Source code for scml.oneshot.agents.aspiration

import itertools
import random
from typing import Dict

from negmas import Outcome
from negmas import PolyAspiration
from negmas import ResponseType
from negmas.outcomes.issue_ops import enumerate_issues
from negmas.sao import SAOResponse

from ..agent import OneShotSyncAgent

__all__ = ["SingleAgreementAspirationAgent"]


[docs]class SingleAgreementAspirationAgent(OneShotSyncAgent): """ Uses a time-based strategy to accept a single agreement from the set it is considering. """
[docs] def before_step(self): self.__endall = not self.awi.is_first_level and not self.awi.is_last_level if self.__endall: return # we assume that we are either in the first or the latest layer # and calculate our ufun limits and reserved value self.ufun.reserved_value = self.ufun.from_contracts([]) self._reserved_value = self.ufun.reserved_value self._asp = PolyAspiration( max_aspiration=1.0, aspiration_type=float(random.randint(1, 4)) if random.random() < 0.7 else random.random(), ) # if self.awi.current_exogenous_input_quantity or self.awi.current_exogenous_output_quantity: # breakpoint() self._limit = self.ufun.find_limit( True, int(self.awi.is_last_level), int(self.awi.is_first_level) ) self._max_utility = self._limit.utility urange = self._max_utility - self._reserved_value if urange <= 1e-5: urange = 1e-5 self._urange = urange if self.awi.is_last_level: self._best = (self._limit.input_quantity, self._limit.input_price) else: self._best = (self._limit.output_quantity, self._limit.output_price) # compile a list of all outcomes with their utilities and sort it # descendigly by utility issues = ( self.awi.current_output_issues if self.awi.is_first_level else self.awi.current_output_issues ) outcomes = list(enumerate_issues(issues)) self._outcomes = sorted( zip( ( ( self.ufun.from_offers((_,), (self.awi.is_first_level,)) - self._reserved_value ) / (self._urange) for _ in outcomes ), outcomes, ), key=lambda x: -x[0], ) self._last_index = 0
[docs] def counter_all(self, offers, states): if self.__endall: return dict( zip( offers.keys(), itertools.repeat(SAOResponse(ResponseType.END_NEGOTIATION, None)), ) ) # find current aspiration level between zero and one asp = max( self._asp.utility_at(state.relative_time) for state in states.values() ) # acceptance strategy partner_utils = sorted( zip( offers.keys(), ( (self.ufun(_) - self._reserved_value) / self._urange for _ in offers.values() ), ), key=lambda x: -x[1], ) if partner_utils[0][1] >= asp: response = dict( zip( offers.keys(), itertools.repeat(SAOResponse(ResponseType.END_NEGOTIATION, None)), ) ) response[partner_utils[0][0]] = SAOResponse(ResponseType.ACCEPT_OFFER, None) self.__endall = True return response # offering strategy i = self._last_index for i, (u, _) in enumerate(self._outcomes[self._last_index :]): if u >= asp: continue if i > 0: outcome, self._last_index = self._outcomes[i - 1][1], i - 1 else: outcome, self._last_index = self._outcomes[0][1], 0 break else: outcome, self._last_index = self._outcomes[i][1], i return self.choose_agents(offers, outcome)
[docs] def choose_agents(self, offers, outcome): """Selects an appropriate way to distribute this outcome to agents with given IDs.""" if len(offers) == 0: return dict() # fidn the partner which gave me the offer most similar to my best dists = sorted( ( (sum((a - b) * (a - b) for a, b in zip(outcome, v)), k) for k, v in offers.items() ), key=lambda x: x[0], ) # offer everyone nothing excdpt the one agent that gave me the offer most # similar to my preferred outcome result = dict( zip( offers.keys(), itertools.repeat(SAOResponse(ResponseType.REJECT_OFFER, None)), ) ) result[dists[0][1]] = SAOResponse(ResponseType.REJECT_OFFER, outcome) return result
[docs] def first_proposals(self) -> Dict[str, "Outcome"]: """ Gets a set of proposals to use for initializing the negotiation. Returns: A dictionary mapping each negotiator (in self.negotiators dict) to an outcome to be used as the first proposal if the agent is to start a negotiation. """ if self.__endall: return dict( zip( self.negotiators.keys(), itertools.repeat(None), ) ) # that is a risk. The agent will send its best offer to everyone risking # two of them accepting it which is suboptimal. return dict( zip( self.negotiators.keys(), itertools.repeat(self._outcomes[0][1]), ) )