{"url":"/dataset/agent","name":"AGENT","full_name":null,"description_markdown":"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.","description_withheld":null,"homepage":"https://www.tshu.io/AGENT/","introduced_date":"2021-02-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/agent-a-benchmark-for-core-psychological","title":"AGENT: A Benchmark for Core Psychological Reasoning","first_author":"Tianmin Shu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Core Psychological Reasoning","url":"/task/core-psychological-reasoning","datasets_with_task":"/datasets/task/core-psychological-reasoning"}],"languages":[],"variants":["AGENT"],"data_loaders":[],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}