no code implementations • 18 Dec 2023 • Christian Muise, Sheila A. McIlraith, J. Christopher Beck
Fully Observable Non-Deterministic (FOND) planning is a variant of classical symbolic planning in which actions are nondeterministic, with an action's outcome known only upon execution.
no code implementations • 8 Dec 2023 • Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith
In this paper we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions.
no code implementations • 8 Jan 2023 • Phillip J. K. Christoffersen, Andrew C. Li, Rodrigo Toro Icarte, Sheila A. McIlraith
Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR.
no code implementations • 20 Nov 2022 • Andrew C. Li, Zizhao Chen, Pashootan Vaezipoor, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions.
1 code implementation • 8 Nov 2022 • Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor, Toryn Q. Klassen, Scott Sanner, Sheila A. McIlraith
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 observations conveyed through natural language.
1 code implementation • 3 Jun 2022 • Andrew C. Li, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith
Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi.
no code implementations • 17 Dec 2021 • Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Valenzano, Margarita P. Castro, Sheila A. McIlraith
Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems.
Partially Observable Reinforcement Learning Problem Decomposition +2
no code implementations • 4 Jun 2021 • Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith
We endow RL agents with the ability to contemplate such impact by augmenting their reward based on expectation of future return by others in the environment, providing different criteria for characterizing impact.
1 code implementation • 31 May 2021 • Maayan Shvo, Zhiming Hu, Rodrigo Toro Icarte, Iqbal Mohomed, Allan Jepson, Sheila A. McIlraith
We introduce an RL-based framework for learning to accomplish tasks in mobile apps.
no code implementations • ICLR 2021 • Keiran Paster, Sheila A. McIlraith, Jimmy Ba
Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents.
3 code implementations • 6 Oct 2020 • Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith
First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure.
1 code implementation • 6 Oct 2020 • Maayan Shvo, Andrew C. Li, Rodrigo Toro Icarte, Sheila A. McIlraith
Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance.
no code implementations • 5 Oct 2020 • Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen, Phillip Christoffersen, Amir-Massoud Farahmand, Sheila A. McIlraith
Learning memoryless policies is efficient and optimal in fully observable environments.
Partially Observable Reinforcement Learning reinforcement-learning +1
no code implementations • 6 May 2020 • 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.
no code implementations • 31 Dec 2019 • Alberto Camacho, Sheila A. McIlraith
Synthesizing a program that realizes a logical specification is a classical problem in computer science.
no code implementations • 14 Jun 2019 • Maayan Shvo, Sheila A. McIlraith
Critical to successful human interaction is a capacity for empathy - the ability to understand and share the thoughts and feelings of another.
no code implementations • 31 Aug 2018 • Alberto Camacho, Meghyn Bienvenu, Sheila A. McIlraith
In this paper, we investigate the problem of synthesizing strategies for linear temporal logic (LTL) specifications that are interpreted over finite traces -- a problem that is central to the automated construction of controllers, robot programs, and business processes.
no code implementations • 14 Sep 2016 • Jorge A. Baier, Alberto Camacho, Christian Muise, Sheila A. McIlraith
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications.