Distributed automated planning and execution platform for designing and running complex processes

Abstract

A distributed automated planning and execution platform for designing, instantiating, and running complex and evolving processes is provided, comprising an automated planning service that receives an automated planning job, constructs a simulation using known information about the world, system resources, and other contextual data, assigns individual work tasks to worker nodes for processing, analyzes the results and assigns uncertainty values as needed, and produces an action plan as output.

What is claimed is: 
     
         1 . A system for management and tracking of collaborative projects, comprising:
 an automated planning service comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to:
 operate a plurality of master nodes, each master node in turn operating a plurality of worker nodes; 
 receive a plurality of simulation conditions at a master node; 
 construct a plurality of simulation components based on the received simulation conditions; 
 construct a planning model based on the received simulation conditions; 
 assign, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation; 
 analyze results of each of the plurality of discrete simulation tasks as they are completed; and 
 provide the analyzed results as output. 
   
     
     
         2 . The system of  claim 1 , wherein the simulation conditions are retrieved from a data storage. 
     
     
         3 . The system of  claim 1 , wherein the simulation conditions are received via a RESTful API. 
     
     
         4 . The system of  claim 1 , wherein the analysis comprises determining an uncertainty value for at least a portion of the results. 
     
     
         5 . The system of  claim 1 , wherein the analysis comprises determining whether a plurality of end conditions have been met, and if the plurality of end conditions have been met, halting the assignment of discrete simulation tasks to worker nodes. 
     
     
         6 . A method for management and tracking of collaborative projects, comprising the steps of:
 operating, at an automated planning service, a plurality of master nodes, each master node in turn operating a plurality of worker nodes;   receiving a plurality of simulation conditions at a master node;   constructing a plurality of simulation components based on the received simulation conditions;   constructing a planning model based on the received simulation conditions;   assigning, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation;   analyzing results of each of the plurality of discrete simulation tasks as they are completed; and   providing the analyzed results as output.   
     
     
         7 . The method of  claim 6 , wherein the simulation conditions are retrieved from a data storage. 
     
     
         8 . The method of  claim 6 , wherein the simulation conditions are received via a RESTful API. 
     
     
         9 . The method of  claim 6 , wherein the analysis comprises determining an uncertainty value for at least a portion of the results. 
     
     
         10 . The method of  claim 6 , wherein the analysis comprises determining whether a plurality of end conditions have been met, and if the plurality of end conditions have been met, halting the assignment of discrete simulation tasks to worker nodes.