Independence of Tabulation-Based Hash Classes. - Toryn Qwyllyn Klassen; Philipp Woelfel (LATIN, 2012)

Towards Tractable Inference for Resource-Bounded Agents. - Toryn Q. Klassen; Sheila A. McIlraith; Hector J. Levesque (AAAI Spring Symposia, 2015)

Towards Representing What Readers of Fiction Believe. - Toryn Q. Klassen; Hector J. Levesque; Sheila A. McIlraith (COMMONSENSE, 2017)

Advice-Based Exploration in Model-Based Reinforcement Learning. - Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Anthony Valenzano; Sheila A. McIlraith (Canadian AI, 2018)

Teaching Multiple Tasks to an RL Agent using LTL. - Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Anthony Valenzano; Sheila A. McIlraith (AAMAS, 2018)

Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning. - Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Anthony Valenzano; Sheila A. McIlraith (ICML, 2018)

Specifying Plausibility Levels for Iterated Belief Change in the Situation Calculus. - Toryn Q. Klassen; Sheila A. McIlraith; Hector J. Levesque (KR, 2018)

LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning. - Alberto Camacho; Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Anthony Valenzano; Sheila A. McIlraith (IJCAI, 2019)

Learning Reward Machines for Partially Observable Reinforcement Learning. - Rodrigo Toro Icarte; Ethan Waldie; Toryn Q. Klassen; Richard Anthony Valenzano; Margarita P. Castro; Sheila A. McIlraith (NeurIPS, 2019)

Towards the Role of Theory of Mind in Explanation. - Maayan Shvo; Toryn Q. Klassen; Sheila A. McIlraith (EXTRAAMAS@AAMAS, 2020)

Epistemic Plan Recognition. - Maayan Shvo; Toryn Q. Klassen; Shirin Sohrabi; Sheila A. McIlraith (AAMAS, 2020)

Changing Beliefs about Domain Dynamics in the Situation Calculus. - Toryn Q. Klassen; Sheila A. McIlraith; Hector J. Levesque (KR, 2020)

The act of remembering: a study in partially observable reinforcement learning. - Rodrigo Toro Icarte; Richard Anthony Valenzano; Toryn Q. Klassen; Phillip J. K. Christoffersen; Amir-massoud Farahmand; Sheila A. McIlraith (CoRR, 2020)

Be Considerate: Objectives, Side Effects, and Deciding How to Act. - Parand Alizadeh Alamdari; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2021)

Representing Plausible Beliefs about States, Actions, and Processes. - Toryn Qwyllyn Klassen (2021)

Planning to Avoid Side Effects. - Toryn Q. Klassen; Sheila A. McIlraith; Christian Muise; Jarvis Xu (AAAI, 2022)

Resolving Misconceptions about the Plans of Agents via Theory of Mind. - Maayan Shvo; Toryn Q. Klassen; Sheila A. McIlraith (ICAPS, 2022)

Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning. - Parand Alizadeh Alamdari; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (AAMAS, 2022)

Learning to Follow Instructions in Text-Based Games. - Mathieu Tuli; Andrew C. Li; Pashootan Vaezipoor; Toryn Q. Klassen; Scott Sanner; Sheila A. McIlraith (NeurIPS, 2022)

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines. - Andrew C. Li; Zizhao Chen; Pashootan Vaezipoor; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2022)

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning. - Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Anthony Valenzano; Sheila A. McIlraith (J. Artif. Intell. Res., 2022)

Epistemic Side Effects: An AI Safety Problem. - Toryn Q. Klassen; Parand Alizadeh Alamdari; Sheila A. McIlraith (AAMAS, 2023)

Learning Belief Representations for Partially Observable Deep RL. - Andrew Wang; Andrew C. Li; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (ICML, 2023)

Planning with Epistemic Preferences. - Toryn Q. Klassen; Christian Muise; Sheila A. McIlraith (KR, 2023)

Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (Preliminary Report). - Parand Alizadeh Alamdari; Toryn Q. Klassen; Elliot Creager; Sheila A. McIlraith (CoRR, 2023)

Learning reward machines: A study in partially observable reinforcement learning. - Rodrigo Toro Icarte; Toryn Q. Klassen; Richard Valenzano; Margarita P. Castro; Ethan Waldie; Sheila A. McIlraith (Artif. Intell., 2023)

Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making. - Parand A. Alamdari; Toryn Q. Klassen; Elliot Creager; Sheila A. McIlraith (ICML, 2024)

Reward Machines for Deep RL in Noisy and Uncertain Environments. - Andrew C. Li; Zizhao Chen; Toryn Q. Klassen; Pashootan Vaezipoor; Rodrigo Toro Icarte; Sheila A. McIlraith (NeurIPS, 2024)

Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI. - Parand A. Alamdari; Toryn Q. Klassen; Rodrigo Toro Icarte; Sheila A. McIlraith (CoRR, 2024)

Pluralistic Alignment Over Time. - Toryn Q. Klassen; Parand A. Alamdari; Sheila A. McIlraith (CoRR, 2024)

Ground-Compose-Reinforce: Tasking Reinforcement Learning Agents through Formal Language. - Andrew C. Li; Toryn Q. Klassen; Andrew Wang; Parand A. Alamdari; Sheila A. McIlraith (CoRR, 2025)