[75] | 1 | import logging
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| 2 | import numpy as np
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| 3 |
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| 4 | from random import randint
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| 5 | from time import time
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| 6 | from typing import cast
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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.actions.PartyId import PartyId
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| 12 | from geniusweb.bidspace.AllBidsList import AllBidsList
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| 13 | from geniusweb.inform.ActionDone import ActionDone
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| 14 | from geniusweb.inform.Finished import Finished
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| 15 | from geniusweb.inform.Inform import Inform
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| 16 | from geniusweb.inform.Settings import Settings
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| 17 | from geniusweb.inform.YourTurn import YourTurn
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| 18 | from geniusweb.issuevalue.Bid import Bid
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| 19 | from geniusweb.issuevalue.Domain import Domain
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| 20 | from geniusweb.party.Capabilities import Capabilities
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| 21 | from geniusweb.party.DefaultParty import DefaultParty
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| 22 | from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import (
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| 23 | LinearAdditiveUtilitySpace,
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| 24 | )
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| 25 | from geniusweb.profileconnection.ProfileConnectionFactory import (
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| 26 | ProfileConnectionFactory,
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| 27 | )
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| 28 | from geniusweb.progress.ProgressTime import ProgressTime
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| 29 | from geniusweb.references.Parameters import Parameters
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| 30 | from tudelft_utilities_logging.ReportToLogger import ReportToLogger
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| 31 |
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| 32 | from agents.template_agent.utils.opponent_model import OpponentModel
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| 33 |
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| 34 |
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| 35 | class RGAgent(DefaultParty):
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| 36 | """
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| 37 | @brief: Raviv-Gavriely negotiation agent of a Python GeniusWeb agent.
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| 38 | """
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| 39 |
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| 40 | def __init__(self) -> None:
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| 41 | super().__init__()
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| 42 | self.logger: ReportToLogger = self.getReporter()
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| 43 |
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| 44 | self.domain: Domain = None
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| 45 | self.parameters: Parameters = None
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| 46 | self.profile: LinearAdditiveUtilitySpace = None
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| 47 | self.progress: ProgressTime = None
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| 48 | self.me: PartyId = None
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| 49 | self.other: str = None
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| 50 | self.settings: Settings = None
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| 51 | self.storage_dir: str = None
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| 52 |
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| 53 | self.last_received_bid: Bid = None
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| 54 | self.opponent_model: OpponentModel = None
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| 55 | self.logger.log(logging.INFO, "party is initialized")
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| 56 |
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| 57 | # Our parameters:
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| 58 | self.max_acceptance_threshold = 0.9 # From optimal
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| 59 | self.min_acceptance_threshold = 0.5 # From optimal
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| 60 | self.compromising_factor = 4 # Higher value means compromise later
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| 61 | self.bids_to_consider = 800
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| 62 | self.optimal_bid = None
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| 63 | self.best_opponent_bid = None
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| 64 | self.all_previous_bids = []
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| 65 |
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| 66 | def notifyChange(self, data: Inform) -> None:
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| 67 | """
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| 68 | @brief: Notify on a change.
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| 69 |
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| 70 | This is the entry point of all interaction with your agent after is has been initialized.
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| 71 | How to handle the received data is based on its class type.
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| 72 |
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| 73 | @param info: Contains either a request for action or information.
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| 74 |
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| 75 | @return: None.
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| 76 | """
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| 77 |
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| 78 | # A Settings message is the first message that will be send to your
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| 79 | # agent containing all the information about the negotiation session.
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| 80 | if isinstance(data, Settings):
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| 81 | self.settings = cast(Settings, data)
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| 82 | self.me = self.settings.getID()
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| 83 |
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| 84 | # Progress towards the deadline has to be tracked manually through the use of the Progress object
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| 85 | self.progress = self.settings.getProgress()
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| 86 |
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| 87 | self.parameters = self.settings.getParameters()
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| 88 | self.storage_dir = self.parameters.get("storage_dir")
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| 89 |
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| 90 | # The profile contains the preferences of the agent over the domain
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| 91 | profile_connection = ProfileConnectionFactory.create(
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| 92 | data.getProfile().getURI(), self.getReporter()
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| 93 | )
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| 94 | self.profile = profile_connection.getProfile()
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| 95 | self.domain = self.profile.getDomain()
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| 96 | # Calculate best bid and threshold:
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| 97 | all_bids = AllBidsList(self.profile.getDomain())
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| 98 | optimal_bid = (None, 0)
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| 99 | for current_bid in all_bids:
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| 100 | current_bid_utility = self.profile.getUtility(current_bid)
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| 101 | if current_bid_utility > optimal_bid[1]:
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| 102 | optimal_bid = (current_bid, current_bid_utility)
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| 103 | self.optimal_bid = optimal_bid[0]
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| 104 | self.max_acceptance_threshold *= float(optimal_bid[1])
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| 105 | self.min_acceptance_threshold *= float(optimal_bid[1])
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| 106 |
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| 107 | profile_connection.close()
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| 108 |
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| 109 | # ActionDone informs you of an action (an offer or an accept)
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| 110 | # that is performed by one of the agents (including yourself).
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| 111 | elif isinstance(data, ActionDone):
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| 112 | action = cast(ActionDone, data).getAction()
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| 113 | actor = action.getActor()
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| 114 |
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| 115 | # Ignore action if it is our action
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| 116 | if actor != self.me:
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| 117 | # Obtain the name of the opponent, cutting of the position ID.
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| 118 | self.other = str(actor).rsplit("_", 1)[0]
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| 119 |
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| 120 | # Process action done by opponent
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| 121 | self.opponent_action(action)
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| 122 | # YourTurn notifies you that it is your turn to act
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| 123 | elif isinstance(data, YourTurn):
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| 124 | # Execute a turn
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| 125 | self.my_turn()
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| 126 |
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| 127 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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| 128 | elif isinstance(data, Finished):
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| 129 | self.save_data()
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| 130 | # terminate the agent MUST BE CALLED
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| 131 | self.logger.log(logging.INFO, "party is terminating:")
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| 132 | super().terminate()
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| 133 | else:
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| 134 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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| 135 |
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| 136 | def getCapabilities(self) -> Capabilities:
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| 137 | """
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| 138 | @brief: Returns the capability of the agent.
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| 139 |
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| 140 | Method to indicate to the protocol what the capabilities of this agent are.
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| 141 |
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| 142 | @return: Capabilities representation class.
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| 143 | """
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| 144 | return Capabilities(
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| 145 | set(["SAOP"]),
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| 146 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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| 147 | )
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| 148 |
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| 149 | def send_action(self, action: Action) -> None:
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| 150 | """
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| 151 | @brief: Sends an action to the opponent(s).
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| 152 |
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| 153 | @param action: action of this agent
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| 154 |
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| 155 | @return: None.
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| 156 | """
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| 157 | self.getConnection().send(action)
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| 158 |
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| 159 | def getDescription(self) -> str:
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| 160 | """
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| 161 | @brief: Returns a description of your agent.
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| 162 |
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| 163 | @return: Agent description.
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| 164 | """
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| 165 | return "Raviv-Gavriely description for the ANL 2022 competition"
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| 166 |
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| 167 | def opponent_action(self, action: Action) -> None:
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| 168 | """
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| 169 | @brief: Process an action that was received from the opponent.
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| 170 |
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| 171 | @param: Action of opponent.
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| 172 |
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| 173 | @return: None.
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| 174 | """
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| 175 | # If it is an offer, set the last received bid:
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| 176 | if isinstance(action, Offer):
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| 177 | # Create opponent model if it was not yet initialized
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| 178 | if self.opponent_model is None:
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| 179 | self.opponent_model = OpponentModel(self.domain)
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| 180 |
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| 181 | bid = cast(Offer, action).getBid()
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| 182 |
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| 183 | # Update opponent model with bid
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| 184 | self.opponent_model.update(bid)
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| 185 | # Set bid as last received
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| 186 | self.last_received_bid = bid
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| 187 | # Keep track of all the bids and which bid is the best
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| 188 | self.all_previous_bids.append(bid)
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| 189 | if self.best_opponent_bid is None:
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| 190 | self.best_opponent_bid = bid
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| 191 | if self.profile.getUtility(bid) > self.profile.getUtility(self.best_opponent_bid):
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| 192 | self.best_opponent_bid = bid
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| 193 |
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| 194 | def my_turn(self) -> None:
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| 195 | """
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| 196 | @brief: My turn.
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| 197 |
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| 198 | This method is called when it is our turn. It should decide upon an action
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| 199 | to perform and send this action to the opponent.
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| 200 |
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| 201 | @return: None.
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| 202 | """
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| 203 | # Check if the last received offer is good enough
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| 204 | if self.accept_condition(self.last_received_bid):
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| 205 | # If so, accept the offer
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| 206 | action = Accept(self.me, self.last_received_bid)
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| 207 | else:
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| 208 | # If not, find a bid to propose as counter offer
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| 209 | bid = self.find_bid()
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| 210 | action = Offer(self.me, bid)
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| 211 |
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| 212 | # Send the action
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| 213 | self.send_action(action)
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| 214 |
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| 215 | def save_data(self) -> None:
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| 216 | """
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| 217 | @brief: Saves data.
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| 218 |
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| 219 | This method is called after the negotiation is finished. It can be used to store data
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| 220 | for learning capabilities. Note that no extensive calculations can be done within this method.
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| 221 | Taking too much time might result in your agent being killed, so use it for storage only.
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| 222 |
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| 223 | @return: None.
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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 | def accept_condition(self, bid: Bid) -> bool:
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| 230 | """
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| 231 | @brief: Accept the given bid.
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| 232 |
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| 233 | @return: Boolean indicator if to accept/reject the bid.
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| 234 | """
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| 235 | if bid is None:
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| 236 | return False
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| 237 |
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| 238 | # Progress of the negotiation session between 0 and 1 (1 is deadline)
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| 239 | progress = self.progress.get(time() * 1000)
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| 240 |
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| 241 | acceptance_threshold = -np.exp(self.compromising_factor * progress)
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| 242 | acceptance_threshold /= np.exp(self.compromising_factor) - 1 # scale to 1
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| 243 | acceptance_threshold *= self.max_acceptance_threshold - self.min_acceptance_threshold
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| 244 | acceptance_threshold += self.max_acceptance_threshold
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| 245 |
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| 246 | return self.profile.getUtility(bid) >= acceptance_threshold
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| 247 |
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| 248 | def find_bid(self) -> Bid:
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| 249 | """
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| 250 | @brief: Finds the bid.
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| 251 |
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| 252 | @return: The chosen bid.
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| 253 | """
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| 254 | # Compose a list of all possible bids
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| 255 | domain = self.profile.getDomain()
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| 256 | all_bids = AllBidsList(domain)
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| 257 |
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| 258 | best_bid_score = 0.0
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| 259 | best_bid = None
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| 260 |
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| 261 | # Take X attempts to find a bid according to a heuristic score
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| 262 | for _ in range(self.bids_to_consider):
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| 263 | bid = all_bids.get(randint(0, all_bids.size() - 1))
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| 264 | bid_score = self.score_bid(bid)
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| 265 | if bid_score > best_bid_score:
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| 266 | best_bid_score, best_bid = bid_score, bid
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| 267 | if self.accept_condition(best_bid):
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| 268 | return best_bid
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| 269 | else:
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| 270 | return self.optimal_bid
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| 271 |
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| 272 | def score_bid(self, bid: Bid, alpha: float = 0.95, eps: float = 0.1) -> float:
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| 273 | """
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| 274 | @brief: Calculate heuristic score for a bid.
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| 275 |
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| 276 | @param bid: Bid to score
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| 277 | @param alpha: Trade-off factor between self interested and
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| 278 | altruistic behavior. Defaults to 0.95.
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| 279 | @param eps: Time pressure factor, balances between conceding
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| 280 | and Boulware behavior over time. Defaults to 0.1.
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| 281 |
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| 282 | Returns:
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| 283 | float: score
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| 284 | """
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| 285 | progress = self.progress.get(time() * 1000)
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| 286 |
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| 287 | our_utility = float(self.profile.getUtility(bid))
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| 288 |
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| 289 | time_pressure = 1.0 - progress ** (1 / eps)
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| 290 | score = alpha * time_pressure * our_utility
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| 291 |
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| 292 | if self.opponent_model is not None:
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| 293 | opponent_utility = self.opponent_model.get_predicted_utility(bid)
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| 294 | opponent_score = (1.0 - alpha * time_pressure) * opponent_utility
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| 295 | score += opponent_score
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| 296 |
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| 297 | return score
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