@inproceedings{e779318441ca42adaf14443f1cf06040,
title = "Accounting for observeras partial observability in stochastic goal recognition design",
abstract = "Motivated by security applications, where agent intentions are unknown, actions may have stochastic outcomes, and an observer may have an obfuscated view due to low sensor resolution, we introduce partially-observable states and unobservable actions into a stochastic goal recognition design framework. The proposed model is accompanied by a method for calculating the expected maximal number of steps before the goal of an agent is revealed and a new sensor refinement modification that can be applied to enhance goal recognition. A preliminary empirical evaluation on a range of benchmark applications shows the effectiveness of our approach.",
author = "Christabel Wayllace and Sarah Keren and Avigdor Gal and Erez Karpas and William Yeoh and Shlomo Zilberstein",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.; 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 ; Conference date: 29-08-2020 Through 08-09-2020",
year = "2020",
month = aug,
day = "24",
doi = "10.3233/FAIA200370",
language = "الإنجليزيّة",
series = "Frontiers in Artificial Intelligence and Applications",
pages = "2394--2401",
editor = "{De Giacomo}, Giuseppe and Alejandro Catala and Bistra Dilkina and Michela Milano and Senen Barro and Alberto Bugarin and Jerome Lang",
booktitle = "ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings",
}