def test_acceptable_outcomes():
p = SAOMechanism(outcomes=6, n_steps=10)
p.add(
LimitedOutcomesNegotiator(name="seller", acceptable_outcomes=[(2,), (3,), (5,)])
)
p.add(
LimitedOutcomesNegotiator(name="buyer", acceptable_outcomes=[(1,), (4,), (3,)])
)
state = p.run()
assert state.agreement == (3,)
def test_elicitor_runs(
self, elicitor: Union[str, "BaseElicitor"], master, true_utilities, **kwargs
):
neg = SAOMechanism(outcomes=n_outcomes, n_steps=10)
user, strategy = master
opponent = LimitedOutcomesNegotiator(
acceptable_outcomes=accepted,
acceptance_probabilities=[1.0] * len(accepted),
)
strategy.on_enter(ami=neg.ami)
if isinstance(elicitor, str):
elicitor = f"negmas.elicitation.{elicitor}"
if "VOI" in elicitor:
kwargs["dynamic_query_set"] = True
elicitor = instantiate(elicitor, strategy=strategy, user=user, **kwargs)
neg.add(opponent)
neg.add(elicitor)
assert elicitor.elicitation_cost == 0.0
neg.run()
queries = list(elicitor.user.elicited_queries())
assert len(neg.history) > 0
assert neg.agreement is None or neg.agreement in accepted
assert (
elicitor.elicitation_cost > 0.0
or cost == 0.0
or elicitor.strategy is None
or neg.state.step < 2
)
if neg.agreement is not None:
assert (
elicitor.user_ufun(neg.agreement)
== true_utilities[neg.agreement[0]] - elicitor.elicitation_cost
)
if hasattr(elicitor, "each_outcome_once") and elicitor.each_outcome_once:
assert len(set([_[0] for _ in elicitor.offers])) == len(elicitor.offers)
def test_voi_baarslag_example(self):
outcomes = [(0, ), (1, )]
alphas = [0.0, 1.0]
opponent = LimitedOutcomesNegotiator(
acceptable_outcomes=outcomes,
acceptance_probabilities=alphas,
)
def test_elicitor_can_run_from_genius_domain(self, data_folder):
domain, agents_info, issues = load_genius_domain_from_folder(
os.path.join(data_folder, "Laptop"),
force_single_issue=True,
keep_issue_names=False,
keep_value_names=False,
)
domain.add(LimitedOutcomesNegotiator(), ufun=agents_info[0]["ufun"])
# domain.n_steps = 10
user = User(ufun=agents_info[0]["ufun"], cost=0.2)
strategy = EStrategy(strategy="titration-0.5")
strategy.on_enter(ami=domain.ami)
elicitor = PandoraElicitor(strategy=strategy, user=user)
domain.add(elicitor)
front, _ = domain.pareto_frontier()
domain.run()
assert len(domain.history) > 0
def test_elicitor_can_get_frontier(self, data_folder):
domain, agents_info, issues = load_genius_domain_from_folder(
os.path.join(data_folder, "Laptop"),
force_single_issue=True,
keep_issue_names=False,
keep_value_names=False,
normalize_utilities=True,
)
assert len(issues) == 1
assert len(agents_info) == 2
domain.add(LimitedOutcomesNegotiator(), ufun=agents_info[0]["ufun"])
user = User(ufun=agents_info[0]["ufun"], cost=cost)
strategy = EStrategy(strategy="titration-0.5")
strategy.on_enter(ami=domain.ami)
elicitor = FullKnowledgeElicitor(strategy=strategy, user=user)
domain.add(elicitor)
front, _ = domain.pareto_frontier()
assert front == [(1.0, 1.0)]
def test_full_knowledge(self, master):
user, strategy = master
neg = SAOMechanism(outcomes=[(_,) for _ in range(n_outcomes)], n_steps=10)
opponent = LimitedOutcomesNegotiator(
acceptable_outcomes=accepted,
acceptance_probabilities=[1.0] * len(accepted),
)
elicitor = FullKnowledgeElicitor(user=user)
neg.add(opponent)
neg.add(elicitor, ufun=u0(neg))
neg.run()
# print(
# f'Got {elicitor.ufun(neg.agreement)} with elicitation cost {elicitor.elicitation_cost}'
# f' for {elicitor} using 0 elicited_queries')
assert len(neg.history) > 0
assert neg.agreement is None or neg.agreement in accepted
# assert len(set([_[0] for _ in elicitor.offers])) > 1 or len(elicitor.offers) < 2
assert elicitor.elicitation_cost == 0.0
def test_pareto_frontier_2(self):
n_outcomes = 10
strategy = "titration-0.5"
cost = 0.01
reserved_value = 0.1
outcomes = [(_,) for _ in range(n_outcomes)]
accepted = [(2,), (3,), (4,), (5,)]
elicitor_utilities = [
0.5337723805661662,
0.8532272031479199,
0.4781281413197942,
0.7242899747791032,
0.3461879818432919,
0.2608677043479706,
0.9419131964655383,
0.29368079952747694,
0.6093201983562316,
0.7066918086398718,
]
# list(np.random.rand(n_outcomes).tolist())
opponent_utilities = [
1.0 if (_,) in accepted else 0.0 for _ in range(n_outcomes)
]
frontier, frontier_locs = pareto_frontier(
[
MappingUtilityFunction(
lambda o: elicitor_utilities[o[0]],
reserved_value=reserved_value,
outcome_type=tuple,
),
MappingUtilityFunction(
lambda o: opponent_utilities[o[0]],
reserved_value=reserved_value,
outcome_type=tuple,
),
],
outcomes=outcomes,
sort_by_welfare=True,
)
welfare = (
np.asarray(elicitor_utilities) + np.asarray(opponent_utilities)
).tolist()
# print(f'frontier: {frontier}\nmax. welfare: {max(welfare)} at outcome: ({welfare.index(max(welfare))},)')
# print(f'frontier_locs: frontier_locs')
neg = SAOMechanism(outcomes=n_outcomes, n_steps=10, outcome_type=tuple)
opponent = LimitedOutcomesNegotiator(
acceptable_outcomes=accepted,
acceptance_probabilities=[1.0] * len(accepted),
)
eufun = MappingUtilityFunction(
dict(zip(outcomes, elicitor_utilities)),
reserved_value=reserved_value,
outcome_type=tuple,
)
user = User(ufun=eufun, cost=cost)
strategy = EStrategy(strategy=strategy)
strategy.on_enter(ami=neg.ami)
elicitor = FullKnowledgeElicitor(strategy=strategy, user=user)
neg.add(opponent)
neg.add(elicitor)
neg.run()
f2, f2_outcomes = neg.pareto_frontier(sort_by_welfare=True)
assert len(frontier) == len(f2)
assert all([_1 == _2] for _1, _2 in zip(frontier, f2))
assert [_[0] for _ in f2_outcomes] == frontier_locs