1. An online learning dialog system comprising: one or more processing units; memory communicatively coupled to the one or more processing units, the memory having stored instructions that, when executed by the one or more processing units, configure the online learning dialog system to implement: a speech model that receives a speech input and provides speech events; a decision engine model that receives the speech events from the speech model and selects an action based, at least in part, upon a probability distribution, the probability distribution being associated with uncertainty regarding a plurality of parameters of the decision engine model applied to the speech input, wherein the probability distribution is: defined by an influence diagram that is configured to maximize long term expected utility and apply the Thompson strategy; and expressed as: p ⁡ ( U , V ⁢ | ⁢ D , Θ ) = ∏ X ∈ U ⋃ V ⁢ p ⁡ ( X ⁢ | ⁢ Pa ⁡ ( X ) , Θ X ) where U denotes chance variables, D denotes decision variables, and V denotes value variables; where Pa(X) denotes a set of parents for node X; and where Θ X denotes a subset of parameters related to the applied speech input in Θ that define local distribution of X; and, a learning component that in an online manner modifies at least one of the parameters of the decision engine model based upon feedback associated with the selected action, wherein the feedback comprises a lack of verbal input from a user of the system or an environment within a predefined period of time.