The document discusses classical planning approaches in artificial intelligence. It covers topics like planning algorithms, representation of states, goals, and actions. Classical planning assumes fully observable, deterministic, static, and discrete environments. Planning involves finding a sequence of actions to achieve a goal state from the initial state. Different planning approaches like state space search, planning graphs, and logical planning were discussed. Examples like the vacuum cleaner agent and box world domain were provided to illustrate classical planning concepts and representations.

1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 25
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 25
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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 Agent Eg: vacuum cleaner
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5. Purpose of Planning
• The purpose of planning is to find a sequence of actions that
achieves a given goal when performed starting in a given state.
• In other words, given a set of operator instances (defining the
possible primitive actions by the agent), an initial state
description, and a goal state description or predicate, the
planning agent computes a plan.
• Start Final Operator Instances PLAN.
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6. Type of Environments
– fully observable
• we see everything that matters
– deterministic
• the effects of actions are known exactly
– static
• no changes to environment other than those caused by
agent actions
– discrete
• changes in time and space occur in quantum amounts
– single agent
• no competition or cooperation to account for
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7. What is plan?
• The task of coming up with a sequence of actions
that will achieve a goal is called Planning.
• Planning Problems so far:
– search-based problem solving
– logical planning
• Consider only environment that are fully
observable, deterministic, finite, static (change
happens only when the agent acts), and
discrete (in time, action, objects and effects).
These are called Classical Planning.
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8. State and Goal: Classical planning
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9. Non classical planning
• Non classical planning is for partially
observable or stochastic environments and
involves a different set of algorithms and agent
designs.
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10. Why we need planning?
• Intelligent agents must operate in the world.
– Take intelligent actions
– Compose actions together to achieve complex
goals
• Change the world to suit the needs. Agents
need to reason about what the world will be
like after executing a sequence of actions
– Need to reason about dynamic environment
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12. Planning algorithm
• Generate a goal to achieve
• Construct a plan to achieve goal from current
state
• Execute plan until finished
• Begin again with new goal
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13. The language planning problems
• Planning algorithms should take advantage of the
logical structure of the problem.
• The key is to find a language that is expressive enough
to describe a wide variety problems, but restrictive
enough to allow efficient algorithms to operate over it.
• The problem should be expressed in a suitable logical
language.
• Planning is considered different from problem solving
because of the difference in the way they represent
states, goals, actions, and the differences in the way
they construct action sequences.
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14. Representation of states
– Planners decompose the world into logical conditions and
represent a state as conjunction of positive literals
– Example; prepositional logic P V Q
Representation of goals
– A goal is partially specified state, represented as a conjunction of
positive ground literals, such as 𝐏 ∧Q.
– A propositional state s satisfies a goal g if s contains all the
atoms in g.
– Example; P∧ 𝑸 ∧R satisfies the goal 𝐏 ∧Q
Representation of actions
– An action is specified in terms of the pre conditions that must
hold before it can be executed and the effects that ensue when it
is executed.
– Example: Action(Fly(p,From,To),
– PRECOND: At(p, from) ∧ Plane(p) ∧ Airport(from) ∧
Airport(to), EFFECT: ~At(p,from) ∧ At(p,to))
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15. Action schema
• Action schema, meaning that it represents a
number of different actions that can be derived
by instantiating the variables p, from and to
different constants. In general action schema
consists of three parts:
– The action name and parameter list
– The precondition is a conjunction of function-free
positive literals stating what must be true in state
before the actions can be executed.
– The effect is a conjunction of function-free literals
describing how the state changes when the action is
executed.
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16. Example : Box world
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19. Topics to be covered in next session 26
• Planning state space
Thank you!!!
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