966 resultados para stakholder dialogue
Resumo:
In this article I will argue that acts of improvisation are not productively understood in opposition to other practices which form our wider musical culture. Improvisation might be better understood as both rooted in, but not limited by, personal and cultural memory. Improvisational activities are legible to the performer and audience through a shared understanding of social norms, but only become a singular instance of improvisation through unique performative actions. This tension between experience and invention is played out in a dialogue between performer and listener, demanding a response that crucially takes the form of self-articulation, or autobiography. Finally, I contend that it is from this position that improvisation offers the possibility to transgress established personal and cultural identities.
Resumo:
In a human-computer dialogue system, the dialogue strategy can range from very restrictive to highly flexible. Each specific dialogue style has its pros and cons and a dialogue system needs to select the most appropriate style for a given user. During the course of interaction, the dialogue style can change based on a user’s response and the system observation of the user. This allows a dialogue system to understand a user better and provide a more suitable way of communication. Since measures of the quality of the user’s interaction with the system can be incomplete and uncertain, frameworks for reasoning with uncertain and incomplete information can help the system make better decisions when it chooses a dialogue strategy. In this paper, we investigate how to select a dialogue strategy based on aggregating the factors detected during the interaction with the user. For this purpose, we use probabilistic logic programming (PLP) to model probabilistic knowledge about how these factors will affect the degree of freedom of a dialogue. When a dialogue system needs to know which strategy is more suitable, an appropriate query can be executed against the PLP and a probabilistic solution with a degree of satisfaction is returned. The degree of satisfaction reveals how much the system can trust the probability attached to the solution.