This document discusses artificial intelligence planning and summarizes key ideas in 3 sentences or less: Artificial intelligence planning involves representing problems as initial states, goals, and actions that can transform states. Classical planning uses representations like STRIPS to search for sequences of actions to transform the initial state into a goal state. Recent research focuses on more expressive representations and techniques like partial-order planning to reduce the complexity of searching large problem spaces.

1. Outline
Artificial Intelligence
Planning
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Definition of AI planning
Key ideas: representation and search
STRIPS representation
partial-order planning and other techniques
for reducing complexity of plan search
• limiting assumptions of classical planning
• Recent trends on planning research
Blocks world example
A planning problem is specified by:
• an initial state
• a goal
• a model of the actions that can be performed
A solution to a planning problem is a sequence of
actions (a plan) that transforms the initial state
into a state that satisfies the goal description,
and possibly optimizes some measure of
performance.
B
A
initial state
goal state
Plan: Move C from A to table
Move B from table to C
Move A from table to B
Distinctions
• Planning is closely-related to problemsolving and search, but there are important
differences
• Planning is also related to, but different
from, scheduling, theorem-proving,
automatic programming, and work in
control theory, decision theory and
operations research, etc.
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B
C
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Applications
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Robot navigation and control
Spacecraft control (Deep Space One)
factory and manufacturing operations
intelligent tutoring
natural language generation and
understanding (plan recognition)
• many others
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2. Beginnings of planning research
Key issues: Representation and search
• A domain-independent planner consists of a
language for expressing planning problems and
an associated search algorithm for finding plans
• Examples:
Situation calculus (1959-)
QA3 (1969)
frame problem
Resolution theorem-proving (1965)
STRIPS (1972)
GPS (1969)
Epistemological and heuristic adequacy
The two key issues in planning research are:
– modeling actions and change
– organizing the search for plans
“There is a spectrum of more and more expressive languages
for representing the world, an agent’s goals, and possible actions.
The task of writing a planning algorithm is harder for more
expressive representation languages, and the speed of the resulting
algorithm decreases as well.” (Dan Weld)
– situation calculus and resolution theorem-proving
– STRIPS language and planning algorithm
– many others
STRIPS Language (1)
• States and goals described by conjunctions of
predicates applied to constant symbols
• Example:
– constants: blocks A, B, C, table
– predicates:
• (clear A) = “block A has nothing on it”
• (on A, B) = “block A is on block B”
• (on B, table) = “block B is on the table”
– state: (clear A) ∧ (on A B) ∧ (on B table)
STRIPS Language (2)
• Actions represented as follows:
– name:
– precondition list:
– delete list:
– add list:
(move x from y to z)
(on x y) ∧ (clear x) ∧ (clear z)
(on x y) ∧ (clear z)
(on x z)
• An action (operator) with variables is called
an action (operator) schema
Partial descriptions
• Only relevant aspects of the world need to be
included in state description
• STRIPS Assumption: Everything about the
world that is not mentioned by the action
schema remains unchanged by action – this is
“solution” to frame problem
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3. Influence
• The STRIPS planning algorithm had many
deficiencies and was never used again.
Many improved planning algorithms have
been developed since.
• The STRIPS language, and variations of it,
continues to be used by most planners
Search in situation space
Searching for a plan
• Besides modeling actions and change, the
second important issue in planning is how
to make the search for a plan efficient
• Distinction between searching in situation
space and plan space
Situation space for the blocks world
• States represent situations
• Edges represent actions that transform a
situation into a new situation
• Search strategies:
– forward from initial state (progression) or
backward from goal (regression or backward
chaining)
– total-order planning = plan is build action-byaction in forward or backward direction
Search in plan space
Plan space for the blocks world
• First used by planner called NOAH [1975].
• States represent partially specified plans.
• Edges represent plan-refinement operations (e.g.
adding actions or ordering constraints).
• Search strategies
– least-commitment = planner avoids making decisions
until a good reason to make a choice
– partial-order planning = decide what actions to
perform before deciding ordering of actions
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4. Example of nonlinear plan
(move A from table to B)
Finish
Start
(move B from table to C)
Example of partial-order planning
Advantages of partial-order planning
• By postponing choice about how to order actions,
can avoid search of exponential possible orderings
• It may take only a few steps to construct a plan that
would take thousands of steps using a standard
total-order approach
• The least-commitment approach means the planner
only needs to search in places where subplans
interact with each other
The null plan for the Sussman anomaly contains two actions:
*start* specifies the initial state and *end* specifies the goal.
• The following simple blocks-world planning
problem is called the “Sussman anomaly”
C
A
B
A
B
C
• The next few slides show some of the steps involved
in finding a plan using a partial-order planner
The plan after adding a causal link to support (on b c)
The agenda contains [(clear b) (clear c) (on b table) (on a b)]
The plan after adding a causal link to support (clear b)
The agenda is set to [(clear c) (on b table) (on a b)]
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