no code implementations • 5 Feb 2024 • Sugandha Sharma, Guy Davidson, Khimya Khetarpal, Anssi Kanervisto, Udit Arora, Katja Hofmann, Ida Momennejad
First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space.
no code implementations • 16 Jan 2024 • Ida Momennejad
Memory is inherently entangled with prediction and planning.
no code implementations • 30 Sep 2023 • Safoora Yousefi, Leo Betthauser, Hosein Hasanbeig, Raphaël Millière, Ida Momennejad
In this work, we investigate how LLM embeddings and attention representations change following in-context-learning, and how these changes mediate improvement in behavior.
2 code implementations • 30 Sep 2023 • Taylor Webb, Shanka Subhra Mondal, Chi Wang, Brian Krabach, Ida Momennejad
To address this, we take inspiration from the human brain, in which planning is accomplished via the recurrent interaction of specialized modules in the prefrontal cortex (PFC).
no code implementations • 24 Sep 2023 • Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser, Felipe Vieira Frujeri, Ida Momennejad
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike.
no code implementations • 15 Mar 2023 • Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar
This is challenging for deep-learning-based world models due to catastrophic forgetting.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 2 Mar 2023 • Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game.
1 code implementation • 25 Jan 2023 • Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
no code implementations • 8 Dec 2022 • Ida Momennejad
The author maintains that future progress in artificial intelligence will need strong interactions across the disciplines, with iterative feedback loops and meticulous validity tests, leading to both known and yet-unknown advances that may span decades to come.
1 code implementation • 25 Oct 2022 • Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios.
no code implementations • 16 Jun 2022 • Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies.
no code implementations • 10 Jun 2022 • Eleni Nisioti, Mateo Mahaut, Pierre-Yves Oudeyer, Ida Momennejad, Clément Moulin-Frier
Comparing the level of innovation achieved by different social network structures across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic structure achieves the highest level of innovation in tasks with a deceptive nature and large search spaces.
Cultural Vocal Bursts Intensity Prediction Reinforcement Learning (RL)
1 code implementation • 6 Jun 2022 • Arthur Juliani, Samuel Barnett, Brandon Davis, Margaret Sereno, Ida Momennejad
On the other hand, artificial intelligence researchers often struggle to find benchmarks for neurally and biologically plausible representation and behavior (e. g., in decision making or navigation).
1 code implementation • 25 Apr 2022 • Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen
We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 9 Mar 2022 • Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté, Matthew Hausknecht, Romain Laroche, Ida Momennejad, Harm van Seijen, Benjamin Van Durme
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
no code implementations • 9 Jun 2021 • Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
We propose Interaction-Grounded Learning for this novel setting, in which a learner's goal is to interact with the environment with no grounding or explicit reward to optimize its policies.
1 code implementation • 20 May 2021 • Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann
A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness.