Výsledky vyhledávání - "Hussain, Mustafa"

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Neuro-Symbolic World Models for Adapting to Open World Novelty

Autor: Balloch, Jonathan, Lin, Zhiyu, Wright, Robert, Peng, Xiangyu, Hussain, Mustafa, Srinivas, Aarun, Kim, Julia, Riedl, Mark O.

Open-world novelty--a sudden change in the mechanics or properties of an environment--is a common occurrence in the real world. Novelty adaptation is an agent's ability to improve its policy performance post-novelty. Most reinforcement learning (RL)

Externí odkaz: http://arxiv.org/abs/2301.06294

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Akademický článek

Patient-Reported Outcomes and Complications of Simultaneous versus Staged Surgical Decompression for Tandem Spinal Stenosis.

Autor: Hussain, Mustafa Mushtaq¹ (AUTHOR), Khalil, Mujtaba² (AUTHOR), Suleman, Fatima¹ (AUTHOR), Ali, Iqra Fatima Munawar³ (AUTHOR), Masroor, Mehar¹ (AUTHOR), Shamim, Muhammad Shahzad¹ (AUTHOR) shahzad.shamim@aku.edu

Publikováno v: Asian Journal of Neurosurgery. Sep2024, Vol. 19 Issue 3, p419-425. 7p.

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Akademický článek

Farmer’s environmental orientation as an antecedent to the intention for adopting conservational agriculture practices: the moderation analysis

Autor: Gillani, Syed Hussain Mustafa, Kiani, Malkah Noor, Abid, Saifullah

Publikováno v: International Journal of Climate Change Strategies and Management, 2023, Vol. 16, Issue 2, pp. xx-xxxvi.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/IJCCSM-09-2021-0106

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Akademický článek

How a chairperson's international experience can affect the post-entry speed of international SMEs from emerging economies: the roles of functional variety and power

Autor: Qin, Linghua, Akhtar, Naveed, Farooq, Qamar, Gillani, Syed Hussain Mustafa

Publikováno v: International Journal of Emerging Markets, 2022, Vol. 19, Issue 2, pp. 540-560.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/IJOEM-12-2021-1913

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NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty

Autor: Balloch, Jonathan, Lin, Zhiyu, Hussain, Mustafa, Srinivas, Aarun, Wright, Robert, Peng, Xiangyu, Kim, Julia, Riedl, Mark

A robust body of reinforcement learning techniques have been developed to solve complex sequential decision making problems. However, these methods assume that train and evaluation tasks come from similarly or identically distributed environments. Th

Externí odkaz: http://arxiv.org/abs/2203.12117

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