A system and method for automatically optimizing states of communications and operations in a contact center, using a reinforcement learning module comprising a reinforcement learning server and an optimization server introduced to existing infrastructure of the contact center, that, through use of a model set up a fully observable Markov decision process within a known time period, a resulting hyper-policy is computed through backwards induction to provide an optimal action policy to use in each state of a contact center, thereby ultimately optimizing states of communications and operations for an overall return over the time period considered.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for optimizing communication operations in a contact center using a reinforcement learning server, comprising: a reinforcement learning server comprising at least a first plurality of programming instructions stored in a first memory and operating on a first processor of a first computing device, wherein the first plurality of programming instructions, when operating on the first processor, cause the first processor to: receive a plurality of historical data from a contact center; form a partially-observable Markov chain model by fitting at least a portion of the historical data with a Baum-Welch algorithm to infer model parameters associated with hidden states based on known observations; develop a training set for use in the partially-observable Markov chain model, the training set being based at least in part on historical data; provide the partially-observable Markov chain model to an optimization server; record and analyze the results of the optimization server's operation; an optimization server comprising at least a second plurality of programming instructions stored in a second memory and operating on a second processor of a second computing device, wherein the second plurality of programming instructions, when operating on the second processor, cause the second processor to: receive a partially-observable Markov chain model from a reinforcement learning server; assign and apply a plurality of actions to each of a plurality of states in the partially-observable Markov chain model; direct the operation of a plurality of contact center systems based at least in part on the assigned actions; record and analyze a plurality of observations based on the execution of the assigned actions; provide the observation data to the reinforcement learning server; a retrain and design server comprising at least a third plurality of programming instructions stored in a third memory and operating on a third processor of a third computing device, wherein the third plurality of programming instructions, when operating on the third processor, cause the third processor to: observe and analyze a plurality of historical data from a contact center; provide at least a portion of the historical data to a reinforcement learning server for use in a partially-observable Markov chain model; define a plurality of reward values to direct the operation of the reinforcement learning server; and design and train a Markov decision process model based at least in part on the partially-observable Markov chain model, using at least a portion of the defined reward values. 2. A method for optimizing states of communications and operations in a contact center using a reinforcement learning server, comprising the steps of: receiving, at a retrain and design server comprising at least a first plurality of programming instructions stored in a first memory and operating on a first processor of a first computing device, a plurality of historical data from a contact center; defining a plurality of reward values to direct the operation of a reinforcement learning server; providing at least a portion of the historical data to a reinforcement learning server for use in a partially-observable Markov chain model; forming, using a reinforcement learning server comprising at least a second plurality of programming instructions stored in a second memory and operating on a second processor of a second computing device, a partially-observable Markov chain model based at least in part on the historical data, by fitting at least a portion of the historical data with a Baum-Welch algorithm to infer model parameters associated with hidden states based on known observations; assigning, using an optimization server comprising at least a third plurality of programming instructions stored in a third memory and operating on a third processor of a third computing device, a plurality of actions to each of a plurality of states within the partially-observable Markov chain model; directing the operation of a plurality of contact center systems based at least in part on the assigned actions; and training a Markov decision process model based at least in part on the partially-observable Markov chain model, using at least a portion of the defined reward values.