--- pretty_name: procedural-typed-decisions language: - en license: apache-2.0 task_categories: - text-classification tags: - tasksource - jev - system-one - procedural - synthetic - multi-question configs: - config_name: arithmetic data_files: - split: train path: arithmetic/train-*.parquet - split: validation path: arithmetic/validation-*.parquet - split: test path: arithmetic/test-*.parquet - config_name: entity_belief_tracking data_files: - split: train path: entity_belief_tracking/train-*.parquet - split: validation path: entity_belief_tracking/validation-*.parquet - split: test path: entity_belief_tracking/test-*.parquet - config_name: event_state_reconstruction data_files: - split: train path: event_state_reconstruction/train-*.parquet - split: validation path: event_state_reconstruction/validation-*.parquet - split: test path: event_state_reconstruction/test-*.parquet - config_name: evidence_sufficiency data_files: - split: train path: evidence_sufficiency/train-*.parquet - split: validation path: evidence_sufficiency/validation-*.parquet - split: test path: evidence_sufficiency/test-*.parquet - config_name: multi_view_adjudication data_files: - split: train path: multi_view_adjudication/train-*.parquet - split: validation path: multi_view_adjudication/validation-*.parquet - split: test path: multi_view_adjudication/test-*.parquet - config_name: needle_retrieval data_files: - split: train path: needle_retrieval/train-*.parquet - split: validation path: needle_retrieval/validation-*.parquet - split: test path: needle_retrieval/test-*.parquet - config_name: partial_observation_calibration data_files: - split: train path: partial_observation_calibration/train-*.parquet - split: validation path: partial_observation_calibration/validation-*.parquet - split: test path: partial_observation_calibration/test-*.parquet - config_name: policy_applicability data_files: - split: train path: policy_applicability/train-*.parquet - split: validation path: policy_applicability/validation-*.parquet - split: test path: policy_applicability/test-*.parquet - config_name: policy_under_uncertainty data_files: - split: train path: policy_under_uncertainty/train-*.parquet - split: validation path: policy_under_uncertainty/validation-*.parquet - split: test path: policy_under_uncertainty/test-*.parquet - config_name: record_aggregation data_files: - split: train path: record_aggregation/train-*.parquet - split: validation path: record_aggregation/validation-*.parquet - split: test path: record_aggregation/test-*.parquet - config_name: state_perturbation data_files: - split: train path: state_perturbation/train-*.parquet - split: validation path: state_perturbation/validation-*.parquet - split: test path: state_perturbation/test-*.parquet - config_name: table_lookup data_files: - split: train path: table_lookup/train-*.parquet - split: validation path: table_lookup/validation-*.parquet - split: test path: table_lookup/test-*.parquet - config_name: taxonomy_routing data_files: - split: train path: taxonomy_routing/train-*.parquet - split: validation path: taxonomy_routing/validation-*.parquet - split: test path: taxonomy_routing/test-*.parquet --- # procedural-typed-decisions Procedurally generated decision problems. Each row is one structured state (JSON, or a table, CSV, key=value lines, or prose for the arithmetic, retrieval, and aggregation configs) with **several typed questions over that same state**, following the Jev / System One request shape: `choice` (pick one criterion), `noul` (a number in [0, 1]; a probability or a yes/no), and `score` (an ordered rubric). Every answer is computed exactly from the state by rules that the state itself spells out, so the labels are noise-free. Several configs vary the number of options (4 to 60), to balance the binary and 4–6-option questions that dominate the rest of Jev. This is an independent dataset. It is not an official TypeSafe Jev dataset and is not produced by or affiliated with TypeSafe or OpenJev. ## Configs | config | questions | |---|---| | `arithmetic` | An order with a discount/shipping rule, an account ledger, or a schedule; each state asks 2–5 of: `amount_due` / `final_balance` / `finish_time` (choice among the result and typical slips), `within_budget`, `went_negative`, `done_by_deadline` (noul), `random_line_bulk`, `random_is_deposit`, `random_is_long` (noul, exact probability k/n), `budget_use`, `net_change` (score, descriptive levels), `lines_above`, `withdrawal_count`, `starts_before_noon` (score), `largest_line`, `lowest_day`, `longest_task` (choice) | | `entity_belief_tracking` | `world_location` (choice), `agent_belief_location` (choice), `belief_matches_world` (noul); 4 to 16 locations | | `event_state_reconstruction` | `current_owner` (choice), `is_open` (noul), `current_severity` (score) | | `evidence_sufficiency` | `claim_supported` (noul), `has_conflict` (noul), `strongest_support_origin` (choice) | | `multi_view_adjudication` | `intent` (choice), `is_urgent` (noul), `workflow_impact` (score) | | `needle_retrieval` | `value_of_id` (choice), `id_has_value` (noul), `id_listed` (noul); up to ~300 records whose ids differ from the target by one or two digits; 6 to 20 options | | `partial_observation_calibration` | `incident_real` (noul, exact Bayesian posterior) | | `policy_applicability` | `access_allowed` (noul), `governing_policy` (choice), `review_risk` (score) | | `policy_under_uncertainty` | `access_allowed` (noul), `governing_policy` (choice), `requester_role` (choice); exact posteriors over a role known through history counts and reports of stated reliability | | `record_aggregation` | `count_in_category` (score), `largest_quantity` (choice), `any_out_of_stock` (noul), `total_above` (noul) | | `state_perturbation` | `material_change` (noul), `changed_dimension` (choice), `risk_direction` (score) | | `table_lookup` | `find_person` (choice, two-condition filter; 6 to 40 options, capped by the table), `manager_of` (choice, join), `started_before` (noul), `count_matching` (score) | | `taxonomy_routing` | `route` (choice among the 4–60 categories of a routing guide drawn fresh per state; many rules share a condition with the right one), `belongs_to` (noul), `conditions_met` (score, 0–3) | ## Schema | field | meaning | |---|---| | `id` | `task:split:index` | | `level` | Difficulty level (0–4); larger levels add events, records, sensors, or distractors. | | `state` | The state: a JSON string, or rendered text for the retrieval and aggregation configs. | | `questions` | JSON object of named System One questions (`type`, `instructions`, `criteria`). | | `answers` | JSON object of reference answers, in the System One `answers` shape. | | one column per question | Flat label, for browsing and filtering: a `ClassLabel` for choice, score, and yes/no noul questions; a float for graded noul (`incident_real`, `random_*`); the option text for open numeric choices (`amount_due`, `final_balance`, `finish_time`). Null when the state does not ask that question (`arithmetic` only). | States are unique within a split, and validation/test states never occur in train. ## Use As a multi-question Jev request, send `{"state": row["state"], "questions": json.loads(row["questions"])}` (parsing the state first when it is JSON) and compare with `row["answers"]`. The same rows are included, grouped by state, in [`tasksource/tasksource-jev-typed-decisions`](https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions). ## Reproduction Generation is deterministic (row `i` of a split is seeded by `task:split:i`). From a [tasksource](https://github.com/sileod/tasksource) checkout: ```bash PYTHONPATH=.:src python scripts/build_procedural_jev.py --output build/procedural-typed-decisions --upload ``` Generators live in `src/tasksource/jev/procedural/`.