Assessing the HI-ness of Virtual Heritage Applications with Knowledge Engineering
Authors: Delaram Javdani Rikhtehgar, Ilaria Tiddi, Shenghui Wang, Stefan Schlobach, Dirk Heylen.
Abstract
Virtual Heritage exhibitions aim to engage a diverse audience through the integration of Virtual Reality and various AI technologies, including Artificial Agents, and Knowledge Graphs. Understanding the nuances of human-agent interactions is crucial to fully harness the potential of these technologies and deliver personalized and captivating experiences. Evaluating the alignment of Virtual Heritage applications with the vision of Hybrid Intelligence – where humans and machines collaborate toward a common goal – presents a significant challenge. In this paper, we investigate the assessment of Hybrid Intelligence
within the Virtual Heritage domain using Knowledge Engineering methods. Through the analysis of six different scenarios presented as workflows of tasks and input/output data, we identify and compare classical Knowledge Engineering tasks with HI-specific tasks to measure the level of HI-ness achieved. Our study focuses on evaluating the synergy achieved by mixed teams during various tasks as a measure of HI-ness. The findings provide insights into the effectiveness of Knowledge Engineering to identify HI aspects within existing applications, the potential for quantifying and improving HI-ness in an application, and the identification of modeling limitations.
About this book
This book enables readers to understand the challenges and opportunities of developing human-centered AI with commonsense reasoning abilities. Despite apparent accuracy improvements brought by large neural models across task benchmarks, common sense is still lacking. The lack of common sense affects many tasks, including story understanding, decision-making, and question answering. Commonsense knowledge and reasoning have long been considered the “black matter” of AI, raising concerns about the trustworthiness and applicability of AI methods in both autonomous and hybrid applications. This book describes how to design a more robust, collaborative,
explainable, and responsible AI through incorporating neuro-symbolic commonsense reasoning. In addition, the book provides examples of how these properties of AI can facilitate a wide range of social-good applications in digital democracy, traffic monitoring, education, and robotics. What makes commonsense reasoning such a unique and impactful challenge? What can we learn from cognitive research when designing and developing AI systems? How can we approach building responsible, robust, collaborative, and explainable AI with common sense? And finally, what is the impact of this work on human-AI teaming? This book provides an accessible introduction and exploration of these topics.