depends on State Mutex links in previous level
State Mutex:
• One is the negation of the other.
• All pairs of actions that could make both true have Act on
mutex links between them.
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13. Example
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14. Topics to be covered in next session 29
• GRAPH-PLAN Algorithm
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Thank you!!!