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M. Stefik, Planning and Meta-Planning (MOLGEN: Part 2). Artificial Intelligence 16 (2), 1981.

Author of the summary: Samuel Collins, 1999, collinss@bellsouth.net

Cite this paper for:

Planning and Meta-Planning (MOLGEN: Part 2)

Mark Stefik (PARC, Xerox)

1. Introduction
   * There exists the need for reasoning control and a control structure.

2. The Rationale for Layers
2.1 The trouble with agendas
    * Fixed order (order of the tasks being generated)
    * Priority (fixed)
    * Scheduler (dynamic priority)
    * All are at the agenda side, not the interpreter side
2.2 Recognizing the meta-problem
    * Goal at the meta-level is to find a solution to the problem.
Main Points

The research seeks to enhance the power of problem solvers by enabling
to reason about their own reasoning process.

The paper argues that the organization of a problem solver can be
simplified by partitioning problem solving knowledge into layers.

It describes a control structure, termed meta-planning, which enables
planner to reason (to some degree) about its own reasoning process.

A control structure is a framework for organizing decisions on when
of a plan can be applied and how they should be combined. A
control structure should:

1.      Take advantage of new information
2.      Make guesses and correct mistakes
3.      Should be able to recognize when an approach is succeeding
4.      Recognize when an approach is failing
5.      Decide what action to try
6.      Know when to make commitments and when to wait

Meta-Planning provides framework for partitioning control knowledge
layers so that flexibility is achieved.

The paper considers the control of decision making in planning. A
program, named MOLGEN, was used to study planning.

The Meta-Problem

Many of the important actions, goals, and constraints can be
as being on a meta-level

Any choices or evaluation criteria, which relate to the process of
solving, can be characterized as meta-level considerations.

Monolithic agenda approaches provide no meta-level concepts or global
prospective for scheduling and arbitration.

A Model for Planning


A trivial finite-state machine as the top-level interpreter
The factoring of the knowledge for using plausible and logical
from the planning operations
The development of a vocabulary of operators and concepts for
planning with constraints

Three layers and an interpreter
Laboratory space: Describes what can be done in the laboratory, but
when to do it in an experiment.
Design Space: Execute steps in order to create and refine the
Strategy space: Execute steps to create and execute the steps in the
Outer control loop
Creates and executes steps in the strategy space
The design operators plan by creating and scheduling laboratory steps
The strategy operators "meta-plan" by creating and scheduling design


Laboratory Space

Model of the objects and actions relevant to gene cloning experiments
Define the set of possible laboratory experiments by describing the
allowable laboratory objects and operators.
Operators represent physical process that can be carried out in the
genetics laboratory
Organized in four groups (Merge, Amplify, React, sort)

Design Space

Contains operators for planning
Planning can be viewed as operations on constraints (formulation,
propagation, and satisfaction)

Strategy Space and Interpreter

Strategy space is organized as four strategy operators:
Focus - Used to create and execute new design tasks
Resume - Restarts suspended design steps
Guess - Sends a guess message to the operator of every suspended
Undo - The strstegy operator for backtracking when a plan has become

When MOLGEn has run out of least-commitment changes to a plan, it
looks for
a plausible commitment that will allow it to continue with the design
process. This is recognized by MOLGEN when the Focus and Resume
operators have nothing to do.

Summary author's notes:

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Last modified: Fri Apr 7 11:23:08 EDT 2000