Observation Interference in Partially Observable Assistance Games
Scott Emmons, Caspar Oesterheld, Vincent Conitzer, Stuart Russell
2025Year
1Top-tier citations
Abstract
We study partially observable assistance games (POAGs), a model of the human-AI value alignment problem which allows the human and the AI assistant to have partial observations. Motivated
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 9963524c-897d-4946-a3f7-65091035d668Cited by top-tier papers1
Ask how each one uses itBuilds on6
- "Other-Play" for Zero-Shot CoordinationHengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. FoersterICML 2020 · 271 citations
- Honesty Is the Best Policy: Defining and Mitigating AI DeceptionFrancis Ward, Francesca Toni, Francesco Belardinelli, Tom EverittNeurIPS 2023 · 60 citations
- The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human ModelsCassidy Laidlaw, Anca D. DraganICLR 2022 · 46 citations
- A New Formalism, Method and Open Issues for Zero-Shot CoordinationJohannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob N. FoersterICML 2021 · 45 citations
- Learning to Interactively Learn and AssistMark Woodward, Chelsea Finn, Karol HausmanAAAI 2020 · 37 citations
Related papers
- A decision-theoretic representation of assistive interfacesJulien Gori, Aurélien Nioche, Christoph Albert Johns, Antti OulasvirtaCHI 2026 · 1 citation
- AssistanceZero: Scalably Solving Assistance GamesCassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng et al.ICML 2025
- Belief-Driven Value Alignment for Human-Robot CollaborationSaisai Li, Bing Shi, Yiming Xia, Xiao SuAAAI 2026
- Value Alignment VerificationDaniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott NiekumICML 2021 · 41 citations
- PAMDP: Interact to Persona Alignment via a Partially Observable Markov Decision ProcessZhe Yang, Yi Huang, Si Chen, Xiaoting Wu et al.ICLR 2026
