Question: Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?
Evidence: The Alexa Prize funded 12 international teams to compete to create a conversational agent that can discuss any topic for at least 20 minutes. UCSC's Slugbot was one of these funded teams. The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation. SlugBot's conversations over the semi-finals user evaluation averaged 8:17 minutes.
More challenging is that at each system turn, there are a large number of conversational moves that are possible. Making good decisions about what to say next requires balancing a dialogue policy as to what dialogue acts might be good in this context, with real-time information as to what types of content might be possible to use in this context. Slugbot could offer an opinion as in turn S3, ask a follow-on question as in S3, take the initiative to provide unasked for information, as in S5, or decide, e.g. in the case of the user's request for plot information, to use search to retrieve some relevant content. Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary. Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.
The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. 
This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation
More challenging is that at each system turn, there are a large number of conversational moves that are possible.
 Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.
 Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary. 
Answer: The answers are shown as follows:
* do not follow a particular plan or pursue a particular fixed information need
*  integrating content found via search with content from structured data
* at each system turn, there are a large number of conversational moves that are possible
* most other domains do not have such high quality structured data available
* live search may not be able to achieve the required speed and efficiency