UncertainOpponentModel

class negmas.models.acceptance.UncertainOpponentModel(outcomes, opponents, uncertainty=0.5, adaptive=False, rejection_discount=0.95, rejection_delta=0.0, constant_base=True, accesses_real_acceptance=False)[source]

Bases: AggregatingDiscreteAcceptanceModel

A model for which the uncertainty about the acceptance probability of different negotiators is controllable.

This is not a realistic model but it can be used to experiment with effects of this uncertainty on different negotiation related algorithms (e.g. elicitation algorithms)

Parameters:

outcomes (Collection[tuple]) – The list of possible outcomes
uncertainty (float) – The uncertainty level. Zero means no uncertainty and 1.0 means maximum uncertainty
adaptive (bool) – If true then the random part will learn from experience with the opponents otherwise it will not.
rejection_discount (float) – Only effective if adaptive is True. See AdaptiveDiscreteAcceptanceModel
rejection_delta (float) – Only effective if adaptive is True. See AdaptiveDiscreteAcceptanceModel

Methods Summary

`acceptance_probabilities`()	rtype: `ndarray`
`probability_of_acceptance`(outcome)
`probability_of_acceptance_indx`(outcome_index)	rtype: `float`
`update_accepted`(outcome)
`update_offered`(outcome)
`update_offered_indx`(outcome_index)
`update_rejected`(outcome)
`update_rejected_indx`(outcome_index)

Methods Documentation

acceptance_probabilities()

Return type:: ndarray

probability_of_acceptance(outcome)

probability_of_acceptance_indx(outcome_index)

Return type:: float

update_accepted(outcome)

update_offered(outcome)

update_offered_indx(outcome_index)

update_rejected(outcome)

update_rejected_indx(outcome_index)