AI Player Logic Module

This module defines the logic and strategies for Artificial Intelligence (AI) players within the Property Tycoon game. It provides different AI personalities or difficulty levels, encapsulating the decision-making processes for various game situations like buying properties, bidding in auctions, managing assets, and handling jail time.

The AI Player Logic module provides:

EasyAIPlayer: A baseline AI implementing straightforward decision rules based on predefined strategy parameters (e.g., bid multipliers, development thresholds).
HardAIPlayer: A more complex AI that builds upon the Easy AI logic but introduces a ‘mood’ or ‘emotion’ system. This mood modifier influences decisions, making the AI more aggressive or conservative depending on hunman player’s selection.
Methods for evaluating property value from the AI’s perspective.
Logic for deciding whether to buy properties, bid in auctions, develop properties, mortgage/unmortgage assets, and handle jail situations.
Helper functions for analyzing property groups and ownership.

class src.Ai_Player_Logic.EasyAIPlayer(difficulty='easy')[source]

Bases: object

can_build_house(property, color_group_properties)[source]

check_group_ownership(color_group, board_properties, player_name)[source]

consider_trade_offer(ai_player, offered_properties, requested_properties, cash_difference, board_properties)[source]

get_auction_bid(current_minimum, property_data, ai_player, board_properties)[source]

get_development_priority(properties)[source]

get_group_properties(color_group, board_properties)[source]

get_location_type(position, board_properties)[source]

get_property_name(position, board_properties)[source]

get_property_value(property_data, owned_properties, total_money)[source]

handle_ai_turn(ai_player, board_properties)[source]

handle_emergency_cash(ai_player, required_amount, board_properties)[source]

handle_jail_strategy(ai_player, jail_free_cards)[source]

handle_property_development(ai_player, board_properties)[source]

handle_turn(ai_player, current_location, board_properties, is_first_round=False)[source]

make_auction_bid(property_data, current_bid, player_money, owned_properties)[source]

should_buy_property(property_data, player_money, owned_properties)[source]

should_develop_property(property_data, player_money, owned_properties)[source]

should_mortgage_property(ai_player, required_money)[source]

should_pay_jail_fine(ai_player)[source]

should_sell_houses(property_data, player_money, target_amount)[source]

should_unmortgage_property(property_data, player_money)[source]

should_use_get_out_of_jail_card()[source]

class src.Ai_Player_Logic.HardAIPlayer[source]

Bases: object

get_adjusted_probability(base_probability)[source]

get_auction_bid(current_minimum, property_data, ai_player, board_properties)[source]

get_property_value(property_data, ai_player, board_properties)[source]

handle_jail_strategy(ai_player, jail_free_cards)[source]

handle_property_development(ai_player, board_properties)[source]

should_buy_property(property_data, player_money, owned_properties)[source]

should_mortgage_property(ai_player, required_money)[source]

update_mood(is_happy)[source]

High-Level Design

Use Case Diagram (AI Decision Making)

Illustrates the primary decision points where the Game Logic interacts with the AI Player Logic module.

Shows the core concepts involved in the AI’s decision-making process.

$@startuml class AIPlayerLogic { difficulty : String } class EasyAIPlayerLogic { strategy : Map decide_purchase() decide_bid() decide_jail() decide_development() decide_mortgage() } class HardAIPlayerLogic { easy_ai : EasyAIPlayerLogic mood_modifier : Float update_mood() decide_purchase() decide_bid() decide_jail() decide_development() } class PropertyData { name : String price : Integer group : String owner : String houses : Integer is_mortgaged : Boolean is_station : Boolean is_utility : Boolean } class PlayerData { name : String money : Integer properties : PropertyData[] in_jail : Boolean jail_free_cards : Integer } note right of EasyAIPlayerLogic : Predefined parameters note right of HardAIPlayerLogic : Uses base logic EasyAIPlayerLogic --> AIPlayerLogic HardAIPlayerLogic --> AIPlayerLogic AIPlayerLogic .. PropertyData AIPlayerLogic .. PlayerData HardAIPlayerLogic *-- EasyAIPlayerLogic @enduml$

Detailed Design

AI Player Class Diagram

Details the structure of EasyAIPlayer and HardAIPlayer, their methods, and relationship.

$@startuml interface AIPlayer { + get_property_value(property_data, ai_player, board_properties) : float + get_auction_bid(current_minimum, property_data, ai_player, board_properties) : int + handle_jail_strategy(ai_player, jail_free_cards) : str + handle_property_development(ai_player, board_properties) : Property + should_buy_property(property_data, player_money, owned_properties) : bool + should_mortgage_property(property_data, player_money) : bool + consider_trade_offer() : bool } class EasyAIPlayer { + difficulty : str + strategy : Map<String, float> + __init__(difficulty) + get_group_properties(color_group, board_properties) : List<PropertyData> + check_group_ownership(color_group, board_properties, player_name) : bool + can_build_house(property, color_group_properties) : bool + get_property_value(property_data, ai_player, board_properties) : float + handle_turn(ai_player, current_location, board_properties, is_first_round) : Map + should_use_get_out_of_jail_card() : bool + should_pay_jail_fine(ai_player) : bool + should_mortgage_property(ai_player, required_money) : List<Property> + handle_ai_turn(ai_player, board_properties) : bool + get_location_type(position, board_properties) : str + get_property_name(position, board_properties) : str + get_auction_bid(current_minimum, property_data, ai_player, board_properties) : int + handle_jail_strategy(ai_player, jail_free_cards) : str + handle_property_development(ai_player, board_properties) : Map + handle_emergency_cash(ai_player, required_amount, board_properties) : List<Property> + consider_trade_offer() : bool + should_buy_property(property_data, player_money, owned_properties) : bool + make_auction_bid(property_data, current_bid, player_money, owned_properties) : int + should_develop_property(property_data, player_money, owned_properties) : bool + should_mortgage_property(property_data, player_money) : bool + should_unmortgage_property(property_data, player_money) : bool + should_sell_houses(property_data, player_money, target_amount) : bool + get_development_priority(properties) : List<Property> } class HardAIPlayer { + difficulty : str + mood_modifier : float - easy_ai : EasyAIPlayer + __init__() + update_mood(is_happy) + get_adjusted_probability(base_probability) : float + get_property_value(property_data, ai_player, board_properties) : float + get_auction_bid(current_minimum, property_data, ai_player, board_properties) : int + handle_jail_strategy(ai_player, jail_free_cards) : str + should_mortgage_property(ai_player, required_money) : list + handle_property_development(ai_player, board_properties) : dict + should_buy_property(property_data, player_money, owned_properties) : bool } note right of EasyAIPlayer::strategy Contains parameters like: - max_bid_multiplier - development_threshold - jail_stay_threshold - mortgage_threshold end note note right of EasyAIPlayer::handle_turn More complex than interface suggests end note note right of EasyAIPlayer::should_mortgage_property Returns list of props to mortgage end note note right of EasyAIPlayer::handle_ai_turn Simplified decision logic? end note note right of EasyAIPlayer::handle_property_development Returns property dict end note note right of EasyAIPlayer::make_auction_bid Seems redundant with get_auction_bid? end note note right of EasyAIPlayer::should_mortgage_property Different signature than above end note note right of HardAIPlayer::get_property_value Wraps EasyAI end note note right of HardAIPlayer::get_auction_bid Wraps EasyAI end note note right of HardAIPlayer::handle_jail_strategy Wraps EasyAI end note note right of HardAIPlayer::should_mortgage_property Delegates to EasyAI end note note right of HardAIPlayer::handle_property_development Wraps EasyAI end note note right of HardAIPlayer::should_buy_property Wraps EasyAI end note note bottom of HardAIPlayer HardAIPlayer uses an instance of EasyAIPlayer to get baseline decisions, then modifies them based on the `mood_modifier`. end note HardAIPlayer o-- EasyAIPlayer : uses EasyAIPlayer ..|> AIPlayer HardAIPlayer ..|> AIPlayer AIPlayer ..> "Property" : uses data AIPlayer ..> "Player" : uses data @enduml$

Activity Diagram: Hard AI Auction Bid Decision

Illustrates the flow for HardAIPlayer.get_auction_bid, showing the interaction with EasyAIPlayer and the mood influence.

Sequence Diagram: Hard AI Purchase Decision (should_buy_property)

Shows HardAIPlayer deciding whether to buy, potentially overriding the EasyAIPlayer logic based on mood.

Sequence Diagram: Updating AI Mood (update_mood)

Shows the simple process of adjusting the mood_modifier.

@startuml
participant "GameLogic" as Caller
participant "HardAIPlayer" as HardAI

Caller -> HardAI : update_mood(is_happy=True)
activate HardAI

alt is_happy is True
HardAI -> HardAI : mood_modifier = max(-0.3, mood_modifier - 0.05)
else is_happy is False
HardAI -> HardAI : mood_modifier = min(0.3, mood_modifier + 0.05)
end

HardAI --> Caller : return
deactivate HardAI
@enduml