Referring Locative Expressions in a Bounded-Optimal Localization Agent

Abstract

Hitherto, in Artificial Intelligence the relations between visual and verbal space have mostly been examined assuming unlimited computational and natural resources and complete perceptual information. But considering dynamic environments like dialog situations, we find that response times are usually limited. Therefore, we should be able to generate some answer even before a perhaps complex computation has finished with the best possible result. These responses necessarily are suboptimal but by applying intelligent algorithms and architectures their quality will increase with the amount of time, the main resource in this case. Here we present some aspects of a boundedoptimal localization agent describing spatial configurations with referring locative expressions. Introduction In the last few years, the idea of anytime computation became more and more popular. This development was caused by the fact that we cannot assume that all information to an AI system is always complete and corr..