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DecisionChainScenario

Robotics scenarios · Implementation: mlsysim/engine/scenario_definitions/robotics/supervision.py.

Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.

links independently kept decision records behind one admitted action, each retrievable with probability p; the whole chain resolves with p^links for each p in link_probabilities.

Notes

Equation: Complete-chain probability is the product of the same per-link retrievability probability across links.

Assumptions: Every required record must resolve, with independent identically likely retrieval outcomes.

Excludes: Correlated record loss, redundancy, and time-dependent retrieval availability.

References

Derivation: the scenario composes mlsysim.physics.statistics.calc_conjunctive_confidence. Their contracts supply the underlying equations; property equations show the remaining unit conversions and accounting.

Examples

>>> from mlsysim import Scenarios
>>> scenario = Scenarios.AdmittedCommandDecisionChain
>>> value = scenario.completeness_by_link
>>> type(value).__name__
'dict'

Attributes

Name Description
completeness_by_link Map each declared per-link probability to its complete-chain probability.
link_probabilities
links

Methods

Name Description
completeness Probability that all links are retrievable when each independently resolves with

completeness

engine.scenario_types.DecisionChainScenario.completeness(p)

Probability that all links are retrievable when each independently resolves with probability p.

Equation: calc_conjunctive_confidence([p] * links).

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