1 | import logging
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2 | from random import randint
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3 | from time import time
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4 | from typing import cast
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5 |
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6 | import pandas as pd
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7 | from geniusweb.actions.Accept import Accept
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8 | from geniusweb.actions.Action import Action
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9 | from geniusweb.actions.Offer import Offer
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10 | from geniusweb.actions.PartyId import PartyId
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11 | from geniusweb.bidspace.AllBidsList import AllBidsList
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12 | from geniusweb.inform.ActionDone import ActionDone
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13 | from geniusweb.inform.Finished import Finished
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14 | from geniusweb.inform.Inform import Inform
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15 | from geniusweb.inform.Settings import Settings
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16 | from geniusweb.inform.YourTurn import YourTurn
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17 | from geniusweb.issuevalue.Bid import Bid
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18 | from geniusweb.issuevalue.Domain import Domain
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19 | from geniusweb.party.Capabilities import Capabilities
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20 | from geniusweb.party.DefaultParty import DefaultParty
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21 | from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import (
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22 | LinearAdditiveUtilitySpace,
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23 | )
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24 | from geniusweb.profileconnection.ProfileConnectionFactory import (
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25 | ProfileConnectionFactory,
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26 | )
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27 | from geniusweb.progress.ProgressTime import ProgressTime
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28 | from geniusweb.references.Parameters import Parameters
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29 | from tudelft_utilities_logging.ReportToLogger import ReportToLogger
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30 |
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31 | from agents.template_agent.utils.opponent_model import OpponentModel
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32 |
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33 | # our imports
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34 | import numpy as np
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35 | from sklearn import tree
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36 | from sklearn.preprocessing import label_binarize
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37 | import random
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38 |
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39 |
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40 | class GEAAgent(DefaultParty):
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41 | """
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42 | Template of a Python geniusweb agent.
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43 | """
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44 |
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45 | def __init__(self):
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46 | super().__init__()
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47 | self.logger: ReportToLogger = self.getReporter()
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48 |
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49 | self.domain: Domain = None
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50 | self.parameters: Parameters = None
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51 | self.profile: LinearAdditiveUtilitySpace = None
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52 | self.progress: ProgressTime = None
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53 | self.me: PartyId = None
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54 | self.other: str = None
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55 | self.settings: Settings = None
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56 | self.storage_dir: str = None
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57 |
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58 | self.last_received_bid: Bid = None
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59 | self.opponent_model: OpponentModel = None
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60 | self.logger.log(logging.INFO, "party is initialized")
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61 |
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62 | # our parameters
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63 | # collect negitioation data
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64 | self.dataX = []
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65 | self.dataY = []
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66 | self.data_len = 0
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67 | self.issue_encoder = {}
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68 |
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69 | # decision tree and weights
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70 | self.decision_model = None
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71 | self.tree_depth = 20
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72 | self.orig_opponent_agree_weight = 0.15
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73 | self.opponent_agree_weight = self.orig_opponent_agree_weight
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74 | self.accept_threshold = 0.85 # for heuristic function, not utility.
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75 |
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76 | # define
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77 | self.OPPONENT_ACCEPT = 1
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78 | self.OPPONENT_REJECT = -1
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79 |
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80 | # bid dictionaries
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81 | self.bid_values = {}
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82 | self.all_issue_values = {}
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83 |
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84 | # bid lookup indices
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85 | # self.lower_threshold = 0
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86 | # self.higher_threshold = 200
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87 |
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88 | self.last_bid_utility = 0
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89 | self.debug_offer = 0
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90 |
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91 | self.randomn = random.uniform(0, 1)
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92 |
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93 | def notifyChange(self, data: Inform):
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94 | """MUST BE IMPLEMENTED
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95 | This is the entry point of all interaction with your agent after is has been initialised.
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96 | How to handle the received data is based on its class type.
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97 |
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98 | Args:
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99 | info (Inform): Contains either a request for action or information.
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100 | """
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101 |
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102 | # a Settings message is the first message that will be send to your
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103 | # agent containing all the information about the negotiation session.
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104 | if isinstance(data, Settings):
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105 | self.settings = cast(Settings, data)
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106 | self.me = self.settings.getID()
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107 |
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108 | # progress towards the deadline has to be tracked manually through the use of the Progress object
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109 | self.progress = self.settings.getProgress()
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110 |
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111 | self.parameters = self.settings.getParameters()
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112 | self.storage_dir = self.parameters.get("storage_dir")
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113 |
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114 | # the profile contains the preferences of the agent over the domain
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115 | profile_connection = ProfileConnectionFactory.create(
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116 | data.getProfile().getURI(), self.getReporter()
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117 | )
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118 | self.profile = profile_connection.getProfile()
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119 | self.domain = self.profile.getDomain()
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120 | profile_connection.close()
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121 |
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122 | # our code: init issue dictionary
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123 | self.init_bid_values()
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124 |
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125 | # ActionDone informs you of an action (an offer or an accept)
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126 | # that is performed by one of the agents (including yourself).
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127 | elif isinstance(data, ActionDone):
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128 | action = cast(ActionDone, data).getAction()
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129 | actor = action.getActor()
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130 |
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131 | # ignore action if it is our action
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132 | if actor != self.me:
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133 | # obtain the name of the opponent, cutting of the position ID.
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134 | self.other = str(actor).rsplit("_", 1)[0]
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135 |
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136 | # process action done by opponent
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137 | self.opponent_action(action)
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138 | # YourTurn notifies you that it is your turn to act
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139 | elif isinstance(data, YourTurn):
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140 | # execute a turn
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141 | self.my_turn()
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142 |
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143 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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144 | elif isinstance(data, Finished):
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145 | self.save_data()
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146 | # terminate the agent MUST BE CALLED
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147 | self.logger.log(logging.INFO, "party is terminating:")
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148 | super().terminate()
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149 | else:
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150 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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151 |
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152 | def getCapabilities(self) -> Capabilities:
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153 | """MUST BE IMPLEMENTED
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154 | Method to indicate to the protocol what the capabilities of this agent are.
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155 | Leave it as is for the ANL 2022 competition
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156 |
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157 | Returns:
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158 | Capabilities: Capabilities representation class
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159 | """
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160 | return Capabilities(
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161 | set(["SAOP"]),
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162 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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163 | )
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164 |
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165 | def send_action(self, action: Action):
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166 | """Sends an action to the opponent(s)
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167 |
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168 | Args:
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169 | action (Action): action of this agent
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170 | """
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171 | self.getConnection().send(action)
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172 |
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173 | # give a description of your agent
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174 | def getDescription(self) -> str:
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175 | """MUST BE IMPLEMENTED
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176 | Returns a description of your agent. 1 or 2 sentences.
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177 |
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178 | Returns:
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179 | str: Agent description
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180 | """
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181 | return "Template agent for the ANL 2022 competition"
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182 |
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183 | def opponent_action(self, action):
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184 | """Process an action that was received from the opponent.
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185 |
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186 | Args:
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187 | action (Action): action of opponent
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188 | """
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189 | # if it is an offer, set the last received bid
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190 | if isinstance(action, Offer):
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191 | # create opponent model if it was not yet initialised
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192 | if self.opponent_model is None:
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193 | self.opponent_model = OpponentModel(self.domain)
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194 |
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195 | bid = cast(Offer, action).getBid()
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196 |
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197 | # update opponent model with bid
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198 | self.opponent_model.update(bid)
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199 | # set bid as last received
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200 | self.last_received_bid = bid
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201 |
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202 | def my_turn(self):
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203 | """This method is called when it is our turn. It should decide upon an action
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204 | to perform and send this action to the opponent.
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205 | """
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206 |
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207 | # check if the last received offer is good enough
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208 | if self.accept_condition(self.last_received_bid):
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209 | # if so, accept the offer
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210 | action = Accept(self.me, self.last_received_bid)
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211 | else:
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212 | # if not, find a bid to propose as counter offer
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213 | bid = self.find_bid()
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214 | action = Offer(self.me, bid)
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215 | self.append_data_and_train_tree(bid, self.OPPONENT_REJECT)
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216 |
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217 | # send the action
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218 | self.send_action(action)
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219 |
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220 | def save_data(self):
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221 | """This method is called after the negotiation is finished. It can be used to store data
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222 | for learning capabilities. Note that no extensive calculations can be done within this method.
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223 | Taking too much time might result in your agent being killed, so use it for storage only.
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224 | """
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225 | data = "Data for learning (see README.md)"
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226 | with open(f"{self.storage_dir}/data.md", "w") as f:
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227 | f.write(data)
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228 |
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229 | ###########################################################################################
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230 | ################################## Example methods below ##################################
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231 | ###########################################################################################
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232 |
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233 | def accept_condition(self, bid: Bid) -> bool:
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234 | if bid is None:
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235 | return False
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236 |
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237 | # our code
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238 | # process new bid offer
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239 | heuristic_score = self.score_bid(bid)
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240 | objective_utility = self.profile.getUtility(bid)
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241 |
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242 | self.append_data_and_train_tree(bid, self.OPPONENT_ACCEPT)
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243 |
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244 | if objective_utility < self.last_bid_utility:
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245 | self.opponent_agree_weight *= 0.99
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246 | if objective_utility > self.last_bid_utility:
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247 | self.opponent_agree_weight /= 0.99
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248 |
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249 | if self.opponent_agree_weight == 0:
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250 | self.opponent_agree_weight = 0.01
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251 |
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252 | self.last_bid_utility = objective_utility
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253 |
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254 | # progress of the negotiation session between 0 and 1 (1 is deadline)
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255 | progress = self.progress.get(time() * 1000)
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256 |
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257 | conditions = [
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258 | progress > 0.95 and objective_utility > 0.4,
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259 | progress > 0.9 and objective_utility > 0.6,
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260 | objective_utility > 0.7,
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261 | heuristic_score >= self.accept_threshold,
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262 | ]
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263 | return any(conditions)
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264 |
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265 | def find_bid(self) -> Bid:
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266 | # compose a list of all possible bids
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267 | domain = self.profile.getDomain()
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268 | all_bids = AllBidsList(domain)
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269 |
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270 | best_bid_score = 0.0
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271 | best_bid = None
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272 |
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273 | # take 500 attempts to find a bid according to a heuristic score
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274 | for _ in range(500):
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275 | bid = all_bids.get(randint(0, all_bids.size() - 1))
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276 | bid_score = self.score_bid(bid)
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277 | if bid_score > best_bid_score:
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278 | best_bid_score, best_bid = bid_score, bid
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279 |
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280 | return best_bid
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281 |
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282 | def score_bid(self, bid: Bid, alpha: float = 0.95, eps: float = 0.1) -> float:
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283 | ''' Calculate heuristic score for a bid '''
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284 | progress = self.progress.get(time() * 1000)
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285 |
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286 | our_utility = float(self.profile.getUtility(bid))
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287 |
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288 | time_pressure = 1.0 - progress ** (1 / eps)
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289 | score = alpha * time_pressure * our_utility
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290 |
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291 | opponent_score = self.tree_predict(bid) * self.opponent_agree_weight
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292 | score += opponent_score
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293 |
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294 | return score
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295 |
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296 |
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297 | def tree_predict(self, bid: Bid) -> float:
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298 | ''' returns acceptance estimation for the other agent '''
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299 | bid_data = []
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300 | bid_issue_values = bid.getIssueValues()
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301 | domain_issues = list(bid_issue_values.keys())
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302 | domain_issues.sort()
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303 | for issue in domain_issues:
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304 | # encode categorical data
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305 | issue_encoded = label_binarize([str(bid_issue_values[issue])], classes=self.all_issue_values[issue])
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306 | # concat current category to X
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307 | bid_data.extend(issue_encoded.flatten().tolist())
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308 |
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309 | # if the tree is trained, we can use it to predict opponent reaction
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310 | if self.decision_model is not None:
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311 | tree_prediction = float(self.decision_model.predict(np.array(bid_data).reshape(1, -1)))
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312 | return tree_prediction
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313 |
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314 | return 0 # no knowledge
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315 |
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316 | def append_data_and_train_tree(self, bid: Bid, opponent_accept: int) -> None:
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317 | ''' appends new bid to negotiation history and retrain model '''
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318 | bid_data = []
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319 | bid_issue_values = bid.getIssueValues()
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320 | domain_issues = list(bid_issue_values.keys())
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321 | domain_issues.sort()
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322 | for issue in domain_issues:
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323 | # encode categorical data
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324 | issue_encoded = label_binarize([str(bid_issue_values[issue])], classes=self.all_issue_values[issue])
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325 | # concat current category to X
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326 | bid_data.extend(issue_encoded.flatten().tolist())
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327 |
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328 | self.data_len += 1
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329 | self.dataX.append(bid_data)
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330 | self.dataY.append(opponent_accept)
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331 |
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332 | # train tree if at least two samples were collected
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333 |
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334 | if self.data_len > 2:
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335 | self.decision_model = tree.DecisionTreeClassifier(criterion="entropy", max_depth=self.tree_depth)
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336 | self.decision_model.fit(self.dataX, self.dataY)
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337 |
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338 | def init_bid_values(self):
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339 | ''' must be called to binarize labels '''
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340 | domain = self.profile.getDomain()
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341 | domain_issues = domain.getIssues()
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342 | self.all_issue_values = {}
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343 | for issue in domain_issues:
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344 | self.all_issue_values[issue] = []
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345 | for value in domain.getValues(issue):
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346 | self.all_issue_values[issue].append(str(value))
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