Cognition · Data artifact

Action Effect Model

Data artifactCognitionCognition & Memoryarc:ActionEffectModel

A planning-domain model describing how each action transforms state: its preconditions, expected effects, durations and costs, used to predict outcomes.

Responsibility. Specifies the executable semantics of primitive tasks for plan validity reasoning.

Also known as: Planning operators, Operator library, STRIPS operators, Action schema, Primitive task definitions, Transition model, Internal physics model, Planning domain model

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

is read by dependency

is written by dependency

Action Model Learner Ch5.6

Design guidance

MUST model the agent's real capabilities accurately; misclassifying abstract tasks as primitive causes execution failures.
SHOULD NOT assume operators always succeed when preconditions hold in networked or physical systems.
SHOULD be updated when monitoring reveals systematic (patterned) rather than random discrepancies, e.g., adding surface friction as a grasp precondition or acceleration costs to travel time.

Quantitative guidance

As stated by the sources; verify before use.

Constant-speed travel model predicted 240 s for 800 m vs ~280 s actual, a 40 s systematic error (Ch5.6).

Classification

Patterns: STRIPS operator representationPDDLNumeric fluentsConditional effectsProbabilistic effects
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Plans assuming actions the agent cannot performInvalid action sequencesSystematic prediction errors from over-simplified models

Sources

Ch5.4: T. Nguyen, "Hierarchical Planning Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.4. ISBN: 9798244538229.
Ch5.6: T. Nguyen, "A* Search and Replaning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.6. ISBN: 9798244538229.