Algorithmic and Human Teaching of Sequential Decision Tasks

Maya Cakmak 1 Manuel Lopes 2
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : A helpful teacher can significantly improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teaching a new concept. However the field has so far focused purely on classification tasks. In this paper we introduce a novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations and evaluate it on several navigation tasks. Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.
Document type :
Conference papers
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.inria.fr/hal-00755253
Contributor : Manuel Lopes <>
Submitted on : Tuesday, November 20, 2012 - 5:39:25 PM
Last modification on : Wednesday, July 31, 2019 - 3:24:15 PM
Long-term archiving on : Thursday, February 21, 2013 - 12:30:57 PM

File

aaai_teaching_final.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00755253, version 1

Citation

Maya Cakmak, Manuel Lopes. Algorithmic and Human Teaching of Sequential Decision Tasks. AAAI Conference on Artificial Intelligence (AAAI-12), Jul 2012, Toronto, Canada. ⟨hal-00755253⟩

Share

Metrics

Record views

480

Files downloads

697