Decision Making Strategies for Finite-State Bi-automaton in Dialog Management

Stochastic regular bi-languages has been recently proposed to model the joint probability distributions appearing in some statistical approaches of spoken dialog systems. To this end a deterministic and probabilistic finite-state bi-automaton was defined to model the distribution probabilities for the dialog model. In this work we propose and evaluate decision strategies over the defined probabilistic finite-state bi-automaton to select the best system action at each step of the interaction. To this end the paper proposes some heuristic decision functions that consider both action probabilities learn from a corpus and number of known attributes at running time. We compare heuristics either based on a single next turn or based on entire paths over the automaton. Experimental evaluation was carried out to test the model and the strategies over the Let’s Go Bus Information system. The results obtained show good system performances. They also show that local decisions can lead to better system performances than best path-based decisions due to the unpredictability of the user behaviors.

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