[75] | 1 | import logging
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| 2 | import numpy as np
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| 3 | from time import time
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| 4 | from typing import cast
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| 5 | from geniusweb.actions.Accept import Accept
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| 6 | from geniusweb.actions.Action import Action
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| 7 | from geniusweb.actions.Offer import Offer
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| 8 | from geniusweb.actions.PartyId import PartyId
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| 9 | from geniusweb.bidspace.AllBidsList import AllBidsList
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| 10 | from geniusweb.inform.ActionDone import ActionDone
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| 11 | from geniusweb.inform.Finished import Finished
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| 12 | from geniusweb.inform.Inform import Inform
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| 13 | from geniusweb.inform.Settings import Settings
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| 14 | from geniusweb.inform.YourTurn import YourTurn
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| 15 | from geniusweb.issuevalue.Bid import Bid
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| 16 | from geniusweb.issuevalue.Domain import Domain
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| 17 | from geniusweb.party.Capabilities import Capabilities
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| 18 | from geniusweb.party.DefaultParty import DefaultParty
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| 19 | from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import LinearAdditiveUtilitySpace
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| 20 | from geniusweb.profileconnection.ProfileConnectionFactory import ProfileConnectionFactory
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| 21 | from geniusweb.progress.ProgressTime import ProgressTime
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| 22 | from geniusweb.references.Parameters import Parameters
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| 23 | from tudelft_utilities_logging.ReportToLogger import ReportToLogger
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| 24 |
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| 25 | class agentBidHistory:
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| 26 | def __init__(self):
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| 27 | self.bidHistory = []
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| 28 |
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| 29 | def addBid(self, bid, label):
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| 30 | self.bidHistory.append((bid, label))
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| 31 |
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| 32 | class Agent007(DefaultParty):
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| 33 | """Agent007"""
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| 34 | def __init__(self):
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| 35 | super().__init__()
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| 36 | self._profileint: LinearAdditiveUtilitySpace = None
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| 37 | self.lastOfferedBid = None
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| 38 | self.logger: ReportToLogger = self.getReporter()
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| 39 | self.logger.log(logging.INFO, "party is initialized")
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| 40 | self.me: PartyId = None
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| 41 | self.progress: ProgressTime = None
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| 42 | self.settings: Settings = None
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| 43 | self.domain: Domain = None
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| 44 | self.parameters: Parameters = None
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| 45 | self.other: str = None
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| 46 | self.storage_dir: str = None
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| 47 | self.bidHistory = None
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| 48 |
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| 49 | def notifyChange(self, data: Inform):
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| 50 | """ Arg: info (Inform): Contains either a request for action or information. """
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| 51 | if isinstance(data, Settings):
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| 52 | self.settings = cast(Settings, data)
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| 53 | self.me = self.settings.getID()
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| 54 | self.progress = self.settings.getProgress()
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| 55 | self._profileint = ProfileConnectionFactory.create( # the profile contains the preferences of the agent over the domain
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| 56 | data.getProfile().getURI(), self.getReporter()
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| 57 | )
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| 58 | self.parameters = self.settings.getParameters()
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| 59 | self.storage_dir = self.parameters.get("storage_dir")
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| 60 | self.domain = self._profileint.getProfile().getDomain()
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| 61 | self._profileint.close()
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| 62 | self.rejected_bids = []
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| 63 | self.bidHistory = agentBidHistory()
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| 64 | self.issues = [issue for issue in sorted(self.domain.getIssues())]
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| 65 | self.num_values_in_issue = [self.domain.getValues(issue).size() for issue in self.issues]
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| 66 | self.bid_dict = self.bid_decode()
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| 67 |
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| 68 | elif isinstance(data, ActionDone): # if opponent answered (reject or accept)
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| 69 | action: Action = data.getAction()
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| 70 | if isinstance(action, Offer): # [1] if opponent respond by reject our offer + proposed his offer
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| 71 | if self.lastOfferedBid: # if we have already proposed an offer before
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| 72 | self.rejected_bids.append(self.lastOfferedBid)
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| 73 | self.bidHistory.addBid(self.bid_encode(self.lastOfferedBid), 0) # opponent rejected our offer (negative label)
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| 74 | actor = action.getActor()
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| 75 | self.other = str(actor).rsplit("_", 1)[0] # obtain the name of the opponent, cutting of the position ID.
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| 76 | self.lastOfferedBid = cast(Offer, action).getBid()
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| 77 | self.bidHistory.addBid(self.bid_encode(self.lastOfferedBid), 1) # opponent offer (positive label)
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| 78 | else: # if [2] opponent accepted our offer
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| 79 | self.bidHistory.addBid(self.bid_encode(self.lastOfferedBid), 1) # opponent accepted our offer (positive label)
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| 80 | elif isinstance(data, YourTurn): # [3] YourTurn notifies you that it is your turn to act
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| 81 | action = self.chooseAction()
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| 82 | self.send_action(action)
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| 83 | elif isinstance(data, Finished): # [2] Finished will be send if the negotiation has ended (through agreement or deadline)
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| 84 | self.save_data()
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| 85 | self.logger.log(logging.INFO, "party is terminating:")
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| 86 | super().terminate() # terminate the agent MUST BE CALLED
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| 87 | else:
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| 88 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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| 89 |
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| 90 | def send_action(self, action: Action):
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| 91 | """Sends an action to the opponent(s) """
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| 92 | self.getConnection().send(action)
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| 93 |
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| 94 | def getCapabilities(self) -> Capabilities:
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| 95 | return Capabilities(set(["SAOP"]),set(["geniusweb.profile.utilityspace.LinearAdditive"]))
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| 96 |
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| 97 | def getDescription(self) -> str:
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| 98 | return "Agent007 for the ANL 2022 competition"
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| 99 |
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| 100 | def save_data(self):
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| 101 | """This method is called after the negotiation is finished. It can be used to store data
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| 102 | for learning capabilities. Note that no extensive calculations can be done within this method.
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| 103 | Taking too much time might result in your agent being killed, so use it for storage only.
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| 104 | """
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| 105 | data = "Data for learning (see README.md)"
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| 106 | with open(f"{self.storage_dir}/data.md", "w") as f:
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| 107 | f.write(data)
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| 108 |
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| 109 | def bid_decode(self):
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| 110 | ''' perform decoding on the bid'''
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| 111 | bid_dict = {}
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| 112 | for bid in AllBidsList(self.domain):
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| 113 | bid_vals = tuple(self.domain.getValues(issue).getValues().index(bid.getValue(issue)) for issue in self.issues)
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| 114 | bid_dict[bid_vals] = bid
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| 115 | return bid_dict
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| 116 |
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| 117 | def bid_encode(self, bid: Bid):
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| 118 | ''' perform One Hot Encoding on the bid'''
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| 119 | bid_vals = [self.domain.getValues(issue).getValues().index(bid.getValue(issue)) for issue in self.issues]
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| 120 | total_num_values = sum(self.num_values_in_issue)
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| 121 | ohe_vec = np.zeros(1+total_num_values) # added 1 for bias
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| 122 | ohe_vec[0] = 1.0 # the bias term
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| 123 | start = 1
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| 124 | for i in range(len(self.num_values_in_issue)):
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| 125 | ohe_vec[start + bid_vals[i]] = 1.0
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| 126 | start += self.num_values_in_issue[i]
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| 127 | return ohe_vec
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| 128 |
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| 129 | def chooseAction(self):
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| 130 | ''' Choose if to accept the last offer or make a new offer
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| 131 | @return The chosen action
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| 132 | '''
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| 133 | progress = self.progress.get(time() * 1000)
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| 134 | if self.shouldAccept():
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| 135 | action = Accept(self.me, self.lastOfferedBid)
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| 136 | elif progress > 0.7: # if we have enough data
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| 137 | nextBid = self.get_bid()
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| 138 | self.lastOfferedBid = nextBid
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| 139 | action = Offer(self.me, nextBid)
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| 140 | else:
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| 141 | nextBid = self.findNextBid()
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| 142 | self.lastOfferedBid = nextBid
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| 143 | action = Offer(self.me, nextBid)
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| 144 | return action
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| 145 |
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| 146 | def shouldAccept(self):
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| 147 | '''
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| 148 | @return Whether to accept the last bid or offer the nextBid
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| 149 | '''
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| 150 | progress = self.progress.get(time() * 1000)
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| 151 | if self.lastOfferedBid == None:
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| 152 | return False
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| 153 | if progress > 0.97:
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| 154 | return True
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| 155 | if progress > 0.9 and self._profileint.getProfile().getUtility(self.lastOfferedBid) > 0.5:
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| 156 | return True
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| 157 | if progress > 0.8 and self._profileint.getProfile().getUtility(self.lastOfferedBid) > 0.6:
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| 158 | return True
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| 159 | return False
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| 160 |
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| 161 | def get_bid(self):
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| 162 | issue_pos = [1]+[sum(self.num_values_in_issue[:i])+1 for i in range(1, len(self.num_values_in_issue))]
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| 163 | profile = self._profileint.getProfile()
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| 164 | issue_weight = [float(profile.getWeights()[issue]) for issue in profile.getWeights()]
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| 165 | utilities = [profile.getUtilities()[issue] for issue in profile.getUtilities()]
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| 166 | issues_values = [[float(v) for v in util.getUtilities().values()] for util in utilities]
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| 167 |
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| 168 | total_num_values = sum(self.num_values_in_issue)
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| 169 | offered = np.zeros(1+total_num_values) # added 1 for bias
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| 170 | for bid in self.bidHistory.bidHistory:
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| 171 | if bid[1] == 1:
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| 172 | offered = np.add(offered, bid[0])
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| 173 |
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| 174 | issues_offered = [offered[v_pos: v_pos+v_len] for (v_pos, v_len) in zip(issue_pos, self.num_values_in_issue)]
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| 175 | vec = []
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| 176 | for i in range(len(self.issues)):
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| 177 | avg = sum(issue_weight) / len(issue_weight)
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| 178 | weight_ = issue_weight[i]/avg
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| 179 | avg = sum(issues_offered[i]) / len(issues_offered[i])
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| 180 | issues_offered_ = [issue_offered/avg for issue_offered in issues_offered[i]]
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| 181 | avg = (sum(issues_values[i])/len(issues_values[i]))
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| 182 | issues_values_ = [issue_value/avg for issue_value in issues_values[i]]
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| 183 | candidates = [(j,offer,val) for (j,offer,val) in zip(range(len(issues_offered_)), issues_offered_, issues_values_) if (offer >= 1 and val >= 1)]
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| 184 | if len(candidates) == 0:
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| 185 | if weight_ >= 1:
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| 186 | value_id = np.argmax(issues_values_) # select best for my agent
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| 187 | else:
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| 188 | value_id = np.argmax(issues_offered_) # select best for opponent
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| 189 | elif len(candidates) == 1:
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| 190 | value_id = candidates[0][0] # select best for both my agent and opponent
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| 191 | else:
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| 192 | values_ids, offers, values = zip(*candidates)
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| 193 | if weight_ >= 1:
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| 194 | id = np.argmax(values) # select best for my agent
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| 195 | else:
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| 196 | id = np.argmax(offers) # select best for opponent
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| 197 | value_id = values_ids[id]
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| 198 | vec.append(value_id)
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| 199 | bid = self.bid_dict[tuple(vec)]
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| 200 | return bid
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| 201 |
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| 202 | def findNextBid(self):
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| 203 | '''
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| 204 | @return The next bid to offer
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| 205 | '''
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| 206 | all_bids = AllBidsList(self.domain)
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| 207 | bestBidEvaluation = 0
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| 208 | nextBid = None
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| 209 | for _ in range(500):
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| 210 | domain_size = all_bids.size()
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| 211 | id = np.random.randint(domain_size)
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| 212 | bid = all_bids.get(id)
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| 213 | bid_utility = float(self._profileint.getProfile().getUtility(bid))
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| 214 | if bid_utility >= bestBidEvaluation:
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| 215 | nextBid = bid
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| 216 | bestBidEvaluation = bid_utility
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| 217 | return nextBid
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