Improving Search by Utilizing State Information in OPTIC Planners
  Compilation to LP
  Automated planners are computer tools that allow autonomous agents to make
strategies and decisions by determining a set of actions for the agent that to
take, which will carry a system from a given initial state to the desired goal
state. Many planners are domain-independent, allowing their deployment in a
variety of domains. Such is the broad family of OPTIC planners. These planners
perform Forward Search and call a Linear Programming (LP) solver multiple times
at every state to check for consistency and to set bounds on the numeric
variables. These checks can be computationally costly, especially in real-life
applications. This paper suggests a method for identifying information about
the specific state being evaluated, allowing the formulation of the equations
to facilitate better solver selection and faster LP solving. The usefulness of
the method is demonstrated in six domains and is shown to enhance performance
significantly.
