CSC%20550:%20Introduction%20to%20Planning%20Fall%202004

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CSC 550 Introduction to PlanningFall 2004

• Goals
• 1. Defining Planning
• 2. Early planning systems
• 3. Types of Planning and their challenges
• - Hierarchical
• - Non-Hierarchical,
• - Other common approaches
• 4. Current Projects and challenges

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Definition of Planning

• Planning is reasoning about future events in
  order to establish a series of actions to
  accomplish a goal.
• A common approach to planning is representing a
  current state and determining the series of
  actions necessary to reach the goal state. (or
  vice versa)
• Problem solving technique
• Plans are created by searching through a space of
  possible actions until the sequence necessary to
  accomplish the task is discovered.
• Planning is a specific kind of state space search
• Deals with steps and goals that interact
• Initial State and Goal State (Next Slide)-
  Hallmark example with blocks

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Initial State and Goal State
These two diagrams show the initial state and
goal state for a simple planning problem. The
purpose of planning is to find a sequence of
actions that gets from the initial state to the
goal state, or from the goal state back to the
initial state. Possible Solution
Pickup(c),Table(c), Pickup(b), PlaceOn(c),
Pickup(a), PlaceOn(b)
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Search in Planning

• Planning involves search through a search space
• Progression choose an action whose preconditions
  are met until a goal state is reached
• A forward approach, simple algorithm, but can
  have large branching factor
• Regression choose an action that matches an
  unachieved subgoal while adding unmet
  preconditions to the set of subgoals. Continue
  until the set of subgoals is empty.
• A backward approach, goal oriented, tends to be
  more efficient

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Linear and Non-Linear

• Linear
• Solving one goal at a time with a stack of
  unachieved goals, subgoals are solved in the same
  order as the actions of the plan to be executed
• Depth-first search
• Non-Linear
• Solving subgoals that are in a set of unachieved
  goals, can solve parallel branches of the set of
  goals arbitrarily.
• Breadth-first search
• Tends to avoid backtracking
• More flexible execution
• Representing plans and search algorithms are more
  complex than linear

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Definition of Planning, cont.

• Applications in robotics, expert systems,
  manufacturing, and natural language understanding
• Expert System reasoning about events occurring
  over time
• Manufacturing process control
• Robotics organization of partial plans in a
  solution
• Natural Language human interactions, goal
  oriented

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Definition of Planning, cont.

• Potential Benefits of Planning
• Helps to solve large problems quickly
• Find better solutions
• Can resolve goal conflicts
• Can provide methods for error recovery
• General Limitations
• Complexity of state spaces, must represent the
  whole environment, search can become
  exponentially large
• Frame Problem- being able to represent what
  changes and what remains unchanged following an
  action (by default, things stay the same, unless
  you tell it otherwise)
• Inconsistencies between the real world and the
  program model, comes back to the complexity issue

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Early Planning Systems

• 1956 Logic Theorists Newell, Shaw, and Simon
• - One of the first to use heuristics, proved
  theorems in propositional calculus, operated by
  using backward reasoning from the theorem to the
  axioms
• - limited by its heuristics and certain theorems
  could not be proven
• 1957-1969 GPS Newell, Shaw, and Simon
• - The General Problem Solver, how to solve human
  intelligence problems, areas propositional
  calculus proofs, puzzles, symbolic integrations,
  etc.
• - Introduced means-end analysis which tried to
  find the difference between the current state and
  goal, then used a table to find an action to
  minimize the difference between the two states.

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Non-Hierarchical Planners

• Earliest Method of Planning
• Made no distinction between more and less
  important plan elements
• Slowed by getting hung up on less important
  elements
• Lack of structure led to poor performance with
  complex problems
• Example STRIPS
• STanford Research Institute Planning System
• 1971 by Fikes and Nilsson
• Used to run the SHAKEY robot of the 1970s
• The block example

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STRIPS

• Goal states are maintained on a stack
• If the top goal on the stack matches the current
  state, the goal is removed from the stack
• Also adds to the goal stack any sub-goals found
  while trying to get to the goal state
• Initial State (On(C,A) and OnTable(B)) and Goal
  State (On(C,B) and On(A,C))

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STRIPS
This diagram is an example of a STRIPS search
graph with goal stacks included. The goal state
is On(A,C) On(C,B).
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STRIPS
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STRIPS

• Problems
• Does not always find the optimal solution (Ex
  On(A,B) before On(B,C))
• Some simple problems that cannot be solved
  switching the contents of two registers
• Cannot tell the difference between important
  information and details
• No guidelines to tell it what to do first
• Cannot know when it is going down a bad path
• Memory Goal Stack and Start State
• Next slide

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Current State State Description Goal Stack
1 CONT(X,A) CONT(X,B) CONT(Y,A)
CONT(Y,B)
CONT(Z,0)
2 CONT(X,A) CONT(X,B)
CONT(Y,B) CONT(Y,A)
CONT(Z,0) CONT(X,B) CONT(Y,A)
3 CONT(X,A) CONT(r,B) CONT(X,t)
CONT(Y,B) Assign(X,r,t,B)
CONT(Z,0) CONT(Y,A)
CONT(X,B) CONT(Y,A)
4 CONT(X,B) CONT(Y,A)
CONT(Y,B) CONT(X,B) CONT(Y,A)
CONT(Z,0)

• At State 3, the program sees the goal CONT(X,B)
  can be completed. The problem is the contents of
  Y are never copied into Z and is lost. At State
  4, the goal cannot be met due to A being lost.
• An example of subgoals that conflict

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Hierarchical Planners

• Makes a distinction between more and less
  important parts of the plan
• Example When purchasing a new Jesuit statue, we
  first need to decide where to get the funds. It
  doesnt make sense to find a good place for it on
  campus before you have the money.
• Example ABSTRIPS 1974 Sacerdoti
• Abstract-Based STRIPS
• Like STRIPS but plans in a hierarchy, greatly
  reduces search space, and is more efficient at
  solving large problems
• Certain preconditions are judged as more
  important than others by adding weights to those
  elements
• Finds early recognition of bad paths and gets rid
  of wasted search
• Uses a hierarchy of abstraction levels
• Solves highest level of abstraction. If that
  passes, it increases level of detail

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ABSTRIPS

• Example PUSH-THRU-DOOR (bx, dx, rx)
• Preconditions 6 PUSHABLE (bx)
• 6 IS-A (dx, DOOR)
• 6 IS-A (rx, ROOM)
• 2 STATUS (dx, OPEN)
• 1 NEXT-TO (bx, dx)
• 1 NEXT-TO (ROBOT, bx)
• Each number represents the weight of the element.
  We see that the elements with weight 6 are the
  first elements we need to know in order to get to
  the goal state.

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Common Approaches

• Opportunistic Planning
• - Situation-based triggering of new goals,
  subgoals, and/or partial plans
• - Implementation is a bottom-up approach,
  whereas Hierarchical planners start with the goal
  and move down
• Resource-Sensitive Planning
• - Takes into account the resources available and
  the cost involved in plans
• - Interval Logic, James Allen-1983
• - A system which represents actions where
  timing is important
• - Uses time interval relations (before, meets,
  overlaps, during, etc.)
• - Links are made between actions that satisfy
  interval relations
• Conditional and Uncertainty Planning
• - Deals with information that is incomplete,
  devises generic plans that leave out specifics,
  details are filled in later
• - Emphasis on uncertainty in real world
  applications

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Current Research Focus

• Emphasis on Hierarchical Planning with special
  consideration given to
• Complex Conditions
• Availability of Resources
• Uncertainty
• More practical approaches considering the complex
  world that we live in.
• - Biggest difficulty is in the representation of
  complex states and actions
• - Problems also arise as complexity leads to a
  sometimes exponentially increased search

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Classical Planning Assumptions

• Perfect Information
• Deterministic Effects
• Instantaneous Execution
• Solo Agent
• No concern over time, cost, resources
• Etc.
• These assumptions were made early on because
  complex tasks were too complex to solve. These
  assumptions were used to complete smaller tasks
  (blocks).
• Modern approaches deal with the scaling issue.

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Recent Projects

• ASPEN
• Automated Scheduling and Planning ENvironment
• NASA application
• Used for mission design
• Surface rover planning
• Ground antenna utilization
• NASA operators send goals to ASPEN, then ASPEN
  sends commands to the spacecraft, ASPEN
  continually receives updates from the spacecraft
  and the current plan is updated to reflect the
  necessary environmental changes
• PLANET
• Applications workflow management, intelligent
  manufacturing, robot planning, aerospace and
  airline planning
• EXCULIBUR
• Computer gaming environment
• Pursue their given goals and adapt to new
  opponents
• Dynamic nature of games, uncertainty

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Sources of Information

• Luger, George F., Artificial Intelligence
  Structures and Strategies For Complex Problem
  Solving, Fourth Edition, Pearson Education
  Limited 2002.
• Nilsson, Nils J. Principles of Artificial
  Intelligence, Tioga Publishing Co., 1980.
• Shirai, Yoshiaki and Tsujii, Jun-ichi, Artificial
  Intelligence Concepts Techniques, and
  Applications, Iwanami Shoten, Publishers, Tokyo,
  1982.
• http//www.cs.washington.edu/ai/PLAN
• http//www.cs.dartmouth.edu/brd/Teaching/AI/Lectu
  res/Summaries/planning.html
• www.cs.bham.ac.uk/mmk/Teaching/Planning/l6.html
• www.cs.umbc.edu/671/fall03/slides/25
• http//www-2.cs.cmu.edu/reids/planning/handouts/R
  eprSearch.pdf
• http//vitalstatistix.nicve.salford.ac.uk/planet2/
  desc.html
• http//www.ai-center.com/projects/excalibur/goals.
  html
• http//www-aig.jpl.nasa.gov/public/planning/projec
  ts/current.html