Improving Search by Utilizing State Information in OPTIC Planners Compilation to LP

TL;DR

This paper introduces a method to improve the efficiency of OPTIC planners by utilizing state information to optimize linear programming solver selection, significantly enhancing performance across multiple domains.

Contribution

The paper proposes a novel approach to leverage state-specific information in OPTIC planners to reduce LP solving time and improve overall planning efficiency.

Findings

Performance improved in six domains

Faster LP solving with state-aware formulation

Enhanced scalability of OPTIC planners

Abstract

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.