Lau, Tessa


Why Programming-By-Demonstration Systems Fail: Lessons Learned for Usable AI

AI Magazine

Programming by demonstration systems have long attempted to make it possible for people to program computers without writing code. However, while these systems have resulted in many publications in AI venues, none of the technologies have yet achieved widespread.adoption. Usability remains a critical barrier to their success. On the basis of lessons learned from three different programming by demonstration systems, we present a set of guidelines to consider when designing usable AI-based systems.


Interpreting Written How-To Instructions

AAAI Conferences

Written instructions are a common way of teaching people how to accomplish tasks on the web. However, studies have shown that written instructions are difficult to follow, even for experienced users. A system that understands human-written instructions could guide users through the process of following the directions, improving completion rates and enhancing the user experience. While general natural language understanding is extremely difficult, we believe that in the limited domain of how-to instructions it should be possible to understand enough to provide guided help in a mixed-initiative environment. Based on a qualitative analysis of instructions gathered for 43 web-based tasks, we have formalized the problem of understanding and interpreting how-to instructions. We compare three different approaches to interpreting instructions: a keyword-based interpreter, a grammar-based interpreter, and an interpreter based on machine learning and information extraction. Our empirical results demonstrate the feasibility of automated how-to instruction understanding.


AAAI 2008 Workshop Reports

AI Magazine

AAAI 2008 Workshop Reports


AAAI 2008 Workshop Reports

AI Magazine

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13–14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy.