1 | import logging
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2 | import time
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3 | from typing import cast
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4 | from operator import itemgetter
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5 | import numpy as np
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6 | from decimal import Decimal
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7 |
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8 | from geniusweb.actions.Accept import Accept
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9 | from geniusweb.actions.Action import Action
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10 | from geniusweb.actions.Offer import Offer
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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.party.Capabilities import Capabilities
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19 | from geniusweb.party.DefaultParty import DefaultParty
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20 | from geniusweb.profileconnection.ProfileConnectionFactory import (
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21 | ProfileConnectionFactory,
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22 | )
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23 | from geniusweb.progress.ProgressRounds import ProgressRounds
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24 | from .FreqModelWeighted import FreqModelWeighted
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25 | from tudelft_utilities_logging.Reporter import Reporter
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26 |
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27 | """
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28 | BeanBot agent
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29 | """
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30 | class Agent52(DefaultParty):
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31 |
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32 | def __init__(self, reporter: Reporter = None):
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33 | super().__init__(reporter)
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34 | self.getReporter().log(logging.INFO, "party is initialized")
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35 | self._profile = None
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36 | self._last_received_bid: Bid = None
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37 | self._last_received_action = None
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38 | self._opp_model = None
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39 | self._window_size = 10 # last 10 opponent bids are stored window below
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40 | self._opp_bids_window = []
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41 | self._opp_best_bid = None
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42 |
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43 | def notifyChange(self, info: Inform):
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44 | """This is the entry point of all interaction with your agent after is has been initialised.
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45 |
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46 | Args:
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47 | info (Inform): Contains either a request for action or information.
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48 | """
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49 |
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50 | # a Settings message is the first message that will be send to your
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51 | # agent containing all the information about the negotiation session.
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52 | if isinstance(info, Settings):
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53 | self._settings: Settings = cast(Settings, info)
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54 | self._me = self._settings.getID()
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55 |
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56 | # progress towards the deadline has to be tracked manually through the use of the Progress object
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57 | self._progress: ProgressRounds = self._settings.getProgress()
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58 |
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59 | # the profile contains the preferences of the agent over the domain
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60 | self._profile = ProfileConnectionFactory.create(
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61 | info.getProfile().getURI(), self.getReporter()
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62 | )
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63 |
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64 | # Create the weighted frequency model
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65 | self._opp_model = FreqModelWeighted.create().With(self._profile.getProfile().getDomain(), None)
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66 | self._opp_model.__class__ = FreqModelWeighted
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67 |
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68 |
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69 | # Generate sorted (decr.) list of all possible bids with their corresponding utility values
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70 | # Create reservation value after
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71 | profile = self._profile.getProfile()
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72 | allBids = AllBidsList(self._profile.getProfile().getDomain())
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73 | self._bid_utility_tuple = [(bid, profile.getUtility(bid)) for bid in allBids]
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74 | self._bid_utility_tuple.sort(key=itemgetter(1), reverse=True)
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75 |
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76 | # set reservation value to maximum of (0.4, worst bid utility in domain)
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77 | alpha = 0.4
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78 | self._rsv_val = alpha if alpha > self._bid_utility_tuple[-1][1] else self._bid_utility_tuple[-1][1]
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79 |
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80 | # ActionDone is an action send by an opponent (an offer or an accept)
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81 | elif isinstance(info, ActionDone):
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82 | action: Action = cast(ActionDone, info).getAction()
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83 |
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84 | # if it is an offer, set the last received bid
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85 | if isinstance(action, Offer):
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86 | self._last_received_bid = cast(Offer, action).getBid()
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87 | self._last_received_action = action
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88 | # YourTurn notifies you that it is your turn to act
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89 | elif isinstance(info, YourTurn):
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90 | action = self._myTurn()
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91 | if isinstance(self._progress, ProgressRounds):
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92 | self._progress = self._progress.advance()
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93 | self.getConnection().send(action)
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94 |
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95 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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96 | elif isinstance(info, Finished):
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97 | # terminate the agent MUST BE CALLED
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98 | self.terminate()
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99 | else:
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100 | self.getReporter().log(
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101 | logging.WARNING, "Ignoring unknown info " + str(info)
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102 | )
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103 |
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104 | # lets the geniusweb system know what settings this agent can handle
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105 | # leave it as it is for this competition
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106 | def getCapabilities(self) -> Capabilities:
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107 | return Capabilities(
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108 | set(["SAOP"]),
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109 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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110 | )
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111 |
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112 | # terminates the agent and its connections
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113 | # leave it as it is for this competition
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114 | def terminate(self):
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115 | self.getReporter().log(logging.INFO, "party is terminating:")
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116 | super().terminate()
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117 | if self._profile is not None:
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118 | self._profile.close()
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119 | self._profile = None
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120 |
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121 |
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122 |
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123 | # give a description of your agent
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124 | def getDescription(self) -> str:
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125 | return "BeanBot implementation for Collaborative AI course"
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126 |
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127 | # execute a turn
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128 | def _myTurn(self):
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129 | # Update the frequency model and the issue weights with the last received bid
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130 | self._opp_model = self._opp_model.WithAction(self._last_received_action, self._progress)
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131 | self._opp_model.__class__ = FreqModelWeighted
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132 | self._opp_model.updateIssueWeights()
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133 |
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134 | # Update the best bid offered by the opponent if the last received bid is better for us
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135 | profile = self._profile.getProfile()
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136 | self._opp_best_bid = self._last_received_bid \
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137 | if self._opp_best_bid is None or profile.getUtility(self._last_received_bid) > profile.getUtility(self._opp_best_bid) \
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138 | else self._opp_best_bid
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139 |
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140 | # Update the bids in the window of last received bids (window has size self._window_size)
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141 | if self._last_received_bid is not None:
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142 | self._opp_bids_window.append(profile.getUtility(self._last_received_bid))
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143 | if len(self._opp_bids_window) > self._window_size:
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144 | self._opp_bids_window.pop(0)
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145 |
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146 | bid = self._findBid()
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147 | action = Offer(self._me, bid)
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148 | # AC_combi check whether we should accept current offer or not
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149 | if self._isGood(self._last_received_bid, bid):
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150 | action = Accept(self._me, self._last_received_bid)
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151 |
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152 | # send the action
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153 | return action
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154 |
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155 |
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156 | """
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157 | AC_combi hybrid acceptance strategy.
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158 | Uses AC_next condition in the first [0, T) fraction of the negotiation.
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159 | Additionally accepts some form of best opponent offer in phase [T, 1] of the negotiation (end phase).
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160 | This can be either if the received utility is better than overall best received bid, best bid in window,
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161 | or average utility in window.
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162 | """
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163 | def _isGood(self, offeredBid: Bid, nextBid: Bid, T=0.9) -> bool:
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164 | if offeredBid is None:
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165 | return False
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166 |
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167 | # progress represents fraction of negotiation that has passed
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168 | profile = self._profile.getProfile()
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169 | progress = self._progress.get(time.time() * 1000)
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170 |
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171 | # AC_next or AC_combi if we are in phase [T, 1]
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172 | accept = profile.getUtility(offeredBid) > profile.getUtility(nextBid) \
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173 | or (progress > T and self._window_max())
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174 | return accept
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175 |
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176 | """
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177 | The following three methods represent the three possible AC_combi methods described above.
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178 | """
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179 | def _window_max(self):
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180 | # check if better than maximum utility in past window
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181 | profile = self._profile.getProfile()
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182 | return profile.getUtility(self._last_received_bid) >= np.max(self._opp_bids_window)
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183 |
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184 | def _window_avg(self):
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185 | # check if better than average utility in past window
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186 | profile = self._profile.getProfile()
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187 | return profile.getUtility(self._last_received_bid) >= np.mean(self._opp_bids_window)
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188 |
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189 | def _overall_max(self):
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190 | # check if better than maximum utility received in entire negotiation
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191 | profile = self._profile.getProfile()
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192 | return profile.getUtility(self._last_received_bid) >= profile.getUtility(self._opp_best_bid)
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193 |
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194 | """
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195 | Implements bidding strategy inspired by the AgreeableAgent2018 (ANAC2018).
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196 | Take all bids higher than target utility and pick random one based on opponent preferences to send to opponent.
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197 | """
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198 | def _findBid(self) -> Bid:
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199 | # e value determines concession rate by influencing the shape of the target utility curve
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200 | e = 0.3
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201 | max_util = self._bid_utility_tuple[0][1]
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202 | target_util = self._getUtilityGoal(self._progress.get(time.time() * 1000), e, Decimal(self._rsv_val), max_util)
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203 | # Allow for some additional (10% of target utility) randomness in the possible bids to send to opponent
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204 | # in the first half of the negotiation. Otherwise, will send mostly the same bid constantly at first.
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205 | if self._progress.get(time.time() * 1000) < 0.5:
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206 | target_util = target_util - Decimal(np.random.uniform(0, 0.1 * float(target_util)))
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207 |
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208 | candidates = []
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209 | opp_utilities = []
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210 | # Find all bids above target utility and store along with the associated opponent utilities of the bids
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211 | for items in self._bid_utility_tuple:
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212 | if items[1] < target_util:
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213 | break
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214 | candidates.append(items[0])
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215 | opp_utilities.append(float(self._opp_model.getUtility(items[0])))
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216 |
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217 | # apply roulette wheel selection to the bids to choose one using exponential fitness function
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218 | return self._roulette_selection(candidates, opp_utilities, self._fitness_exp)
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219 |
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220 | """
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221 | Roulette wheel selection to select a random bid to send.
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222 | Scale opponent utilities to [0, 1], apply fitness function to it and use fitness values as probabilities
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223 | for choosing each bid.
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224 | """
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225 | def _roulette_selection(self, candidates, utilities, fitness_func, eps=0.0001):
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226 | normalised_utils = np.array(utilities)
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227 |
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228 | # if same utilities, choose random bid, otherwise continue scaling to [0, 1]
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229 | if np.max(normalised_utils) - np.min(normalised_utils) < eps:
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230 | return np.random.choice(candidates)
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231 |
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232 | # scale utilities to [0, 1]
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233 | normalised_utils = (normalised_utils - np.min(normalised_utils)) / (np.max(normalised_utils) - np.min(normalised_utils))
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234 | # apply fitness function and divide by total sum in order to obtain probability values for each bid
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235 | fitnesses = fitness_func(normalised_utils)
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236 | fitnesses /= np.sum(fitnesses)
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237 | # return random bid using fitnesses as weights, or completely random bid if something went wrong in fitnesses
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238 | return np.random.choice(candidates, p=fitnesses) if not np.isnan(fitnesses).any() else np.random.choice(candidates)
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239 |
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240 | """
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241 | Next two functions allow for two different shapes for the fitness transformation of the utilities
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242 | """
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243 | def _fitness_linear(self, normalised_utils):
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244 | return 0.3 + 0.7 * normalised_utils
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245 |
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246 | def _fitness_exp(self, normalised_utils, alpha=3):
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247 | return np.exp(-alpha*(1-normalised_utils))
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248 |
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249 | """
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250 | Same function as used by time_dependent_agent to determine the curve of utility target line.
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251 | """
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252 | def _getUtilityGoal(
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253 | self, t: float, e: float, minUtil: Decimal, maxUtil: Decimal
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254 | ) -> Decimal:
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255 | """
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256 | @param t the time in [0,1] where 0 means start of nego and 1 the
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257 | end of nego (absolute time/round limit)
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258 | @param e the e value that determinses how fast the party makes
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259 | concessions with time. Typically around 1. 0 means no
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260 | concession, 1 linear concession, >1 faster than linear
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261 | concession.
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262 | @param minUtil the minimum utility possible in our profile
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263 | @param maxUtil the maximum utility possible in our profile
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264 | @return the utility goal for this time and e value
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265 | """
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266 |
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267 | ft1 = Decimal(1)
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268 | if e != 0:
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269 | ft1 = round(Decimal(1 - pow(t, 1 / e)), 6) # defaults ROUND_HALF_UP
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270 | return max(min((minUtil + (maxUtil - minUtil) * ft1), maxUtil), minUtil)
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