KyuHwon Park
ABSTRACT
We aimed at implementing an indoor dialog agent, namely PhoenixBot, working in a mixed-reality environment. The agent occupies a certain position in a real-world space, and interacts with other nearby human.We developed a server that maintains information of agents and smartphone users, where the information includes current indoor position and direction. We also developed an Android application as a client, which collects real-time data from various sensors such as gyroscope, accelerometer, step detector, WiFi, magnetic field, and gravity sensor. The step detector values and WiFi signals are used to estimate the current location of the user, and the other remaining sensors are used to compute the user direction. The client application displays the real-world scene covered with some virtual objects (e.g., agent, board), as depicted in Fig. 1, where the cartoon character at the center is the dialog agent. In order to compute the right position to display the components, the client keeps consistent information of the virtual objects with the server. That is, if a user moves to other position, then it will be reported to the server and will be disclosed to the other agents and users. The dialog agent works in the way similar to other dialog agents, as it has a pipeline of modules such as Natural Language Understanding (NLU), Dialog Management (DM), and Natural Language Generation (NLG). The agent currently supports four domains: weather, campus, transport, and bible. The agent speaks only Korean for now, but will be portable to other langauges if a dataset for the target language is prepared. You can see the video clip at https://youtu.be/U2FA-XxVPvM
- Gennady Berkovich. 2014. Accurate and Reliable Real-Time Indoor Positioning on Commercial Smartphones. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation . 670--677.Google Scholar
Cross Ref
- Negar Ghourchian, Michel Allegue-Martinez, and Doina Precup. 2017. Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning. In Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications . 4670--4677. Google ScholarDigital Library
- Vladimir Ilievski, Claudiu Musat, Andreea Hossmann, and Michael Baeriswyl. 2018. Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 4115--4121. Google ScholarDigital Library
- Young-Seob Jeong and Hanna Kim. 2018. Dialog System with Postponed Response. In Proceedings of the 13th Asia Pacific International Conference on Information Science and Technology . 185--190.Google Scholar
- Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing Dialogue Agents: I have a dog, do you have pets too?. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . 2204--2213.Google ScholarCross Ref
Index Terms
- Indoor Dialog Agent in Mixed Reality (video)
Recommendations
Single or Multiple Conversational Agents?: An Interactional Coherence Comparison
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing SystemsChatbots focusing on a narrow domain of expertise are in great rise. As several tasks require multiple expertise, a designer may integrate multiple chatbots in the background or include them as interlocutors in a conversation. We investigated both ...
Multimodal augmented reality: the norm rather than the exception
MVAR '16: Proceedings of the 2016 workshop on Multimodal Virtual and Augmented RealityAugmented reality (AR) is commonly seen as a technology that overlays virtual imagery onto a participant's view of the world. In line with this, most AR research is focused on what we see. In this paper, we challenge this focus on vision and make a case ...
Adaptive Mixed-Initiative Dialog Motivates a Game Player to Talk with an NPC
HAI '18: Proceedings of the 6th International Conference on Human-Agent InteractionIn most role-playing games, a non-player character (NPC) tends to have the most initiative, i.e., developing conversation topics, on the dialog. Thus, the player tends to experience a unilateral impression, i.e., the feeling of that an NPC forces ...
Comments