| 1 | from decimal import Decimal
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| 2 | import logging
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| 3 | import json
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| 4 | from os import path
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| 5 | from random import randint
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| 6 | from re import A
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| 7 | from time import time
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| 8 | from typing import Optional, cast
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| 9 |
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| 10 | from geniusweb.actions.Accept import Accept
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| 11 | from geniusweb.actions.Action import Action
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| 12 | from geniusweb.actions.Offer import Offer
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| 13 | from geniusweb.actions.PartyId import PartyId
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| 14 | from geniusweb.bidspace.AllBidsList import AllBidsList
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| 15 | from geniusweb.bidspace.BidsWithUtility import BidsWithUtility
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| 16 | from geniusweb.bidspace.Interval import Interval
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| 17 | from geniusweb.inform.ActionDone import ActionDone
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| 18 | from geniusweb.inform.Finished import Finished
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| 19 | from geniusweb.inform.Inform import Inform
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| 20 | from geniusweb.inform.Settings import Settings
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| 21 | from geniusweb.inform.YourTurn import YourTurn
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| 22 | from geniusweb.issuevalue.Bid import Bid
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| 23 | from geniusweb.issuevalue.Domain import Domain
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| 24 | from geniusweb.party.Capabilities import Capabilities
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| 25 | from geniusweb.party.DefaultParty import DefaultParty
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| 26 | from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import (
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| 27 | LinearAdditiveUtilitySpace,
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| 28 | )
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| 29 | from geniusweb.profileconnection.ProfileConnectionFactory import (
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| 30 | ProfileConnectionFactory,
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| 31 | )
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| 32 | from geniusweb.progress.ProgressTime import ProgressTime
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| 33 | from geniusweb.references.Parameters import Parameters
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| 34 | from numpy import append
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| 35 | from tudelft_utilities_logging.ReportToLogger import ReportToLogger
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| 36 | from .utils import opponent_model
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| 37 |
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| 38 | from .utils.opponent_model import OpponentModel
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| 39 | from .utils.time_estimator import TimeEstimator
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| 40 | from .utils.bid_chooser_2 import BidChooser
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| 41 | from .utils.strategy_model import StrategyModel
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| 42 |
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| 43 | # Some testing flags
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| 44 | test_use_accept = True
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| 45 | class ProcrastinAgent(DefaultParty):
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| 46 | """
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| 47 | The Mild Bunch Team's Python geniusweb agent.
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| 48 | """
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| 49 |
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| 50 | def __init__(self):
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| 51 | super().__init__()
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| 52 | self.logger: ReportToLogger = self.getReporter()
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| 53 |
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| 54 | self.domain: Domain = None
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| 55 | self.parameters: Parameters = None
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| 56 | self.profile: LinearAdditiveUtilitySpace = None
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| 57 | self.progress: ProgressTime = None
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| 58 | self.me: PartyId = None
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| 59 | self.other: str = None
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| 60 | self.settings: Settings = None
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| 61 | self.storage_dir: str = None
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| 62 | self.strategy_model = None
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| 63 |
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| 64 | self.last_received_bid: Bid = None
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| 65 | self.opponent_best_bid: Bid = None
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| 66 | self.opp_best_self_util: Bid = None
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| 67 | self.opponent_concession_bid: Bid = None
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| 68 | self.opp_concession_self_util: Bid = 0.0
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| 69 | self.alpha: float = 0.5
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| 70 | self.lowest_acceptable: float = 1.0
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| 71 | self.opponent_model: OpponentModel = None
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| 72 | self.bid_chooser: BidChooser = None
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| 73 | self.opponent_data: dict = None
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| 74 | self.time_estimator: TimeEstimator = TimeEstimator()
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| 75 | self.bids_sent: int = 0
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| 76 | self.bids_received: int = 0
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| 77 | self.logger.log(logging.INFO, "party is initialized")
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| 78 |
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| 79 | self.test_bids_left = []
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| 80 |
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| 81 | def getCapabilities(self) -> Capabilities:
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| 82 | """MUST BE IMPLEMENTED
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| 83 | Method to indicate to the protocol what the capabilities of this agent are.
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| 84 | Leave it as is for the ANL 2022 competition
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| 85 |
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| 86 | Returns:
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| 87 | Capabilities: Capabilities representation class
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| 88 | """
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| 89 | return Capabilities(
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| 90 | set(["SAOP"]),
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| 91 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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| 92 | )
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| 93 |
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| 94 | def getDescription(self) -> str:
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| 95 | """MUST BE IMPLEMENTED
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| 96 |
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| 97 | Returns:
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| 98 | str: Agent description
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| 99 | """
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| 100 | return "The Mild Bunch's ProcrastinAgent for the ANL 2022 competition." \
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| 101 | " This agent puts off concesssion till the end of the negotiation."
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| 102 | " It's developers are also procrastinagents! The Procrastin-A-Team!"
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| 103 |
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| 104 | def send_action(self, action: Action):
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| 105 | """Sends an action to the opponent(s)
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| 106 |
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| 107 | Args:
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| 108 | action (Action): action of this agent
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| 109 | """
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| 110 | self.getConnection().send(action)
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| 111 |
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| 112 | def extract_name(self, party: PartyId) -> str:
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| 113 | return str(party).rsplit("_", 1)[0]
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| 114 |
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| 115 | def current_time(self) -> float:
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| 116 | return self.progress.get(time() * 1000)
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| 117 |
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| 118 | def notifyChange(self, data: Inform):
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| 119 | """MUST BE IMPLEMENTED
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| 120 | This is the entry point of all interaction with your agent after is has been initialised.
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| 121 | How to handle the received data is based on its class type.
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| 122 |
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| 123 | Args:
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| 124 | data (Inform): Contains either a request for action or information.
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| 125 | """
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| 126 |
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| 127 | # a Settings message is the first message that will be send to your
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| 128 | # agent containing all the information about the negotiation session.
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| 129 | if isinstance(data, Settings):
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| 130 | self.settings = cast(Settings, data)
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| 131 | self.me = self.settings.getID()
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| 132 |
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| 133 | # progress towards the deadline has to be tracked manually through the use of the Progress object
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| 134 | self.progress = self.settings.getProgress()
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| 135 |
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| 136 | self.parameters = self.settings.getParameters()
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| 137 | self.storage_dir = self.parameters.get("storage_dir")
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| 138 |
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| 139 | # the profile contains the preferences of the agent over the domain
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| 140 | profile_connection = ProfileConnectionFactory.create(
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| 141 | data.getProfile().getURI(), self.getReporter()
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| 142 | )
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| 143 | self.profile = profile_connection.getProfile()
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| 144 | self.domain = self.profile.getDomain()
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| 145 | profile_connection.close()
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| 146 |
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| 147 | # ActionDone informs you of an action (an offer or an accept)
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| 148 | # that is performed by one of the agents (including yourself).
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| 149 | elif isinstance(data, ActionDone):
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| 150 | action = cast(ActionDone, data).getAction()
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| 151 | actor = action.getActor()
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| 152 |
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| 153 | # ignore action if it is our action
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| 154 | if actor != self.me:
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| 155 | if self.other is None:
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| 156 | # obtain the name of the opponent, cutting of the position ID.
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| 157 | self.other = self.extract_name(actor)
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| 158 | # now that the name of the opponent is known, we load our stored data about them
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| 159 | self.load_data()
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| 160 |
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| 161 | # process action done by opponent
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| 162 | self.opponent_action(action)
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| 163 | # YourTurn notifies you that it is your turn to act
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| 164 | elif isinstance(data, YourTurn):
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| 165 | # execute a turn
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| 166 | self.my_turn()
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| 167 |
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| 168 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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| 169 | elif isinstance(data, Finished):
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| 170 | finished = cast(Finished, data)
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| 171 | self.save_data(finished)
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| 172 | # terminate the agent MUST BE CALLED
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| 173 | self.logger.log(logging.INFO, "party is terminating:")
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| 174 | super().terminate()
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| 175 | else:
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| 176 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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| 177 |
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| 178 | def opponent_action(self, action: Action):
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| 179 | """Process an action that was received from the opponent.
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| 180 |
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| 181 | Args:
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| 182 | action (Action): action of opponent
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| 183 | """
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| 184 | if isinstance(action, Accept):
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| 185 | # opponent accepted, no response necessary
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| 186 | pass
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| 187 |
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| 188 | # if it is an offer, set the last received bid
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| 189 | if isinstance(action, Offer):
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| 190 | offer = cast(Offer, action)
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| 191 | self.bids_received += 1
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| 192 | self.process_opponent_offer(offer)
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| 193 |
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| 194 | def my_turn(self):
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| 195 | """This method is called when it is our turn. It should decide upon an action
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| 196 | to perform and send this action to the opponent.
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| 197 | """
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| 198 | # log opponent time
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| 199 | self.time_estimator.self_times_add(self.current_time())
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| 200 | # check if the last received offer is good enough
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| 201 | if self.choose_accept(self.last_received_bid):
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| 202 | # if so, accept the offer
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| 203 | action = Accept(self.me, self.last_received_bid)
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| 204 | else:
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| 205 | # if not, find a bid to propose as counter offer
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| 206 | bid = self.choose_bid()
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| 207 | action = Offer(self.me, bid)
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| 208 | self.bids_sent += 1
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| 209 |
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| 210 | # log self time
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| 211 | self.time_estimator.opp_times_add(self.current_time())
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| 212 | # send the action
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| 213 | self.send_action(action)
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| 214 |
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| 215 | ################################################################################################
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| 216 | ################################### Our Implementation ###################################
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| 217 | ################################################################################################
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| 218 |
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| 219 | def load_data(self):
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| 220 | # load_data is called as soon as the opponent is known.
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| 221 | # In the very rare case where the opponent never makes an offer, load_data is never called.
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| 222 | if not path.exists(f"{self.storage_dir}/{self.other}.json"):
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| 223 | # First round
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| 224 | new_data = {}
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| 225 | new_data["count"] = 0
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| 226 | new_data["self_accepts"] = 0
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| 227 | new_data["did_accept"] = []
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| 228 | new_data["opponent_accepts"] = 0
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| 229 | new_data["no_accepts"] = 0
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| 230 | new_data["beta_values"] = []
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| 231 | new_data["time_factor"] = 1.0
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| 232 | new_data["alphas"] = []
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| 233 | new_data["alpha_achieved"] = []
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| 234 | self.opponent_data = new_data
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| 235 | else:
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| 236 | # Not first round
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| 237 | with open(f"{self.storage_dir}/{self.other}.json", "r") as f:
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| 238 | self.opponent_data = json.load(f)
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| 239 | self.time_estimator.update_time_factor(self.opponent_data["time_factor"])
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| 240 |
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| 241 | def choose_bid(self) -> Bid:
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| 242 | if self.bids_sent <= 5:
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| 243 | # Action to take before we have a decent estimate at how many turns are left
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| 244 | # Send best bid
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| 245 | bid_dict = {}
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| 246 | for issue, valueset in self.profile.getUtilities().items():
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| 247 | bid_dict[issue] = max(self.domain.getValues(issue), key = lambda v: valueset.getUtility(v))
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| 248 | best_bid = Bid(bid_dict)
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| 249 | return best_bid
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| 250 |
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| 251 | offers_left = self.time_estimator.turns_left(self.current_time())
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| 252 | self.test_bids_left.append(offers_left)
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| 253 | return self.bid_chooser.choose_bid(offers_left, self.current_time())
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| 254 |
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| 255 | def process_opponent_offer(self, offer: Offer):
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| 256 | """Process an offer that was received from the opponent.
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| 257 |
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| 258 | Args:
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| 259 | offer (Offer): offer from opponent
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| 260 | """
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| 261 | # create opponent model if it was not yet initialised
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| 262 | if self.opponent_model is None:
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| 263 | self.opponent_model = OpponentModel(self.domain)
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| 264 | self.bid_chooser = BidChooser(self.profile, self.opponent_model, 0.5) # TODO update lowest acceptable number
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| 265 |
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| 266 | bid = offer.getBid()
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| 267 |
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| 268 | # set bid as last received
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| 269 | self.last_received_bid = bid
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| 270 | # update opponent model with bid
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| 271 | self.opponent_model.update(bid, self.current_time())
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| 272 |
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| 273 | #set opp_highest_bid to either the first or calculated highest (opp POV) opponent bid
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| 274 | update_opponent_best = False
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| 275 | if self.opponent_best_bid is None:
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| 276 | update_opponent_best = True
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| 277 | elif self.opponent_model.get_predicted_utility(bid)[0] > self.opponent_model.get_predicted_utility(self.opponent_best_bid)[0]:
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| 278 | #update opp_best_bid if new opponent bid is better for them than previous best
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| 279 | update_opponent_best = True
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| 280 | if update_opponent_best:
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| 281 | self.opponent_best_bid = bid
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| 282 | self.opp_best_self_util = float(self.profile.getUtility(bid))
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| 283 | if self.strategy_model is None:
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| 284 | self.strategy_model = StrategyModel(self.opponent_data["alphas"], self.opponent_data["beta_values"], self.opponent_data["did_accept"])
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| 285 | self.alpha = self.strategy_model.max_u(self.opp_best_self_util, 0.5, 1.0, mag = 3)
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| 286 | self.lowest_acceptable = self.opp_best_self_util + self.alpha * (1.0 - self.opp_best_self_util)
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| 287 | self.bid_chooser.update_lowest_acceptable(self.lowest_acceptable)
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| 288 |
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| 289 | #set opp_concession_bid to either the first or highest (self POV) opponent bid
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| 290 | update_opponent_concession = False
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| 291 | if self.opponent_concession_bid is None:
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| 292 | update_opponent_concession = True
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| 293 | elif float(self.profile.getUtility(bid)) > self.opp_concession_self_util:
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| 294 | update_opponent_concession = True
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| 295 | if update_opponent_concession:
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| 296 | self.opponent_concession_bid = bid
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| 297 | self.opp_concession_self_util = float(self.profile.getUtility(bid))
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| 298 |
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| 299 | # update bid_chooser
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| 300 | self.bid_chooser.update_bid(bid)
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| 301 |
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| 302 | def choose_accept(self, bid: Bid) -> bool:
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| 303 | if bid is None:
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| 304 | return False
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| 305 |
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| 306 | # very basic approach that accepts if the offer is valued above a certain amount of opponent's highest utility, calculated
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| 307 | # through formula t = b + alpha (1.0 - b)
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| 308 | # only accepts during last 20 turns or last 1/1000 of the negotiation (or our best was offered)
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| 309 | bid_util = float(self.profile.getUtility(bid))
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| 310 | time = self.current_time()
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| 311 | conditions = [
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| 312 | bid_util >= max(self.lowest_acceptable, self.opp_concession_self_util),
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| 313 | any([
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| 314 | self.time_estimator.turns_left(time) < 20,
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| 315 | time > 0.999,
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| 316 | bid_util >= 1.0,
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| 317 | ]),
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| 318 | #test_use_accept, # Tests agent behaviour without accepting
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| 319 | ]
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| 320 | return all(conditions)
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| 321 |
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| 322 | def save_data(self, finished: Finished):
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| 323 | """This method is called after the negotiation is finished. It can be used to store data
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| 324 | for learning capabilities. Note that no extensive calculations can be done within this method.
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| 325 | Taking too much time might result in your agent being killed, so use it for storage only.
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| 326 | """
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| 327 | agreements = list(finished.getAgreements().getAgreements().items())
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| 328 | save = self.opponent_data
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| 329 | save["count"] += 1
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| 330 | save["test_bid_pool_size"] = len(self.bid_chooser.bid_pool)
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| 331 | save["test_time_list_self"] = self.time_estimator.self_times
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| 332 | #save["test_time_list_opp"] = self.time_estimator.opp_times_adj
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| 333 | save["test_offers_left"] = self.test_bids_left
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| 334 | save["self_diff"] = self.time_estimator.self_diff
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| 335 |
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| 336 | opp_stuff = {"weights": {}}
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| 337 | total_weight = 0.0
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| 338 | for issue in self.domain.getIssues():
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| 339 | opp_stuff[issue] = {}
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| 340 | total_weight += self.opponent_model.issue_estimators[issue].weight
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| 341 | opp_stuff["weights"][issue] = self.opponent_model.issue_estimators[issue].weight
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| 342 | for value in self.domain.getValues(issue):
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| 343 | opp_stuff[issue][value.getValue()] = self.opponent_model.issue_estimators[issue].get_value_utility(value)
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| 344 | for issue in self.domain.getIssues():
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| 345 | opp_stuff["weights"][issue] = self.opponent_model.issue_estimators[issue].weight / total_weight
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| 346 | save["opponent_model"] = opp_stuff
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| 347 |
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| 348 | beta = float((self.opp_concession_self_util-self.opp_best_self_util)/(1 - self.opp_best_self_util))
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| 349 |
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| 350 | save["beta_values"].append(beta)
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| 351 |
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| 352 | if not agreements:
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| 353 | agreement_bid = None
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| 354 | agreement_party = None
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| 355 | save["time_factor"] = self.time_estimator.get_new_time_factor(self.test_bids_left, len(self.bid_chooser.bid_pool))
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| 356 | save["did_accept"].append(False)
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| 357 | else:
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| 358 | agreement = agreements[0]
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| 359 | agreement_bid = agreement[1]
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| 360 | agreement_party = agreement[0]
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| 361 | save["did_accept"].append(True)
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| 362 | if agreement_party is None:
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| 363 | # No agreement was made (or rarely they accepted our first bid)
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| 364 | save["no_accepts"] += 1
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| 365 | elif self.extract_name(agreement_party) == self.extract_name(self.me):
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| 366 | # We sent the agreement
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| 367 | save["self_accepts"] += 1
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| 368 | elif (self.other is not None) and self.extract_name(agreement_party) == self.other:
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| 369 | # They accepted
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| 370 | save["opponent_accepts"] += 1
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| 371 | else:
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| 372 | # Only way I can imagine getting here is if we offered
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| 373 | # the first bid and the opponent accepted.
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| 374 | save["other_accepts"] = save.get("other_accepts", 0) + 1
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| 375 | pass
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| 376 |
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| 377 | if agreement_bid is None:
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| 378 | alpha_achieved = 0.0
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| 379 | else:
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| 380 | alpha_achieved = (float(self.profile.getUtility(agreement_bid)) - self.opp_best_self_util) / (1.0 - self.opp_best_self_util)
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| 381 | save["alphas"].append(self.alpha)
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| 382 | save["alpha_achieved"].append(alpha_achieved)
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| 383 |
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| 384 | with open(f"{self.storage_dir}/{self.other}.json", "w") as f:
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| 385 | json.dump(save, f) |
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