AGENT: A Benchmark for Core Psychological Reasoning

• URL: http://arxiv.org/abs/2102.12321v2
• Date: Thu, 25 Feb 2021 18:11:01 GMT
• Title: AGENT: A Benchmark for Core Psychological Reasoning
• Authors: Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua B. Tenenbaum, Tomer D. Ullman
• Abstract summary: Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
• Score: 60.35621718321559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability to reason about hidden mental variables that drive observable actions, comes naturally to people: even pre-verbal infants can tell agents from objects, expecting agents to act efficiently to achieve goals given constraints. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. Inspired by cognitive development studies on intuitive psychology, we present a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal preferences, action efficiency, unobserved constraints, and cost-reward trade-offs) that probe key concepts of core intuitive psychology. We validate AGENT with human-ratings, propose an evaluation protocol emphasizing generalization, and compare two strong baselines built on Bayesian inverse planning and a Theory of Mind neural network. Our results suggest that to pass the designed tests of core intuitive psychology at human levels, a model must acquire or have built-in representations of how agents plan, combining utility computations and core knowledge of objects and physics.

Related papers

• Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind [0.35154948148425685]
  Theory of Mind (ToM) is the ability to attribute beliefs, intentions, or mental states to others. We show a developmental synergy between learning to predict low-level mental states and attributing high-level ones. We propose that our computational approach can inform the understanding of human social cognitive development.
  arXiv  Detail & Related papers  (2024-07-25T13:15:25Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy [0.0]
  Theory of Mind (TOM) is the ability to attribute to other agents' beliefs, intentions, or mental states in general. We show the synergy between learning to predict low-level mental states, such as intentions and goals, and attributing high-level ones, such as beliefs. We propose that our architectural approach can be relevant for the design of future adaptive social robots.
  arXiv  Detail & Related papers  (2022-10-17T21:12:39Z)
• Modeling Human Behavior Part I -- Learning and Belief Approaches [0.0]
  We focus on techniques which learn a model or policy of behavior through exploration and feedback. Next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams.
  arXiv  Detail & Related papers  (2022-05-13T07:33:49Z)
• Learning Theory of Mind via Dynamic Traits Attribution [59.9781556714202]
  We propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories. This trait vector then multiplicatively modulates the prediction mechanism via a fast weights' scheme in the prediction neural network. We empirically show that the fast weights provide a good inductive bias to model the character traits of agents and hence improves mindreading ability.
  arXiv  Detail & Related papers  (2022-04-17T11:21:18Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Imitating Interactive Intelligence [24.95842455898523]
  We study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. We use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour.
  arXiv  Detail & Related papers  (2020-12-10T13:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.