188 citations · 199 across the 16 of their papers we have counts for
19 papers
Progression- vs Automata-based Anticipatory Monitoring of LTL over Finite Traces (Extended Version)
Sarah Winkler, Toryn Klassen, Sheila McIlraith +1
When safety-critical systems are developed from a known internal specification, their correctness can be established by model checking. In the frequent case where such a specificat…
Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1
We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effecti…
Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
Parand A. Alamdari, Toryn Q. Klassen, Sheila A. McIlraith
We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
Satisficing and Optimal Generalised Planning via Goal Regression (Extended Version)
Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1
Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set…
Pushdown Reward Machines for Reinforcement Learning
Giovanni Varricchione, Toryn Q. Klassen, Natasha Alechina +3
Reward machines (RMs) are automata structures that encode (non-Markovian) reward functions for reinforcement learning (RL). RMs can reward any behaviour representable in regular la…
Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
Andrew C. Li, Toryn Q. Klassen, Andrew Wang +2
Grounding language in perception and action is a key challenge when building situated agents that can interact with humans, or other agents, via language. In the past, addressing t…
Pluralistic Alignment Over Time
Toryn Q. Klassen, Parand A. Alamdari, Sheila A. McIlraith
If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position…
Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI
Parand A. Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Pluralistic alignment is concerned with ensuring that an AI system's objectives and behaviors are in harmony with the diversity of human values and perspectives. In this paper we s…
Reward Machines for Deep RL in Noisy and Uncertain Environments
Andrew C. Li, Zizhao Chen, Toryn Q. Klassen +3
Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the unde…
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager +1
Fair decision making has largely been studied with respect to a single decision. Here we investigate the notion of fairness in the context of sequential decision making where multi…
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Andrew C. Li, Zizhao Chen, Pashootan Vaezipoor +3
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward func…
Learning to Follow Instructions in Text-Based Games
Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor +3
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observatio…
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen +3
Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting wit…
Be Considerate: Objectives, Side Effects, and Deciding How to Act
Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize…
The act of remembering: a study in partially observable reinforcement learning
Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen +3
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is ef…
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano +1
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover reward…
Towards the Role of Theory of Mind in Explanation
Maayan Shvo, Toryn Q. Klassen, Sheila A. McIlraith
Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work - from the social scien…
Independence of Tabulation-Based Hash Classes
Toryn Qwyllyn Klassen, Philipp Woelfel
A tabulation-based hash function maps a key into d derived characters indexing random values in tables that are then combined with bitwise xor operations to give the hash. Thorup a…