Search Results for author:
Found 17 papers, 6 papers with code
Toward Human-AI Alignment in Large-Scale Multi-Player Games
First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space.
Memory, Space, and Planning: Multiscale Predictive Representations
Memory is inherently entangled with prediction and planning.
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models
In this work, we investigate how LLM embeddings and attention representations change following in-context-learning, and how these changes mediate improvement in behavior.
A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models
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).
ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike.
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
This is challenging for deep-learning-based world models due to catastrophic forgetting.
Model-based Reinforcement Learning
reinforcement-learning
+1
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game.
Imitating Human Behaviour with Diffusion Models
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
A Rubric for Human-like Agents and NeuroAI
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.
Eigen Memory Trees
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios.
Interaction-Grounded Learning with Action-inclusive Feedback
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.
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS
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)
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
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).
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
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
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
Interaction-Grounded Learning
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.
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
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.
