Motivation of Utterance of Autonomous Mobile Robots

Yuichiro ANZAI

Department of Computer Science, Keio University
3-14-1, Hiyoshi, Kouhoku-ku, Yokohama 223 JAPAN

A speech dialogue is advantageous over other ways of communication between humans and computers in that it needs no special training.
Much research has been done for speech dialogue systems, however, those systems are generally not able to work in dynamic environments. It is principally because they lack consideration of non-verbal information, and also relation between verbal and non-verbal information. Actually, human conversation contains, or is affected by, much non-verbal information that is somehow shared by speakers and hearers.
``Mental models'', often referred particularly in the field of cognitive science, may include this aspect, that is, integration of verbal and non-verbal information that represent the utterance situation for participants in conversation.
We believe that most of speech dialogue systems for real-world application must somehow include formulation and implementation of mental models in the above sense. Thus our goal has been to model and implement a dynamical construction process of mental models, using the domain of human-robot speech dialogue communication. Actually, last year we succeeded in formulating and implementing a speech dialogue system that integrates sensory information from robot's sensors as non-verbal information. This system, called Linta-II, is able to construct a mental model from the situation by using what we call the attention mechanism and by fusing sensor data, and use it to generate appropriate utterances.
One weakness of this system is that no ``motivation mechanism'' is implemented. Also, the system, though it is reactive to changes of external environments, needs more capacity for reactiveness to avoid danger. Our new system is able to motivate utterances by itself, and also can manage reactiveness, by incorporating a reactive planning algorithm like Georgeff's PRF (Procedural Reasoning System).
We suppose that scenes, in which a speech dialogue with a autonomous mobile robot is embedded, is limited. Dialogue plans are described as reactive plans that possess an ability to respond immediately to changes of linguistic context obtained from speech inputs and changes of external environments obtained from sensory inputs. When the situation matches more than one plan, the dialogue manager mediates these plans, based on some heuristic strategies. That is, when there is no plan being executed, the most urgent action or plan for the situation is selected. In the other cases the dialogue manager mediates matched plans in plans being executed: if no plan matches to the situation, the action or plan is selected in the aformentioned way.
It is assumed that plans has priority values with the range between zero and one. The priority value of the plan being executed is reset to one when the dialogue of the plan is selected. When the plan called from another plan is selected, the priority value of the plan calling the selected plan is updated in some predetermined fashion.
We designed and implemented this speech dialogue system that includes the reactive planning subsystem to realize utterance motivation and more reactiveness.
To accomplish that, we first implemented a voice recognition board and a task planner on the autonomous mobile robot developed in our laboratory. Then, the dialogue planner mentioned above was implemented, which completed our system for speech dialogue with autonomous mobile robots. The overall system integrates goal-oriented dialogue planning and reactive dialogue planning. The former is in a sense similar to those in speech dialogue systems proposed in the conventional work on human-computer interaction. The latter is concerned with external and contextual changes. This integration made our speech dialogue system enable to realize human dialogue with autonomous mobile robots, whose ``mental models'' are reconstructed according to dynamical changes in external environments.

Keywords: dialogue planning, reactive planning, mental model, sensor fusion, autonomous mobile robot