Automatic question generation for propositional logical equivalences

The increase in academic dishonesty cases among college students has raised concern, particularly due to the shift towards online learning caused by the pandemic. We aim to develop and implement a method capable of generating tailored questions for each student. The use of Automatic Question Generation (AQG) is a possible solution. Previous studies have investigated AQG frameworks in education, which include validity, user-defined difficulty, and personalized problem generation. Our new AQG approach produces logical equivalence problems for Discrete Mathematics, which is a core course for year-one computer science students. This approach utilizes a syntactic grammar and a semantic attribute system through top-down parsing and syntax tree transformations. Our experiments show that the difficulty level of questions generated by our AQG approach is similar to the questions presented to students in the textbook [1]. These results confirm the practicality of our AQG approach for automated question generation in education, with the potential to significantly enhance learning experiences.

Temporal credit assignment for one-shot learning utilizing a phase transition material

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient artificial intelligence and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing phase transitions. Here we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically-relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to 4 fold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials, and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning.