burlap.behavior.singleagent.planning.deterministic

Class MultiStatePrePlanner

java.lang.Object

burlap.behavior.singleagent.planning.deterministic.MultiStatePrePlanner

public class MultiStatePrePlanner
extends java.lang.Object

This is a helper class that is used to run a valueFunction from multiple initial states to ensure that an adequate plan/policy exists for each them. It makes uses of an iterable state condition test to define the states from which planning should performed or a collection of state objects.

Author:

James MacGlashan

Constructor Summary

Constructors

Constructor and Description
MultiStatePrePlanner()

Method Summary

Methods

Modifier and Type	Method and Description
static void	runPlannerForAllInitStates(Planner planner, java.util.Collection<State> initialStates) Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.
static void	runPlannerForAllInitStates(Planner planner, StateConditionTestIterable initialStates) Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MultiStatePrePlanner

public MultiStatePrePlanner()

Method Detail

runPlannerForAllInitStates

public static void runPlannerForAllInitStates(Planner planner,
                              StateConditionTestIterable initialStates)

Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.

Parameters:

planner - the valueFunction to be used.

initialStates - a StateConditionTestIterable object that will iterate over the initial states from which to plan.

runPlannerForAllInitStates

public static void runPlannerForAllInitStates(Planner planner,
                              java.util.Collection<State> initialStates)

Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.

Parameters:

planner - the valueFunction to be used.

initialStates - a collection of states from which to plan.