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The RavenClaw/Olympus framework provides a robust platform for research on various dialog management and spoken language interface issues. Some of the current research projects supported by the RavenClaw/Olympus framework are briefly outlined below:

Error handling One of the main goals behind the development of the RavenClaw dialog management framework was providing a solid test-bed for exploring error handling and grounding issues in spoken language interfaces. Currently, Dan Bohus's dissertation focuses on these aspects. Some of the questions under scrutiny are: how does a system “know that it doesn’t know”? How do we develop systems that can monitor and accurately update their beliefs? What set of strategies can be used to set a conversation back on track, and what are the typical user behaviors in response to these strategies? What techniques can be used to learn the optimal system behavior on-line, from detected error segments, and how do we make these systems adapt and improve their performance over time?
More details are available here and on Dan's web-page.
Timing and turn-taking In his dissertation project, Antoine Raux is currently exploring issues of timing and turn-taking. Most spoken language interfaces assume a rigid (you speak - I speak) turn-taking behavior. This assumption can lead to turn-overtaking issues, slow down the dialog, and sometimes lead to complete communication breakdowns. Antoine is currently extending the RavenClaw/Olympus architecture to enable more flexible turn-taking behaviors. More details are available here.
Multi-participant dialog In the TeamTalk project, Thomas Harris investigates some of the challenges related to multi-participant dialog. More details about this project are available here.
Dynamic dialog task construction We have explored issues of dynamic dialog task construction in the context of several spoken dialog systems: LARRI, IPA and Madeleine. In these domains, the dialog task structure is not fixed in advance, like in most information-access systems. Rather, the dialog task is constructed on the fly, based on information selected from a backend. For instance the LARRI system helps aircraft mainenance personnel throughout the execution of maintenance tasks. The structure of the dialog depends on the structure of the maintenance task and is constructed dynamically, based on an XML specification returned from the maintenance task library.
Automatic knowledge extraction Building a spoken language interface requires a number of language resources, such as dictionary, language model, grammar, language generation templates. The development of these resources requires significant amounts of expert knowledge and time. For some domains, this knowledge exists in a different form, not suitable for direct use in a spoken language interface. For instance, for the LARRI system, vast amounts of technical documentation and maintenance procedures are available in text/pdf/paper format. Can we automatically (or semi-automatically) acquire the necessary language resources from these documents? Can we automatically create a spoken language interface from a technical manual?
Taskable agents In the context of the Vera system, we have explored issues related to taskable agents. Vera can not only receive calls but also initiate calls in an effort to locate a person and deliver messages.