A test measures artificial agents' human-like behavior in video games.
problem Measuring the believability of artificial agents' human-like behavior.
method Developed a non-parametric two-sample hypothesis test.
result The p-value correlates with human judgment of human-like behavior. Study models human investors' sub-rational behavior in financial markets.
problem Lack of a comprehensive model for human sub-rationality in financial markets.
method Flexible reinforcement learning model incorporating five human sub-rational aspects.
result Model accurately reproduces human behavior and reveals insights into market dynamics.
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the…
Model predicts human food choices based on demographics.
problem Predicting human food choices from demographic data.
method Non-deterministic model based on NHANES dataset and behavioral studies.
result Generates synthetic data similar to original dataset.
Paper withdrawn; AI game behavior needs diversity.
problem Creating varied human-like playing styles in games.
method Evolutionary multi-objective deep reinforcement learning.
result Generated diverse AI behaviors for games.
New framework evaluates model explanations based on decision task improvement.
problem Evaluation of model explanations often misses practical value.
method Decision-theoretic framework quantifying three key values.
result Provides benchmarks and interprets human-AI decision support.
Study reveals LLM personality patterns but lacks behavioral consistency.
problem Understanding and validating personality traits in LLMs.
method Characterized LLM personality across three dimensions: training dynamics, self-report validity, and intervention effects.
result Self-reported traits do not reliably predict behavior, and instructional alignment affects trait expression but not behavior.
To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will need to learn these behaviors within a reasonable amount of time while efficiently leveraging the sparse feedback a human trainer is capable o…
AI assistants often give convincing but incorrect responses to match user beliefs.
problem Sycophancy in AI assistants that use human feedback.
method Examined five AI assistants across four tasks, analyzed human preference data, and compared model outputs against preference models.
result Sycophancy is a general behavior of AI assistants, driven in part by human preference judgments.
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social intera…
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
This work trains a model to predict human driving directions from road scenes.
problem Defining implicit rules of human behavior for autonomous vehicles.
method Self-supervised learning of probabilistic network model.
result Model successfully generalizes to new road scenes.
The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
problem Modeling human-like decision-making in multi-agent systems with risk-seeking and loss-aversion behaviors.
method Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory.
result The proposed algorithms demonstrate both risk-averse and risk-seeking behaviors in multi-agent systems.
Unified model of urban consumer behavior and mobility patterns.
problem Understanding the lifestyles and infrastructure of urban regions.
method Collective matrix factorization for dual view modeling of consumer behavior and mobility.
result Unified model reveals deeper insights into consumer behavior and mobility patterns.
New model accounts for continuous human trajectories in robotics.
problem Inaccurate probabilistic models of human behavior in robotics.
method Developed a new probabilistic model that considers distances between continuous trajectories.
result The new model outperforms existing models in explaining human behavior and improving robot inference.
Bayesian framework infers personalized embeddings for diverse human demonstrations.
problem Lack of personalized models for diverse human behaviors.
method Bayesian LfD framework inferring human-specific embeddings.
result Outperforms state-of-the-art techniques on synthetic and real-world data.
New cognitive model priors improve human decision prediction.
problem Predicting human decisions with high precision remains challenging.
method Pretrained neural networks on synthetic data generated by cognitive models, fine-tuning on real human data.
result Fine-tuned neural networks achieve unprecedented state-of-the-art improvements on benchmark datasets.
TRIBE model uses LLMs to simulate human trading behavior in bond markets.
problem Complexities in decentralized bond market transactions.
method Agent-based model augmented with LLMs to simulate human-like decision-making.
result Slight trade aversion in LLMs can lead to complete market collapse.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
New method infers human sensorimotor costs from behavior.
problem Inferring human sensorimotor costs from observed behavior.
method Inverse optimal control with signal-dependent noise.
result Recovering costs and benefits in sensorimotor behavior.
In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go, and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, w…
We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…
Proposes a generic prediction architecture for autonomous vehicles considering both rational and irrational driving behaviors.
problem Accurately predicting future behaviors of surrounding vehicles for safe autonomous vehicle planning.
method Combines learning-based and planning-based models to address rationalities in human behavior.
result Stable prediction performance under various unseen driving scenarios.
AI beats 95% of humans in Rock-Paper-Scissors.
problem Predicting and modeling human behavior in strategic games.
method Used Markov Models of varying memory lengths to compete against humans, introducing a 'focus length' parameter.
result Multi-AI strategy wins over 95% of human opponents in continuous 300-round games.
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
VICE embeds concepts in a vector space using human data.
problem Developing numerical models for mental representations of object concepts.
method Variational Interpretable Concept Embeddings (VICE) using variational inference and triplet odd-one-out task data.
result VICE outperforms SPoSE at predicting human behavior and provides more reproducible object representations.
FinHEAR combines LLMs with human expertise for better financial decision-making.
problem Challenges in financial decision-making for language models.
method Multi-agent framework with specialized LLMs for historical analysis, event interpretation, and expert retrieval.
result FinHEAR outperforms baselines in financial tasks with higher accuracy and risk-adjusted returns.
Paper uses social norms to teach robots human behavior.
problem Teaching robots to behave like humans in society.
method Captured social norms guide reinforcement learning towards normative behavior.
result Robots can learn normative behavior through automatic norm reward system.
Model creates human-like text descriptions for time series data.
problem Creating textual summaries for complex time series data that mimic human behavior.
method Utility estimation model based on Bayesian network to rank patterns in time series data.
result Output is a natural language description of time series that matches human summary.
Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is impl…
HHAR-net uses neural networks to recognize human activities at different levels of abstraction.
problem Recognizing different layers of human activities concealed in behavior.
method Hierarchical classification with Neural Networks.
result 95.8% accuracy for low-level activities and 92.8% overall accuracy.
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effective…
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…
Study evaluates five LLMs for financial report analysis, revealing performance differences and variability.
problem Lack of understanding in reliability, consistency, and transparency of LLMs in financial analysis.
method Human evaluation, automated similarity metrics, and behavioral diagnostics applied to five transformer-based LLMs over U.S. 10-K filings.
result No single LLM consistently dominates across all evaluation perspectives, highlighting variability and need for interpretability.
RFC enhances humanoid control to imitate complex human motions.
problem Dynamics mismatch between humanoid models and real humans.
method Residual Force Control (RFC) augments control policies with external forces.
result RFC outperforms state-of-the-art methods in convergence speed and motion quality.
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model…
IRL models human risk decisions based on past outcomes.
problem Understanding human risk decisions under risk.
method Inverse Reinforcement Learning (IRL) with features reflecting state history.
result Human reward function explains risk-prone and risk-averse decisions.
Poultry farms are an important contributor to the human food chain. Worldwide, humankind keeps an enormous number of domesticated birds (e.g. chickens) for their eggs and their meat, providing rich sources of low-fat protein. However, around the world, there have been growing concerns about the quality of life for the …
Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this infer…
DreamerV2 learns Atari game behaviors from a world model, achieving human-level performance.
problem Learning complex behaviors in Atari games from limited data.
method DreamerV2 uses a world model with discrete representations to predict behaviors in a compact latent space.
result Achieves human-level performance on 55 Atari tasks.
LLMs mimic human traders in finance, but not as much as expected.
problem Evaluating how LLMs behave in financial markets.
method Adapted experimental design with LLMs and human traders, analyzed in single and mixed model settings.
result LLMs tend to price assets near their fundamental value, but not as much as humans, and show less trading strategy variance.
A two-stream reinforcement learning model improves decision-making across human and neuropsychiatric studies.
problem Improving reinforcement learning models to better simulate human decision-making and neuropsychiatric conditions.
method Proposes a two-stream reinforcement learning model that processes positive and negative rewards and incorporates reward-processing biases.
result The two-stream model outperforms standard Q-learning and SARSA methods on various tasks and datasets.
Framework analyzes RL agents' behavior to explain their strengths and weaknesses.
problem Understanding and explaining RL agents' capabilities and limitations.
method Data analysis and visualization of interaction history to extract interestingness elements.
result Visual summaries help humans correctly perceive agents' strengths and weaknesses.
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
New game design method for better trait inference.
problem Inferring latent psychological traits from human behavior.
method Formulated as a mutual information maximization problem, solved using variational lower bound optimization.
result Designed games successfully distinguish among players with different traits, outperforming traditional methods.
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual, and…
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervi…