In Vulcan’s Project Halo, we developed means of extracting the structure of logical proofs that answer advanced placement (AP) questions in biology. For example, the following shows a proof that separation of chromatids occurs during prophase.
This explanation was generated using capabilities of SILK built on those described in A SILK Graphical UI for Defeasible Reasoning, with a Biology Causal Process Example. That paper gives more details on how the proof structures of questions answered in Project Sherlock are available for enhancing the suggested questions of Inquire (which is described in this post, which includes further references). SILK justifications are produced using a number of higher-order axioms expressed using Flora‘s higher-order logic syntax, HiLog. These meta rules determine which logical axioms can or do result in a literal. (A literal is an positive or negative atomic formula, such as a fact, which can be true, false, or unknown. Something is unknown if it is not proven as true or false. For more details, you can read about the well-founded semantics, which is supported by XSB. Flora is implemented in XSB.)
Now how does all this relate to pedagogy in future derivatives of electronic learning software or textbooks, such as Inquire?
Well, here’s a use case: Continue reading “Pedagogical applications of proofs of answers to questions”
In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment. This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education. Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration of the electronic textbook discussed below. There is a video of that presentation here. The introductory remarks are interesting but not pertinent to this article.
Background on Vulcan’s Project Halo
From 2002 to 2004, Vulcan developed a Halo Pilot that could correctly answer between 30% and 50% of the questions on advanced placement (AP) tests in chemistry. The approaches relied on sophisticated approaches to formal knowledge representation and expert knowledge engineering. Of three teams, Cycorp fared the worst and SRI fared the best in this competition. SRI’s system performed at the level of scoring a 3 on the AP, which corresponds to earning course credit at many universities. The consensus view at that time was that achieving a score of 4 on the AP was feasible with limited additional effort. However, the cost per page for this level of performance was roughly $10,000, which needed to be reduced significantly before Vulcan’s objective of a Digital Aristotle could be considered viable.
Continue reading “Background for our Semantic Technology 2013 presentation”