Another strategy for achieving more nearly practical performance has been to precode (and to some extent learn) more “reactive” (as opposed to deliberative) ways of participating in dialogue. Reactive techniques include (i) formulaic, schema-based responses (reminiscent of ELIZA) where such responses are likely to be appropriate; (ii) rule-based intention and plan recognition; e.g., an automated travel agent confronted with the elliptical input  “Flights to Orlando” can usually assume that the user wishes to be provided with flight options from the user's current city to Orlando, in a time frame that may have been established previously; (iii) statistical domain plan recognition based on probabilistic modeling of the sequences of steps typically taken in pursuit of the goals characteristic of the domain; and (iv) discourse state modeling by classifying speech acts (or utterance acts) and dialogue states into relatively small numbers of types, and viewing transitions between dialogue states as events determined by the current state and current type of speech act. For example, in a state where the dialogue system has no immediate obligation, and the user asks a question, the system assumes the obligation of answering the question, and transitions to a state where it will try to discharge that obligation.