Since the inception of AI research, great strides have been made towards achieving the goal of extending natural language conversation as a medium of interaction with machines. Today, we find many Conversational Agents (CAs) situated in various aspects of our everyday life such as information access, education and entertainment. However, most of the existing work on CAs has focused on agents that support only one user in each interactive session.
On the other hand, people organize themselves in groups such as teams of coworkers, family and networks of friends. With the mass-adoption of Internet based communication technologies for group interaction, there is an unprecedented opportunity for CAs to support interactive situations involving multiple human participants. Support provided by these CAs can make the functioning of some of these groups more efficient, enjoyable and rewarding to the participants.
Through our work on supporting various Multi-Party Interactive Situations (MPIS), we have identified two problems that must be addressed in order to embed effective CAs in such situations. The first problem highlights the technical challenges involving the development of CAs in MPIS. Existing approaches for modeling agent behavior make assumptions that break down in multi-party interaction. As a step towards addressing this problem, this thesis contributes the Basilica software architecture that uses an event-driven approach to model conversation as an orchestration of triggering of conversational behaviors. This architecture alleviates the technical problems by providing a rich representational capability and the flexibility to address complex interaction dynamics.
The second problem involves the choice of appropriate agent behaviors. In MPIS, agents must compete with human participants for attention in order to effectively deliver support and interventions. In this work, we follow a model of human group interaction developed by empirical research in small group communication. This model identifies two fundamental processes in human group interaction, i.e., Instrumental (Task-related) and Expressive (Social-Emotional). Behaviors that constitute this expressive process hold the key to managing and regulating user attention and serve other social functions in group interaction.
This thesis describes two socially capable conversational agents that support users in collaborative learning and group decision making activities. Their social capabilities are composed of a set of behaviors based on the Social-Emotional interaction categories identified by work in small group communication. These agents demonstrate the generalizability of our methodology for designing and implementing social capabilities across two very different interactive situations.
In addition to the implementation of these agents, the thesis presents a series of experiments and analysis conducted to investigate the effectiveness of these social capabilities. First and foremost, these experiments show significant benefits of the use of socially capable agents on task success and agent perception across the two different interactive situations listed above. Second, they investigate issues related to the appropriate use of these social capabilities specifically in terms of the amount and timing of the constituent social behaviors. Finally, these experiments provide an understanding of the underlying mechanism that explains the effects that social capabilities can achieve.
|Advisor:||Rose, Carolyn P.|
|School:||Carnegie Mellon University|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-B 74/01(E), Dissertation Abstracts International|
|Subjects:||Communication, Artificial intelligence, Computer science|
|Keywords:||Collaborative learning, Conversational agents, Human-computer interaction|
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