Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks

1 code implementation • 27 May 2023 • Alperen Tercan, Ahana Ghosh, Hasan Ferit Eniser, Maria Christakis, Adish Singla

We propose a novel synthesis algorithm that generates a progression of subtasks that are high-quality, well-spaced in terms of their complexity, and solving this progression leads to solving the reference task.

Logical Reasoning

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

1 code implementation • 28 Mar 2023 • Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla

We introduce a scaffolding framework based on pop quizzes presented as multi-choice programming tasks.

Synthesizing Tasks for Block-based Programming

1 code implementation • NeurIPS 2020 • Umair Z. Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish Singla

Our task synthesis algorithm operates by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$.

Towards Deployment of Robust AI Agents for Human-Machine Partnerships

no code implementations • 5 Oct 2019 • Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla

In the test phase, the AI agent has to interact with a user of unknown type.

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

no code implementations • NeurIPS 2019 • Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla

We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.

reinforcement-learning Reinforcement Learning (RL)