Normalizable

class negmas.preferences.Normalizable(*args, **kwargs)

Bases: Shiftable, Scalable, Protocol

Can be normalized to a given range of values (default is 0-1)

Attributes Summary

base_type	Returns the utility_function base type ignoring discounting and similar wrappings.
type	Returns the preferences type.

Methods Summary

__call__(offer)	Call self as a function.
difference_prob(first, second)	Returns a numeric difference between the utility of the two given outcomes
eval(offer)	Evaluates the ufun without normalization (See eval_normalized )
eval_normalized(offer[, above_reserve, ...])	Evaluates the ufun normalizing the result between zero and one
is_better(first, second)	Compares two offers using the ufun returning whether the first is strictly better than the second
is_equivalent(first, second)	Compares two offers using the ufun returning whether the first is strictly equivelent than the second
is_not_better(first, second)	Compares two offers using the ufun returning whether the first is worse or equivalent than the second
is_not_worse(first, second)	Compares two offers using the ufun returning whether the first is better than the second
is_session_dependent()	Does the utiltiy of an outcome depend on the NegotiatorMechanismInterface?
is_state_dependent()	Does the utiltiy of an outcome depend on the negotiation state?
is_stationary()	Is the ufun stationary (i.e. utility value of an outcome is a constant)?.
is_volatile()	Does the utiltiy of an outcome depend on factors outside the negotiation?
is_worse(first, second)	Compares two offers using the ufun returning whether the first is strictly worse than the second
minmax()	Finds the minimum and maximum for the ufun
normalize([to])	rtype: TypeVar(N, bound= Normalizable)
scale_by(scale[, scale_reserved])	rtype: Scalable
scale_max(to[, rng])	rtype: Scalable
scale_min(to[, rng])	rtype: Scalable
shift_by(offset[, shift_reserved])	rtype: Shiftable
shift_max(to[, rng])	rtype: Shiftable
shift_min(to[, rng])	rtype: Shiftable

Attributes Documentation

base_type

Returns the utility_function base type ignoring discounting and similar wrappings.

type

Returns the preferences type.

Methods Documentation

__call__(offer)

Call self as a function.

Return type:

Union[Distribution, float]

abstract difference_prob(first, second)

Returns a numeric difference between the utility of the two given outcomes

Return type:

Distribution

eval(offer)

Evaluates the ufun without normalization (See eval_normalized )

Return type:

Union[Distribution, float]

eval_normalized(offer, above_reserve=True, expected_limits=True)

Evaluates the ufun normalizing the result between zero and one

Parameters:

• offer (Outcome | None) – offer

• above_reserve (bool) – If True, zero corresponds to the reserved value not the minimum

• expected_limits (bool) – If True, the expectation of the utility limits will be used for normalization instead of the maximum range and minimum lowest limit

Return type:

Union[Distribution, float]

Remarks:

• If the maximum and the minium are equal, finite and above reserve, will return 1.0.

• If the maximum and the minium are equal, initinte or below reserve, will return 0.0.

• For probabilistic ufuns, a distribution will still be returned.

• The minimum and maximum will be evaluated freshly every time. If they are already caached in the ufun, the cache will be used.

abstract is_better(first, second)

Compares two offers using the ufun returning whether the first is strictly better than the second

Parameters:

• first (tuple | None) – First outcome to be compared

• second (tuple | None) – Second outcome to be compared

Return type:

bool

Remarks:

• Should raise ValueError if the comparison cannot be done

abstract is_equivalent(first, second)

Compares two offers using the ufun returning whether the first is strictly equivelent than the second

Parameters:

• first (tuple | None) – First outcome to be compared

• second (tuple | None) – Second outcome to be compared

Return type:

bool

Remarks:

• Should raise ValueError if the comparison cannot be done

abstract is_not_better(first, second)

Compares two offers using the ufun returning whether the first is worse or equivalent than the second

Parameters:

• first (tuple) – First outcome to be compared

• second (tuple | None) – Second outcome to be compared

Return type:

bool

Remarks:

• Should raise ValueError if the comparison cannot be done

abstract is_not_worse(first, second)

Compares two offers using the ufun returning whether the first is better than the second

Parameters:

• first (tuple | None) – First outcome to be compared

• second (tuple | None) – Second outcome to be compared

• state – The negotiation state at which the comparison is done

Return type:

bool

Remarks:

• Should raise ValueError if the comparison cannot be done

abstract is_session_dependent()

Does the utiltiy of an outcome depend on the NegotiatorMechanismInterface?

Return type:

bool

abstract is_state_dependent()

Does the utiltiy of an outcome depend on the negotiation state?

Return type:

bool

abstract is_stationary()

Is the ufun stationary (i.e. utility value of an outcome is a constant)?

Return type:

bool

abstract is_volatile()

Does the utiltiy of an outcome depend on factors outside the negotiation?

Return type:

bool

Remarks:

• A volatile preferences is one that can change even for the same mechanism state due to outside influence

abstract is_worse(first, second)

Compares two offers using the ufun returning whether the first is strictly worse than the second

Parameters:

• first (tuple | None) – First outcome to be compared

• second (tuple | None) – Second outcome to be compared

Return type:

bool

Remarks:

• Should raise ValueError if the comparison cannot be done

abstract minmax()

Finds the minimum and maximum for the ufun

Return type:

tuple[float, float]

abstract normalize(to=(0.0, 1.0))

Return type:

TypeVar(N, bound= Normalizable)

abstract scale_by(scale, scale_reserved=True)

Return type:

Scalable

abstract scale_max(to, rng=None)

Return type:

Scalable

abstract scale_min(to, rng=None)

Return type:

Scalable

abstract shift_by(offset, shift_reserved=True)

Return type:

Shiftable

abstract shift_max(to, rng=None)

Return type:

Shiftable

abstract shift_min(to, rng=None)

Return type:

Shiftable