The document summarizes a session on planning in artificial intelligence. It discusses classical planning approaches like state-space search and planning graphs. It also covers planning and acting in real-world domains with aspects like time, schedules, and resources. Finally, it describes different planning algorithms like situation-space search with progression and regression approaches.

1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 26
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 26
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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. State space Planning
• Searching Strategies
– possible plans
– "initial plan"
– goal node
– The node itself contains all of the information for
determining a solution plan (e.g. sequence of
actions)
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5. Planning with state space search
Planning as Search:
– There are two main approaches to solving planning
problems, depending on the kind of search space
that is explored:
• Situation-space search
• Planning-space search
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6. Situation-Space Search
• In situation space search
– The search space is the space of all possible states
or situations of the world
– Initial state defines one node
– A goal node is a state where all goals in the goal
state are satisfied
– A solution plan is the sequence of actions (e.g.
operator instances) in the path from the start node
to a goal node.
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7. Box Problem (Hill climbing )
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8. Planning-space search
• The search space is the space of all possible
plans
• A node corresponds to a partial plan
• Initially we will specify an "initial plan" which is
one node in this space
• A goal node is a node containing a plan which is
complete, satisfying all of the goals in the goal
state
• The node itself contains all of the information
for determining a solution plan (e.g. sequence
of actions)
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9. Example
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10. Situation Planning Algorithms
• There are 2 approaches to situation-space
planning:
– Progression situation-space planning
– Regression situation-space planning
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11. Progression(Forward) situation-
space planning
• Forward search through the space of states, starting
from the initial state and using the problem’s actions
until the member of goal states are found.
• You can use any search method you like (i.e. BFS,
DFS, A*)
• Since the forward search is prone to exploring unrelated
nodes, and tend to have many state spaces, it has been
regarded as inefficient without the help of accurate
heuristics.
• Disadvantage: huge search space to explore, so
usually very inefficient
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12. Algorithm
1. Start from initial state.
2. Find all operators whose preconditions are
true in the initial state.
3. Compute effects of operators to generate
successor states.
4. Repeat steps #2-#3 until a new state satisfies
the goal conditions.
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13. Regression (Backward) situation-
space
• Backward search through the relevant states, starting
from the goal state, to the initial state using the inverse
of actions.
• Backward-chaining from goal state to initial state
• Regression situation-space planning is usually more
efficient than progression because many operators are
applicable at each state, yet only a small number of
operators are applicable for achieving a given goal
• Hence, regression is more goal-directed than
progression situation- space planning.
• Disadvantage: cannot always find a plan even if one
exists!
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14. Algorithm
1. Start with goal node corresponding to goal to
be achieved
2. Choose an operator that will add one of the
goals
3. Replace that goal with the operator's
preconditions
4. Repeat steps #2-#3 until you have reached the
initial state
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15. Topics to be covered in next session 27
• Heuristics planning
Thank you!!!
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