Patent ID: 8285581

Claim:
A computer-implemented method for sequential decision making for customer relationship management, comprising: providing customer data comprising stimulus-response history data for a plurality of customers, said stimulus response history data being derived from event data for said customers; in a processor, automatically generating actionable rules for optimizing a sequence of decisions over a period of time based on said stimulus-response history data; estimating a value function using batch reinforcement learning with function approximation, said function approximation representing the value function as a function of state features and actions; and transforming an output of the value function estimation into said actionable rules, wherein the estimating of the value function comprises: estimating a function approximation of the value function of a Markov Decision Process underlying said stimulus-response history data for the plurality of customers used as training data; and iteratively applying a regression model to the training data which comprises sequences of states, actions and rewards resulting for said plurality of customers, and updating in each iteration a target reward value for each state-action pair.