Semantic Fusion for Natural Multimodal Interfaces using Concurrent Augmented Transition Networks

Abstract

Semantic fusion is a central requirement of many multimodal interfaces. Procedural methods like finite-state transducers and augmented transition networks have proven to be beneficial to implement semantic fusion. They are compliant with rapid development cycles that are common for the development of user interfaces, in contrast to machine-learning approaches that require time-costly training and optimization. We identify seven fundamental requirements for the implementation of semantic fusion: Action derivation, continuous feedback, context-sensitivity, temporal relation support, access to the interaction context, as well as the support of chronologically unsorted and probabilistic input. A subsequent analysis reveals, however, that there is currently no solution for fulfilling the latter two requirements. As the main contribution of this article, we thus present the Concurrent Cursor concept to compensate these shortcomings. In addition, we showcase a reference implementation, the Concurrent Augmented Transition Network (cATN), that validates the concept’s feasibility in a series of proof of concept demonstrations as well as through a comparative benchmark. The cATN fulfills all identified requirements and fills the lack amongst previous solutions. It supports the rapid prototyping of multimodal interfaces by means of five concrete traits: Its declarative nature, the recursiveness of the underlying transition network, the network abstraction constructs of its description language, the utilized semantic queries, and an abstraction layer for lexical information. Our reference implementation was and is used in various student projects, theses, as well as master-level courses. It is openly available and showcases that non-experts can effectively implement multimodal interfaces, even for non-trivial applications in mixed and virtual reality.

eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research

“…Figure 2: Examples of XR-AI integrations.From upper left to lower right: A user is interacting with an intelligent virtual agent to solve a construction task(Latoschik, 2005) and interaction with an agent actor in Madam Bovary, an interactive intelligent story telling piece(Cavazza et al, 2007), both in a CAVE(Cruz-Neira et al, 1992); Virtual agents in an Augmented Reality (AR)(Obaid et al, 2012) and in a Mixed Reality (MR)(Kistler et al, 2012); Speech and gesture interaction in a virtual construction scenario in front of a power wall(Latoschik and Wachsmuth, 1998) and in a CAVE(Latoschik, 2005). Multimodal interactions in game-like scenarios full-immersed using a Head-Mounted Display (HMD)(Zimmerer et al, 2018b) and placed at an MR tabletop(Zimmerer et al, 2018a).…”