| 1 | import logging
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| 2 | import numpy as np
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| 3 | from pandas import array
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| 4 | from random import randint
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| 5 | from sklearn.linear_model import LinearRegression
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| 6 | from sklearn.ensemble import RandomForestRegressor
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| 7 | from sklearn.ensemble import VotingRegressor
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| 8 | from sklearn.neighbors import KNeighborsRegressor
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| 9 | from time import time
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| 10 | from typing import cast
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| 11 | import random
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| 12 | from geniusweb.actions.Accept import Accept
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| 13 | from geniusweb.actions.Action import Action
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| 14 | from geniusweb.actions.Offer import Offer
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| 15 | from geniusweb.actions.PartyId import PartyId
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| 16 | from geniusweb.bidspace.AllBidsList import AllBidsList
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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 tudelft_utilities_logging.ReportToLogger import ReportToLogger
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| 35 |
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| 36 | from agents.template_agent.utils.opponent_model import OpponentModel
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| 37 |
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| 38 |
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| 39 | class BIU_agent(DefaultParty):
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| 40 | """
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| 41 | BIU_agent of a Python geniusweb agent.
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| 42 | """
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| 43 | def __init__(self):
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| 44 | super().__init__()
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| 45 | self.logger: ReportToLogger = self.getReporter()
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| 46 |
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| 47 | self.domain: Domain = None
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| 48 | self.parameters: Parameters = None
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| 49 | self.profile: LinearAdditiveUtilitySpace = None
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| 50 | self.progress: ProgressTime = None
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| 51 | self.me: PartyId = None
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| 52 | self.other: str = None
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| 53 | self.settings: Settings = None
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| 54 | self.storage_dir: str = None
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| 55 |
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| 56 | self.last_received_bid: Bid = None
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| 57 | self.opponent_model: OpponentModel = None
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| 58 | self.logger.log(logging.INFO, "party is initialized")
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| 59 |
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| 60 | self.bids_given: list = None
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| 61 | self.bids_received: list = None
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| 62 | self.proposal_time: float = None
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| 63 | self.opponent_bid_times: list = None
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| 64 |
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| 65 | def notifyChange(self, data: Inform):
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| 66 | """MUST BE IMPLEMENTED
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| 67 | This is the entry point of all interaction with your agent after is has been initialised.
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| 68 | How to handle the received data is based on its class type.
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| 69 |
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| 70 | Args:
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| 71 | info (Inform): Contains either a request for action or information.
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| 72 | """
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| 73 |
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| 74 | # a Settings message is the first message that will be send to your
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| 75 | # agent containing all the information about the negotiation session.
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| 76 | if isinstance(data, Settings):
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| 77 | self.settings = cast(Settings, data)
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| 78 | self.me = self.settings.getID()
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| 79 |
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| 80 | # progress towards the deadline has to be tracked manually through the use of the Progress object
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| 81 | self.progress = self.settings.getProgress()
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| 82 |
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| 83 | self.parameters = self.settings.getParameters()
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| 84 | self.storage_dir = self.parameters.get("storage_dir")
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| 85 |
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| 86 | # the profile contains the preferences of the agent over the domain
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| 87 | profile_connection = ProfileConnectionFactory.create(
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| 88 | data.getProfile().getURI(), self.getReporter()
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| 89 | )
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| 90 | self.profile = profile_connection.getProfile()
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| 91 | self.domain = self.profile.getDomain()
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| 92 | profile_connection.close()
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| 93 |
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| 94 | self.opponent_bid_times = []
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| 95 |
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| 96 | # ActionDone informs you of an action (an offer or an accept)
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| 97 | # that is performed by one of the agents (including yourself).
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| 98 | elif isinstance(data, ActionDone):
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| 99 | action = cast(ActionDone, data).getAction()
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| 100 | actor = action.getActor()
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| 101 |
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| 102 | # ignore action if it is our action
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| 103 | if actor != self.me:
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| 104 | # obtain the name of the opponent, cutting of the position ID.
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| 105 | self.other = str(actor).rsplit("_", 1)[0]
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| 106 |
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| 107 | # process action done by opponent
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| 108 | self.opponent_action(action)
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| 109 | # YourTurn notifies you that it is your turn to act
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| 110 | elif isinstance(data, YourTurn):
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| 111 | # execute a turn
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| 112 | if self.proposal_time is not None:
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| 113 | self.opponent_bid_times.append(self.progress.get(time() * 1000) - self.proposal_time)
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| 114 | self.my_turn()
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| 115 | self.proposal_time = self.progress.get(time() * 1000)
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| 116 |
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| 117 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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| 118 | elif isinstance(data, Finished):
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| 119 | self.save_data()
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| 120 | # terminate the agent MUST BE CALLED
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| 121 | self.logger.log(logging.INFO, "party is terminating:")
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| 122 | super().terminate()
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| 123 | else:
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| 124 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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| 125 |
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| 126 | def getCapabilities(self) -> Capabilities:
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| 127 | """MUST BE IMPLEMENTED
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| 128 | Method to indicate to the protocol what the capabilities of this agent are.
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| 129 | Leave it as is for the ANL 2022 competition
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| 130 |
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| 131 | Returns:
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| 132 | Capabilities: Capabilities representation class
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| 133 | """
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| 134 | return Capabilities(
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| 135 | set(["SAOP"]),
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| 136 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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| 137 | )
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| 138 |
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| 139 | def send_action(self, action: Action):
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| 140 | """Sends an action to the opponent(s)
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| 141 |
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| 142 | Args:
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| 143 | action (Action): action of this agent
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| 144 | """
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| 145 | self.getConnection().send(action)
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| 146 |
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| 147 | # give a description of your agent
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| 148 | def getDescription(self) -> str:
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| 149 | """MUST BE IMPLEMENTED
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| 150 | Returns a description of your agent. 1 or 2 sentences.
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| 151 |
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| 152 | Returns:
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| 153 | str: Agent description
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| 154 | """
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| 155 | return "This is a Bar Ilan University agent that learns from the opponent's bids, by using a random forest, a linear regression and a KNN. The agent also using random stochastic to take the offers."
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| 156 |
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| 157 | def opponent_action(self, action):
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| 158 | """Process an action that was received from the opponent.
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| 159 |
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| 160 | Args:
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| 161 | action (Action): action of opponent
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| 162 | """
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| 163 | # if it is an offer, set the last received bid
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| 164 | if isinstance(action, Offer):
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| 165 | # create opponent model if it was not yet initialised
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| 166 | if self.opponent_model is None:
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| 167 | self.opponent_model = OpponentModel(self.domain)
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| 168 |
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| 169 | bid = cast(Offer, action).getBid()
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| 170 |
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| 171 | # update opponent model with bid
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| 172 | self.opponent_model.update(bid)
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| 173 | # set bid as last received
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| 174 | self.last_received_bid = bid
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| 175 |
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| 176 | def my_turn(self):
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| 177 | """This method is called when it is our turn. It should decide upon an action
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| 178 | to perform and send this action to the opponent.
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| 179 | """
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| 180 | if self.accept_condition(self.last_received_bid):
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| 181 | action = Accept(self.me, self.last_received_bid)
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| 182 | else:
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| 183 | t = self.progress.get(time() * 1000)
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| 184 | self.logger.log(logging.INFO, t)
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| 185 | bid = self.find_bid()
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| 186 | if t >= 0.95:
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| 187 | t_o = self.regression_opponent_time(self.opponent_bid_times[-10:])
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| 188 | self.logger.log(logging.INFO, self.opponent_bid_times)
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| 189 | self.logger.log(logging.INFO, t_o)
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| 190 | while all(t < 1 - t_o):
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| 191 | t = self.progress.get(time() * 1000)
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| 192 | action = Offer(self.me, bid)
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| 193 |
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| 194 | self.send_action(action)
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| 195 |
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| 196 | def save_data(self):
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| 197 | """This method is called after the negotiation is finished. It can be used to store data
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| 198 | for learning capabilities. Note that no extensive calculations can be done within this method.
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| 199 | Taking too much time might result in your agent being killed, so use it for storage only.
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| 200 | """
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| 201 | data = " ".join(str(x) for x in self.opponent_bid_times)
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| 202 | # self_dir = "./agents/BIU_agent/data.md"
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| 203 | with open(f"{self.storage_dir}/data.md", "w") as f:
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| 204 | f.write(data)
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| 205 |
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| 206 | ###########################################################################################
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| 207 | ################################## Example methods below ##################################
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| 208 | ###########################################################################################
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| 209 |
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| 210 | def accept_condition(self, bid: Bid) -> bool:
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| 211 | if bid is None:
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| 212 | return False
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| 213 |
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| 214 | # progress of the negotiation session between 0 and 1 (1 is deadline)
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| 215 | progress = self.progress.get(time() * 1000)
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| 216 |
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| 217 | # very basic approach that accepts if the offer is valued above 0.7 and
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| 218 | # 95% of the time towards the deadline has passed
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| 219 | threshold = 0.9
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| 220 | if 0 < progress < 0.2:
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| 221 | threshold = 0.9
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| 222 | if 0.2 < progress <0.3:
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| 223 | threshold = 0.8
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| 224 | elif 0.3 < progress < 0.5:
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| 225 | threshold = 0.6
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| 226 | elif 0.5 < progress < 0.9:
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| 227 | threshold = 0.5
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| 228 | elif 0.9 < progress < 1:
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| 229 | threshold = 0.25
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| 230 |
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| 231 | conditions = [
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| 232 | self.profile.getUtility(bid) > 0.8
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| 233 | ]
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| 234 | return all(conditions)
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| 235 |
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| 236 | def find_bid(self) -> Bid:
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| 237 | # compose a list of all possible bids
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| 238 | domain = self.profile.getDomain()
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| 239 | all_bids = AllBidsList(domain)
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| 240 |
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| 241 | best_bid_score = 0.0
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| 242 | best_bid = None
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| 243 |
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| 244 | # take 500 attempts to find a bid according to a heuristic score
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| 245 | for _ in range(500):
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| 246 | bid = all_bids.get(randint(0, all_bids.size() - 1))
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| 247 | bid_score = self.score_bid(bid)
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| 248 | if bid_score > best_bid_score:
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| 249 | best_bid_score, best_bid = bid_score, bid
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| 250 |
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| 251 | return best_bid
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| 252 |
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| 253 | def score_bid(self, bid: Bid, alpha: float = 0.95, eps: float = 0.5) -> float:
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| 254 | """Calculate heuristic score for a bid
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| 255 |
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| 256 | Args:
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| 257 | bid (Bid): Bid to score
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| 258 | alpha (float, optional): Trade-off factor between self interested and
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| 259 | altruistic behaviour. Defaults to 0.95.
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| 260 | eps (float, optional): Time pressure factor, balances between conceding
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| 261 | and Boulware behaviour over time. Defaults to 0.1.
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| 262 |
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| 263 | Returns:
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| 264 | float: score
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| 265 | """
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| 266 |
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| 267 | # progress = self.progress.get(time() * 1000)
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| 268 |
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| 269 | # our_utility = float(self.profile.getUtility(bid))
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| 270 |
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| 271 | # time_pressure = 1.0 - progress ** (1 / eps)
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| 272 | # score = alpha * time_pressure * our_utility
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| 273 |
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| 274 | # if self.opponent_model is not None:
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| 275 | # opponent_utility = self.opponent_model.get_predicted_utility(bid)
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| 276 | # opponent_score = (1.0 - alpha * time_pressure) * opponent_utility
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| 277 | # score += opponent_score
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| 278 |
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| 279 | # return our_utility
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| 280 | stochastic_alpha = 0
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| 281 | stochastic_eps = 0
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| 282 | STOCHASTIC_TRANSITION = random.randint(0,9)
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| 283 | if 0 < STOCHASTIC_TRANSITION < 9: # alpha stay the same
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| 284 | stochastic_alpha = alpha
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| 285 | elif STOCHASTIC_TRANSITION == 0:
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| 286 | stochastic_alpha = alpha - eps
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| 287 | stochastic_eps = 0.005
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| 288 | else: # STOCHASTIC_TRANSITION = 9
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| 289 | stochastic_alpha = alpha + eps
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| 290 | stochastic_eps = -0.005
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| 291 |
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| 292 | progress = self.progress.get(time() * 1000)
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| 293 |
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| 294 | utility = float(self.profile.getUtility(bid))
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| 295 |
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| 296 | time_pressure = 1.0 - progress ** (1 / eps)
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| 297 | score = stochastic_alpha * time_pressure * utility
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| 298 |
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| 299 | if self.opponent_model is not None:
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| 300 | opponent_utility = self.opponent_model.get_predicted_utility(bid)
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| 301 | opponent_score = (1.0 - stochastic_alpha * time_pressure) * opponent_utility
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| 302 | score += opponent_score
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| 303 | if utility > 0.994 and stochastic_eps > 0:
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| 304 | stochastic_eps = 0
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| 305 | if utility < 0.005 and stochastic_eps < 0:
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| 306 | stochastic_eps = 0
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| 307 | final_score = utility + stochastic_eps
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| 308 | return final_score
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| 309 |
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| 310 |
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| 311 |
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| 312 | def regression_opponent_time(self, bid_times):
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| 313 | r1 = LinearRegression()
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| 314 | r2 = RandomForestRegressor(n_estimators=10, random_state=1)
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| 315 | r3 = KNeighborsRegressor()
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| 316 | X = array(range(len(bid_times))).reshape(-1, 1)
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| 317 | y = array(bid_times).reshape(-1, 1)
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| 318 | er = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)])
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| 319 | return er.fit(X, y).predict(X) |
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