computational system. Distinguish-ing features are the generic nature of the system, and the underlying technology which is finite-state. Results of a two-pronged evaluation dealing with naturalness and ease of use are described.

natural language arguments. The current state of the art in argumentation does not have any formal translation method. In this paper, we discuss this lacuna. First, there are robust, wide-coverage computational linguistic systems which au-tomatically translate multi-sentential structures into first order

: Finite State Machines (FSM) provide a powerful way to describe dynamic behavior of systems and components. However, the implementation of FSMs in OO languages, often suffers from maintenance problems. The State pattern described in [1] that is commonly used to implement FSMs in OO languages, also

Within the framework of Natural Spoken Dialogue systems, this paper describes a method for dynamically adapting a Language Model (LM) to the dialogue states detected. This LM combines a standard n-gram model with Stochastic Finite State Automata (SFSAs). During the training process, the sentence

from Natural Language This paper argues that a finite-state language model with a ternary expression representation is currently the most practical and suitable bridge between natural language processing and information retrieval. Despite the theoretical computational inadequacies of finitestate

from Natural Language This paper argues that a finite-state language model with a ternary expression representation is currently the most practical and suitable bridge between natural language processing and information retrieval. Despite the theoretical computational inadequacies of finitestate

;spadework" of finding a precise mathematical formulation and method of solution, possibly notated in a "convenient problem-oriented language" whose symbols are "more closely related to the mathematical problem to be solved". The major changes since this was written are a) that many computer users do

the construction of an analytical framework that could inform, and better organize, further research. However, given the dif culty of the task, and the necessarily collective character of this endeavour, what is presented here should be considered, literally, as materials to be used in the building of a

In this paper we introduce a stochastic model for dialogue systems based on Markov decision process. Within this framework we show that the problem of dialogue strategy design can be stated as an optimization problem, and solved by a variety of methods, including the reinforcement learning approach

, due to the large number of states and irregular transitions of such systems. One promising method for generating reasonably-sized models from programs is the use of data abstraction, in which the program data is mapped from a large set of possible values to a much smaller set of abstract values