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The field of robotics is coming of age. Robotics and artificial intelligence represent the next cutting edge technology to transform the fields of architecture and design. The past decade's surge towards more computationally defined building systems and highly adaptable open-source design software has left the field ripe for the integration of robotics wither through large-scale building fabrication or through more intelligent/adaptive building systems. Through this surge, architecture has not only been greatly influenced by these emerging technologies, but has also begun influencing other disciplines in unexpected ways. The purpose of this book is to provide systems of classification, categorization and taxonomies of robotics in architecture so that a more systematic and holistic body of work could take place while addressing the multifarious aspects of possible research and production.

The ultimate goal of human natural language interaction is to communicate intentions. However, these intentions are often not directly derivable from the semantics of an utterance (e.g., when linguistic modulations are employed to convey polite-ness, respect, and social standing). Robotic architectures withsimple command-based natural language capabilities are thus not equipped to handle more liberal, yet natural uses of linguistic communicative exchanges. In this paper, we propose novel mechanisms for inferring in-tentions from utterances and generating clarification requests that will allow robots to cope with a much wider range of task-based natural language interactions. We demonstrate the potential of these inference algorithms for natural human-robot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialogue-based instruction task.

Shared mental models have been shown to improve human team performance. We thus conjecture that shared mental models (SMMs) integrated into cognitive robotic architectures might also improve the performance of mixed human-robot teams. To date, very little research has focused on developing appropriate computational constructs that can support domain independence and generalizability, while also being scalable. In this paper, we outline our proposed development of SMMs for cognitive robots.