Stochastic Goal Recognition Design

AAAI 2019 pp. 9904-9905

doi:10.1609/AAAI.V33I01.33019904 /aaai/2019/wayllace2019aaai-stochastic/

Abstract

Given an environment and a set of allowed modifications, the task of goal recognition design (GRD) is to select a valid set of modifications that minimizes the maximal number of steps an agent can take before its goal is revealed to an observer. This document presents an extension of GRD to the stochastic domain: the Stochastic Goal Recognition Design (S-GRD). The GRD framework aims to consider: (1) Stochastic agent action outcomes; (2) Partial observability of agent states and actions; and (3) Suboptimal agents. In this abstract we present the progress made towards the final objective as well as a timeline of projected conclusion.

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Cite

Text

Wayllace. "Stochastic Goal Recognition Design." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019904

Markdown

[Wayllace. "Stochastic Goal Recognition Design." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/wayllace2019aaai-stochastic/) doi:10.1609/AAAI.V33I01.33019904

BibTeX

@inproceedings{wayllace2019aaai-stochastic,
  title     = {{Stochastic Goal Recognition Design}},
  author    = {Wayllace, Christabel},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {9904-9905},
  doi       = {10.1609/AAAI.V33I01.33019904},
  url       = {https://mlanthology.org/aaai/2019/wayllace2019aaai-stochastic/}
}