Creating a simulation game like this in Godot involves defining a flexible architecture that models the dynamic interactions of assemblages, resources, and agents. Here’s a roadmap that covers the architecture, class structure, tools, and best practices for scripting. --- ### **1. Core Architecture and Classes** #### A. **Game World Structure** - **`World` (Root Node)**: - Acts as the container for all game elements and manages the simulation loop. - Monitors global resource flows, manages high-level interactions, and tracks player inputs. - Provides access to a “world clock” for triggering updates and events (like feedback loops or state checks). - **`ResourceManager` (Singleton)**: - Manages global resources, tracking total available resources in the game (e.g., water, energy). - Provides methods for resource distribution, usage, and feedback mechanisms. - Interacts directly with `Assemblage` and `Agent` nodes to allocate resources dynamically. #### B. **Assemblages and Agents** - **`Assemblage` (Base Class)**: - Represents any complex structure that performs specific functions by combining components (like tools, plants, machines). - Contains properties for input needs (resource dependencies) and outputs (products or waste). - Defines `assemble()` and `disassemble()` methods for building or breaking down an assemblage. - Tracks subcomponents and manages their interactions, forming a “system within a system.” - **`Agent` (Derived from `Assemblage`)**: - Represents “living” entities, like humans or animals, which actively consume resources, make decisions, and interact with other agents. - Contains behaviors like `consume()`, `produce()`, and `move()`. - Responds to local stimuli and possesses individual needs or goals (e.g., finding water when thirsty), which influence their actions and affect the assemblage they belong to. #### C. **Components and Resources** - **`Component` (Base Class)**: - Represents basic building blocks that become functional once assembled (e.g., a solar panel, water filter). - Contains a `type` property (e.g., solar, mechanical) to categorize components, facilitating assembly within `Assemblages`. - Methods like `activate()` and `deactivate()` allow components to be turned on/off based on resource availability. - **`Resource`**: - Represents any consumable material (e.g., water, energy). - Tracks properties like `quantity`, `quality`, and `decay` (for resources that degrade over time). - Has functions like `consume()` and `replenish()` for managing usage and regeneration in the world. #### D. **Relational Structures and Feedback Loops** - **`FlowManager` (Singleton)**: - Manages connections between nodes, handling input-output flows between `Assemblages` and `Resources`. - Contains a feedback loop system, using thresholds to trigger adjustments in flows or initiate events like “surplus” or “shortage” modes. - Provides visualization data to track connections, helping players see how resources move through the network. --- ### **2. Detailed Class Structure and Relationships** #### A. **Assemblage Subclass Types** - **Examples**: - `WaterPurificationSystem` (inherits from `Assemblage`): Consumes energy and polluted water, outputs clean water. - `EnergyGenerator` (inherits from `Assemblage`): Consumes sunlight or fuel, outputs energy. - **Interdependencies**: - Assemblages interact based on resource requirements. For example, `WaterPurificationSystem` can depend on `EnergyGenerator` to function, establishing interdependent relationships in the network. #### B. **Resource Flow and Event Triggers** - **Flow Connections**: - Connections between nodes are managed by `FlowManager`, which tracks the start and end of each connection (like a directed graph). - Events are triggered based on resource availability; e.g., if `EnergyGenerator` power output falls below a threshold, downstream nodes (like `WaterPurificationSystem`) might reduce function or shut down. - **Feedback Loops**: - Use timers or state checks to trigger feedback responses. For instance, if water quality in a purification system drops, it could trigger an increased algae population in nearby nodes (simulating a natural feedback loop). - Feedback properties are stored as dictionaries within each assemblage or agent, detailing response conditions and actions. --- ### **3. User Interaction and Visualization** - **Graph-Based Visualization Tool**: - Use a visual layer in Godot that displays resource flows and relationships, likely achieved with 2D `Line2D` nodes connecting resource producers to consumers. - Color-coded lines and icons can denote flow intensity or direction, with special icons for bottlenecks or resource shortages. - **Interactive UI**: - Each `Assemblage` and `Agent` node can have a UI panel displaying its current resource levels, needs, and outputs. This allows players to inspect, modify, and monitor individual nodes. --- ### **4. Simulation Management Tools and Logic** - **State and Update Cycles**: - Create a simulation loop that updates at fixed intervals, checking resource levels, feedback conditions, and relationships. - Use state machines within `Agent` nodes for behaviors like “seek resource” or “produce output” to ensure responsive, realistic interactions. - **AI and Decision Logic**: - Simple AI scripts using behavior trees or finite state machines could control agents’ decision-making, reacting to environmental cues (like resource scarcity). - Randomized probabilities can be added to behaviors for unpredictability, simulating more organic responses. --- ### **5. Roadmap for Development and Best Practices with Chat-GPT** #### A. **Development Roadmap** 1. **Basic World and Resource Setup**: - Set up the `World`, `ResourceManager`, and initial resources. - Build the `Resource` and `FlowManager` classes. 2. **Assemblage and Component Classes**: - Define the `Assemblage` and `Component` base classes, followed by specific assemblage subclasses like `WaterPurificationSystem` or `EnergyGenerator`. 3. **Agent Classes and Behavior Scripts**: - Implement the `Agent` class with basic needs-based logic, such as resource consumption. 4. **Feedback Mechanisms and FlowManager**: - Develop `FlowManager` to handle connections and flow updates, building in feedback loop functionality. 5. **Visualization and UI**: - Create the graph-based visualization for resource flows and assemblage states. #### B. **Best Practices for Script Generation with ChatGPT** - **Break Down Requirements**: Clearly specify individual class functionalities, functions, or complex methods. Use simple, direct language to guide ChatGPT in generating each script in manageable segments. - **Iterative Approach**: Start with base classes and simple methods, then request extensions for more complex features (e.g., add feedback loops, event triggers). Request additional layers step-by-step to gradually refine functionality. - **Use Code Comments for Explanation**: Request code with inline comments that explain each step. This makes it easier to adapt the code later and debug it within the larger project. - **Test and Refine in Small Increments**: Integrate and test each new script or class individually before moving on. This reduces debugging complexity and makes it easier to catch errors specific to each functionality. --- This roadmap should guide the project from basic setup to advanced interactions, using Godot’s nodes and scripting features to model your dynamic, interdependent ecosystem. Let me know when you’re ready to start writing specific scripts, and I can help build them step-by-step! ---- # Containers To integrate "container" assemblages into your simulation architecture, we can expand the `Assemblage` class hierarchy to introduce specific properties and behaviors for these storage-focused structures. Containers will store resources without significant transformation, manage decay or degradation rates, and have defined maximum capacities. Here’s how to logically structure this: --- ### **1. Adding a `Container` Subclass in the Assemblage Hierarchy** #### A. **Container Class** - **Purpose**: Acts as a specialized `Assemblage` that primarily stores resources rather than transforming them. It handles maximum capacity, current resource levels, and decay modifiers. - **Properties**: - `capacity`: The maximum amount of a resource the container can store. - `current_level`: Tracks the current amount of stored resource. - `decay_rate`: Defines how quickly a resource degrades over time due to factors like evaporation, self-discharge, or natural spoilage. - `preservation_modifier`: Adjusts the decay rate (e.g., reducing decay in a refrigerator). - **Methods**: - `add_resource(amount)`: Adds resources to the container, respecting capacity limits. - `remove_resource(amount)`: Removes resources, allowing withdrawal until empty. - `apply_decay(delta_time)`: Updates `current_level` by reducing resources based on the `decay_rate` and `preservation_modifier`. #### B. **Container Subtypes** - **WaterTank**: - Represents water storage with potential evaporation. The `decay_rate` could represent water lost to evaporation, which would be modified if a lid or cover is applied. - **Battery**: - Stores energy with a self-discharge rate, where `decay_rate` could represent gradual energy loss over time. Optionally, `preservation_modifier` could reduce self-discharge if a specific storage environment is created. - **Refrigerator**: - Stores perishable resources, reducing decay to prolong resource life. Here, the `preservation_modifier` might significantly lower decay rate, simulating a cool storage environment. --- ### **2. Integration with the Assemblage System** #### A. **Inherit from `Assemblage`** - Define `Container` as a subclass of `Assemblage`. This lets it inherit basic properties like `input` and `output` resources, even though it primarily stores rather than transforms resources. - Within `Container`, override transformation-related methods from `Assemblage` to simply manage resource decay without conversion to other resource types. #### B. **Tracking Container Assemblages in `FlowManager`** - Update `FlowManager` to recognize `Container` instances as resource “holding” nodes within the network, not directly involved in transformation. - Define `FlowManager` interactions with containers, ensuring that only a certain amount of resources are transferred in or out based on `capacity` and `current_level`. --- ### **3. Detailed Class Structure and Method Logic** #### A. **Container Class Definition** 1. **Properties**: - `capacity`: Defines the container’s maximum storage. - `current_level`: Keeps track of the actual resource quantity inside. - `decay_rate`: Applied per simulation tick to reduce `current_level`. - `preservation_modifier`: A multiplier (default `1.0`) that adjusts `decay_rate` for specific storage conditions. 2. **Methods**: - **`add_resource(amount)`**: - Checks if `current_level + amount` exceeds `capacity`; if so, only adds up to `capacity`. - Returns any excess resource for potential redirection. - **`remove_resource(amount)`**: - Reduces `current_level` by `amount` (up to `0`), simulating withdrawal. - **`apply_decay(delta_time)`**: - Applies `decay_rate * preservation_modifier` to `current_level`, reducing stored resources per simulation tick. - This accounts for time-based degradation while adjusting for environmental conditions (e.g., refrigeration). #### B. **Example Subclasses and Specialized Behavior** - **WaterTank**: - `decay_rate` could represent evaporation, with `preservation_modifier` representing effects of a cover or cooler climate. - Adjusts the `decay_rate` based on specific environmental factors within the simulation (like shade or direct sunlight). - **Battery**: - `decay_rate` reflects self-discharge over time. - Has a minimal `preservation_modifier`, with possible improvements if stored in optimal conditions. - **Refrigerator**: - High `preservation_modifier` to drastically slow down decay for stored perishable resources. - Might interact with other game mechanics that influence internal temperature, affecting the `preservation_modifier`. --- ### **4. Updating Resource Management and Flow Interactions** #### A. **FlowManager Handling for Containers** - `FlowManager` should identify `Container` nodes and: - **Check Capacity**: Only distribute resources to a container up to its `capacity`. - **Decay Handling**: Ensure each container’s `apply_decay()` method runs per simulation tick to adjust `current_level`. - **Display Availability**: Visualize the container’s `current_level` on-screen, allowing players to assess the stored amount and decay rate. #### B. **Global Resource Allocation Logic** - Update `ResourceManager` to distribute resources to containers based on availability and priority. - Containers can be set as reserve storage locations, accessed only when primary sources are insufficient, simulating backup resources. --- ### **5. Visualizing Containers in the Simulation** - **Capacity and Levels**: Show each container’s status with gauges or progress bars, indicating how full they are relative to capacity. - **Decay Feedback**: Use gradual color changes or effects to show decay visually, such as water levels dropping in a tank or energy icons fading in a battery. - **Notifications**: Alert players when containers reach low or high thresholds, especially if decay is increasing (e.g., a water tank left in the sun). --- ### **Best Practices for Script Generation** - **Modular Scripting**: Request scripts in modular segments, beginning with the `Container` class as a general structure, then adding specific methods (`add_resource`, `apply_decay`, etc.) one by one. - **Testing Each Subclass**: Start by scripting the `Container` base, testing it independently, then gradually introduce specialized subclasses like `WaterTank` or `Battery`. - **Iterative Decay Mechanism**: For `apply_decay()`, request incremental adjustments (like adding `preservation_modifier`) to refine the decay mechanics in a controlled way. This design should integrate container assemblages smoothly, maintaining resource storage functionality and adding decay control for a realistic, balanced simulation. Let me know when you’d like to begin writing scripts for any of these classes! Integrating physical connectors with limitations and decay into your game architecture can add a realistic layer to the simulation by making connections between nodes subject to real-world constraints. Here's how you could structure and integrate these connectors into your existing framework: --- ### **1. Introduce a Connector Class** #### A. **Connector Class Overview** - **Purpose**: Represents a physical or data connection between two `Assemblage` nodes (or between `Containers` and `Assemblages`), with defined limitations, decay properties, and capacity. - **Properties**: - `max_capacity`: Maximum amount of resource or data the connector can handle (e.g., liters per minute for pipes, amperage for wires). - `decay_rate`: Percentage or amount of resource lost over distance or time due to resistance, leakage, or other inefficiencies. - `preservation_modifier`: Adjusts `decay_rate` based on insulation or quality of the connector. - `resource_type`: Specifies the type of resource it can transfer, like water, energy, or data. - **Methods**: - `transfer_resource(amount, delta_time)`: Handles resource movement across the connector, respecting `max_capacity` and applying decay. - `apply_decay(distance)`: Reduces the transferred amount based on decay over distance (for long connectors) or time. --- ### **2. Structuring the Connector Class in the Game Architecture** - **Connector as a Standalone Class**: - Define `Connector` as its own class that links specific nodes together. - Each instance of `Connector` has `start_node` and `end_node` properties, representing the two `Assemblage` or `Container` nodes it connects. - `FlowManager` can manage all instances of `Connector`, monitoring for overloading or degradation events if the connector exceeds its `max_capacity`. - **Connector Types (Subclasses of Connector)**: - **Pipe**: Transfers liquids and has a maximum flow rate (`max_capacity`). Includes decay for evaporation (uninsulated pipes) or freezing (temperature-sensitive pipes). - **Wire**: Transfers electrical energy, with `max_capacity` as maximum amperage. Has a decay rate for energy lost to resistance. - **DataProtocol**: Manages data transmission, with `max_capacity` as bandwidth. Includes packet loss or latency over longer connections. - **VacuumSystem**: Simulates pneumatic transfers (like air or gas), with limitations on volume per time and efficiency loss over distance. --- ### **3. Integrating Connectors with FlowManager** #### A. **FlowManager Enhancements** - **Connector Management**: - `FlowManager` should track each `Connector` instance and check whether resources are being routed through connections in line with their `max_capacity`. - If the amount exceeds `max_capacity`, apply a penalty (e.g., increase in `decay_rate`, triggering a resource overflow warning, or resource loss). - **Decay and Preservation**: - `FlowManager` calls each connector’s `apply_decay()` during every simulation update, simulating energy or resource loss in proportion to distance, quality, and the `preservation_modifier`. - **Conditional Routing**: - If a `Connector` is overloaded or degrades, `FlowManager` could reroute resources if alternate paths exist, simulating realistic system response. #### B. **Connector Visual Feedback** - Visualize each connector’s status (e.g., color-coding for load levels or icons for decayed areas) to show the player where resource limits or losses occur. - If a `Connector` is near its limit, you could display a “stress” indicator, warning players of potential failure or loss. --- ### **4. Practical Examples of Connector Logic** #### A. **Pipes for Water Transfer** - **Capacity Limit**: `max_capacity` of 10 liters/minute. - **Decay Mechanism**: Loss of 0.5 liters per minute if pipe is uninsulated. - **Use Case**: Connects a `WaterTank` to a `WaterPurificationSystem`. If over 10 liters/minute is attempted, water loss occurs, and efficiency decreases. #### B. **Wires for Energy Transfer** - **Capacity Limit**: `max_capacity` of 15 amps. - **Decay Mechanism**: 2% energy loss per meter due to resistance, modified by `preservation_modifier` if using high-quality materials. - **Use Case**: Connects an `EnergyGenerator` to other `Assemblages`. Exceeding capacity could trigger an “overload” state or reduce power delivery efficiency. #### C. **DataProtocol for Data Transfer** - **Capacity Limit**: `max_capacity` of 100 Mbps. - **Decay Mechanism**: Latency or packet loss over 50 meters, affecting response times. - **Use Case**: Links `Assemblages` that rely on data exchange. If exceeded, introduces delays or partial data delivery. --- ### **5. Class Structure and Logical Methods for Connectors** #### A. **Connector Class Definition** 1. **Properties**: - `start_node`, `end_node`: Nodes the connector links. - `max_capacity`: Limit on flow rate, amperage, or data bandwidth. - `decay_rate` and `preservation_modifier`: Decay properties applied to resource transfer. 2. **Methods**: - **`transfer_resource(amount, delta_time)`**: - Checks if `amount` exceeds `max_capacity`. If it does, apply a penalty to the `decay_rate`. - Calculate final transferred amount by factoring in `decay_rate * preservation_modifier`. - **`apply_decay(distance)`**: - Reduces `amount` over distance, adjusting for `preservation_modifier`. - Simulates long-distance decay (e.g., water evaporation, energy loss in wires). #### B. **Connector Subclass Examples** - **Pipe**: - `apply_decay` could factor in evaporation and insulation level. - Maximum flow rate (`max_capacity`) defines the upper limit of liquid transfer. - **Wire**: - `apply_decay` factors in energy lost over distance. - Maximum amperage defines safe operation limits, with overload penalties if exceeded. - **DataProtocol**: - `apply_decay` factors in latency or packet loss based on distance. - Bandwidth defines maximum data transfer speed. --- ### **6. Testing and Best Practices** 1. **Start Simple**: Begin by implementing one connector type, such as a `Pipe`, to validate `transfer_resource()` and `apply_decay()` methods before adding additional complexity with other connector types. 2. **Set Realistic Defaults**: Define realistic decay rates, maximum capacities, and preservation modifiers for each connector, allowing players to see the impact of quality and capacity on resource flows. 3. **Use Feedback Loops**: Add event listeners to trigger alerts if `max_capacity` is exceeded or decay reaches a critical threshold, prompting players to intervene (e.g., replacing connectors or upgrading insulation). 4. **Visual Feedback and Debugging**: Implement visual indicators to help players monitor connector status, highlighting overloaded connectors or areas of significant resource loss. --- This connector system will enhance the simulation’s depth, allowing players to navigate real-world constraints and manage the sustainability of their resource networks more strategically. Let me know if you’d like to develop specific scripts for any of these connectors! # Views To integrate these dual visualization modes (the **Conceptual System View** and the **Realistic 3D View**) in your simulation architecture, you'll need to build a flexible and modular system where data from the simulation core can be rendered differently depending on the view mode. Here’s how you can set up these systems and functionality: --- ### **1. Conceptual System View (2D Top-Down with Dynamic Connections)** The **Conceptual System View** will be a 2D, drag-and-drop interface where assemblages can be repositioned, and connection lines dynamically adjust. This view is designed for an abstract representation of flow, making it ideal for understanding resource movement over time. #### A. **Core Components for Conceptual System View** - **Assemblage Nodes (2D Visual Elements)**: - **Positionable Icons**: Each `Assemblage` and `Container` has an icon or shape that users can move on a 2D grid or canvas. - **Event Handling**: Assemblages should support click-and-drag events for repositioning and show relevant data on hover or click. - **Dynamic Connectors**: - **Connection Lines**: Drawn between assemblages using `Line2D` nodes that update in real-time as nodes are moved. - **Flow Visualization**: - **Thickness**: Adjust line thickness based on the flow volume. - **Color Coding**: Use colors to indicate resource type or state (e.g., green for normal flow, red for overload). - **Directional Effect**: Apply shader effects or textures that create a subtle moving effect, simulating flow direction. #### B. **Scene Structure** - **Canvas Layer**: - A `Control` or `CanvasLayer` node holds the 2D view to maintain consistent scaling and positioning for the top-down view. - **Assemblage Manager**: - This manager maintains the updated positions of `Assemblage` nodes and manages the creation, destruction, and realignment of `Line2D` connections. - **UI Layer for Data**: - Overlay elements to show flow rates, node data, and status indicators near each `Assemblage`. --- ### **2. Realistic 3D View (FPV Style)** The **Realistic 3D View** represents the system as it would appear in the physical world, with pipes, wires, and equipment realistically arranged in a 3D environment. This view provides an immersive perspective and a more “hands-on” exploration of the system. The overlay in this mode can display the same flow data as the Conceptual View but in the context of a 3D environment. #### A. **Core Components for Realistic 3D View** - **3D Models of Assemblages and Connectors**: - **Assemblages**: Each `Assemblage` has a 3D model representing it realistically. - **Pipes, Wires, and Cables**: Connector types are represented by 3D models with shape and size constraints that reflect actual hardware. - **Overlay for Flow Data**: - **Flow Lines with Color and Thickness Effects**: Overlay connectors in the Conceptual System View style as translucent or emissive lines to simulate the abstract flow representation. - **Shader Effects for Flow Direction**: Use animated materials to indicate resource direction. - **FPV Navigation and Interaction**: - **Player Controls**: Allow players to move in first-person view (FPV) with an option to toggle the overlay on or off. - **UI Toggle for Overlay**: A simple interface element lets players enable or disable the flow overlay for each connector in the Realistic View. --- ### **3. Data Synchronization and View Switching** #### A. **Shared Data Model** - Both views should pull data from a shared source that holds all flow rates, resource types, connection states, etc. This centralized data model allows each view to reflect changes made by the other in real time. #### B. **View Mode Manager** - A `ViewModeManager` can handle switching between views, pausing or adjusting simulation speeds if necessary to fit each view’s requirements. - The manager initializes and cleans up elements depending on the selected view, ensuring smooth transitions and maintaining performance. --- ### **4. Architecture Integration** #### A. **Assemblage and Connector Logic** - **Assemblages**: Each `Assemblage` should store metadata on its connections, position, and current flow rates. They also need to notify the `ViewModeManager` of any changes, which can relay this to active connectors for real-time updates. - **Connectors**: - In the **Conceptual View**, each connector is a `Line2D` node tied to start and end points. - In the **Realistic View**, each connector can be a 3D model, optionally with a corresponding overlay line that matches the conceptual design. - Both views should adjust the appearance of connectors dynamically based on real-time data from `FlowManager`. #### B. **FlowManager and Connector Synchronization** - `FlowManager` should keep track of all connectors and update both visual flows (for conceptual connectors) and physical flows (for realistic pipes or wires). - `FlowManager` should be able to pause certain calculations when only the 3D view is active, to preserve performance if detailed flow visualization isn’t needed. #### C. **UI and Feedback Mechanism** - Add on-screen indicators to visualize statuses like maximum capacity and decay rates. Players can see this in the Conceptual View, while the Realistic View could display this information as pop-ups or HUD elements. - Alerts or warnings (like overloaded connectors) can appear in both views but may have unique visual cues to suit each mode. --- ### **5. Best Practices for Script Generation** #### A. **Script Modularity** - Separate logic for `Connector`, `Assemblage`, `Container`, and UI scripts to keep functionality distinct. For instance, each connector type can have its own subclass, while the overlay effects can reside in a separate visualization script. #### B. **Dynamic Updates and Debugging** - Use real-time debugging to monitor flow rates, connector statuses, and node positions. Test each view independently to confirm that they react smoothly to updates in the shared data. #### C. **Script Generation with ChatGPT** - **Define Classes Separately**: Start by generating isolated scripts for each core functionality (like a `Connector`, `Assemblage`, or `ViewModeManager`). - **Iterative Testing**: For visual components, test basic `Line2D` and `3D` connector visualizations before adding complex overlays. - **Document Functionality**: Use clear comments and keep each script segmented by functionality, making it easier to debug and iterate on as you refine views and behavior. --- This architecture will give you the flexibility to explore both abstract and realistic views, allowing players to shift perspectives as they build and optimize resource flows in your ecosystem simulation game. Let me know if you'd like specific guidance on implementing these features in Godot! ---- # Agent Sims To integrate **human agents** with individual needs, movement, and behavior into the architecture, we’ll treat each agent as an assemblage with specific needs-driven behaviors, spatial interactions, and dependencies. Here’s a roadmap for adding this functionality: --- ### **1. Core Components for Human Agent Functionality** #### A. **HumanAgent Class** (subclass of Assemblage) - **Attributes**: - **Needs**: Track `water`, `oxygen`, `food`, and `sleeping_hours` as resources with thresholds that, when unmet, trigger specific actions. - **Waste Production**: Define outputs like `urine`, `CO2`, and `feces` with daily production rates. - **Status Flags**: `is_hungry`, `is_thirsty`, `needs_sleep`, `needs_bathroom`, `can_work`. - **Movement & Location**: `current_location`, with target locations like `kitchen`, `bathroom`, `bed`. - **Methods**: - **Update Needs**: Regularly decrement needs based on time to simulate hunger, thirst, fatigue, etc. - **Check Thresholds**: When a need surpasses a defined threshold, update status flags to trigger actions (e.g., if thirst exceeds the threshold, set `is_thirsty` to `true`). - **Produce Waste**: Track waste output and add it to a centralized resource pool (e.g., waste management system) for further processing by other assemblages. #### B. **Behavior Management**: - **Needs-Driven Actions**: - **Determine Next Action**: Based on status flags, the `HumanAgent` prioritizes actions—if hungry, go to the kitchen; if tired, go to bed, etc. - **Pathfinding and Movement**: Implement pathfinding to physically move agents around the environment to reach the locations needed to satisfy specific needs. - **Resource Consumption and Production**: - **Consume Resources**: On arrival at a designated area (e.g., kitchen), the agent will consume resources to meet needs. - **Produce Waste**: When visiting the bathroom, add outputs (e.g., `urine` and `feces`) to waste resources. --- ### **2. Implementing Movement and Spatial Interaction** - **Environment Nodes**: Define `LocationNode` points for key areas (`kitchen`, `bathroom`, `bed`, `workstation`). - **Pathfinding Integration**: - Utilize Godot's `Navigation` system to let agents calculate routes from their current location to the destination node. - **Interaction Points**: - Each key area (kitchen, bathroom, etc.) can have an `InteractionPoint` object to simulate actions like drinking, eating, and sleeping upon arrival. - **Animation & Visuals**: Optionally add animations or visual indicators to show actions (e.g., sitting at a table, using the bathroom) for immersive feedback. --- ### **3. Human Needs and Emergent Behaviors** - **Threshold-Based Decision Tree**: - The `HumanAgent` class should include a decision-making method that evaluates need levels and prioritizes tasks. - Needs hierarchy: Determine a priority order (e.g., thirst > hunger > sleep) so agents tackle the most urgent need first. - **Work Availability**: - When all primary needs are satisfied, set `can_work = true`, allowing the agent to engage in assigned tasks. - If needs fall below a threshold, set `can_work = false`, suspending work tasks. - **Failure Cascade Mechanism**: - If multiple agents cannot meet their needs, they stop performing assigned maintenance tasks, potentially leading to a system collapse, as assemblages and infrastructure decay due to neglect. --- ### **4. Integration of Waste as a Resource for Other Assemblages** - **Waste Processing System**: - Define waste processing assemblages that can take in `urine`, `feces`, or `CO2` as inputs and process them, perhaps converting waste into fertilizer, biofuel, or other usable outputs. - **Waste Tracking**: - Add waste outputs to a central waste pool or send directly to relevant processing assemblages, supporting nutrient cycling or recycling within the system. --- ### **5. Architecture Updates and Best Practices** - **Class Hierarchy**: - Human agents should inherit from a base `Assemblage` class, allowing them to work like any other component but with complex behaviors based on needs. - **Resource and Waste Interfaces**: - Use resource interfaces to standardize how needs are consumed or produced, whether for waste processing or work interaction. - **Behavior Trees for Complex Actions**: - Consider using a behavior tree to define conditional actions based on needs. This can give you flexibility as you add more agent behaviors and dependencies. --- ### **Visualizing Agents and System Behavior** - **In Conceptual View**: Represent agents as animated 2D nodes that move to various assemblages or stations, with icons showing needs and outputs. - **In Realistic View**: Use 3D models of agents that navigate between locations. Display icons or HUD indicators for needs and availability status, or toggle an overlay that shows active needs in real-time. --- ### **Summing Up the Simulation Flow** 1. **Agent Initialization**: Each human agent is initialized with specific needs and assigned locations. 2. **Needs Decrementing**: Over time, needs decrease, updating each agent’s internal status. 3. **Behavior Checks**: When thresholds are hit, the agent prioritizes actions to meet those needs by moving to designated locations. 4. **Work and System Maintenance**: If needs are met, agents become available to operate or maintain infrastructure, adding realism to their interdependence within the system. 5. **Waste Generation**: Needs-driven behaviors (like eating or drinking) generate waste, introducing a further loop of dependencies where waste assemblages process resources that the system can recycle or reuse. --- This architecture will create an emergent, interconnected system where agents' basic needs and behaviors interact to maintain or destabilize the ecosystem. This detailed roadmap should guide the creation of a complex, needs-based system for agents with realistic spatial interactions and feedback loops in your game. # Need Driven Agent Behavior To accurately track waste production, resource consumption, and labor demands (chore-hours) across your simulated agents and infrastructure, we can expand on the existing architecture to include **Per-Interaction Tracking** and **Chore-Hour Allocation**. Here’s how to integrate this functionality: --- ### **1. Refine Agent Attributes for Per-Interaction Tracking** #### **A. Resource and Waste Tracking on an Interaction Basis** - **Fractional Usage Metrics**: - Create fractional metrics for each daily input and output per interaction. For instance: - **Meals**: Divide daily calorie and water intake across 3 meals. - **Bathroom Visits**: Set average frequencies (e.g., 3 urinations, 1 defecation) and assign fractions accordingly. - **Interaction-Based Waste Outputs**: - Every interaction updates both an agent’s daily consumption and the global waste/resource pools for accurate tracking. #### **B. Chore-Hour and Decay Tracking** - **Chore-Hours**: - Set a daily maximum of 8 chore hours per agent. - Track chore-hours required by each assemblage (e.g., cooking = 1 hour, laundry = 15 minutes for start, 15 minutes for unload). - **Decay Tracking**: - Throughout the day, deduct available chore hours gradually, simulating agent fatigue and requiring planning for necessary tasks. ### **2. Structure for Per-Interaction Resource and Waste Generation** To achieve detailed tracking, create a system of **Interaction Modules** within the `HumanAgent` class, each with specific inputs, outputs, and required chore-hours: - **Daily Interaction Breakdown**: - **Eating** (3 times a day): Divides total calorie and water intake by 3, with output tracking (e.g., each meal results in partial CO2 exhaled). - **Drinking** (throughout the day): Apply fractional water intakes per drinking interaction. - **Bathroom Use** (based on average): Assign outputs for each use. - **Flexible Activity System**: - Define each `Interaction` as an instance with parameters for `input`, `output`, `waste_generated`, and `chore_hours`. - Schedule interactions at regular intervals, adjusting them based on agent behavior and status. #### Example of the **Interaction Module Class**: ```plaintext Interaction: - input_requirements (resource amounts needed) - output_produced (waste or byproducts generated) - chore_hours_needed - status (e.g., complete, interrupted, in progress) ``` ### **3. Update Assemblage Classes to Include Chore-Hour Requirements** Integrate **chore-hour requirements** within the `Assemblage` class for each task needing human intervention. - **Human-Required Assemblages**: - Define tasks that are purely human-driven, like `Cooking` or `Bathroom Maintenance`. - For activities like `Cooking`, `Chore-Hour` is deducted from the agent's available hours for each meal. - **Mixed Human-Automated Assemblages**: - Some tasks, such as laundry, can be set up to require minimal direct involvement (e.g., initial loading and unloading). - **Chore-Hour Pool and Allocation**: - Create a **ChoreHourManager** to allocate and track the collective daily hours, distributing chores based on each agent's available time. --- ### **4. Daily and Activity-Based Tracking System** To ensure that resource and waste tracking is dynamic and reflects real-time agent behavior: - **Daily Resource Decay Loop**: - Every 24-hour cycle, calculate remaining resources and redistribute available chore-hours for the next day. - **Agent-Triggered Activities**: - Upon fulfilling needs like eating or using the bathroom, update the respective waste and resource trackers to reflect the output (e.g., add `urine` to the waste pool). --- ### **5. Visualizing Resources, Wastes, and Chore-Hour Distribution** - **Conceptual System View**: - Display each agent’s daily resource intake and waste outputs as icons or bars, adjusting with each interaction. - **Chore-Hour Allocation Map**: - Use an overlay showing chore-hours required vs. available, with tasks that may get dropped if agents run out of time, indicating the likelihood of system collapse due to neglected tasks. ### **6. Simulation Flow Example** 1. **Initialize Agents**: Each agent starts with their daily resource needs and waste production targets based on standard values (e.g., 2.7 L water, 2500 kcal). 2. **Schedule Daily Interactions**: Throughout the day, agents perform scheduled interactions like eating, drinking, bathroom use, with each action deducting resources and generating waste. 3. **Chore-Hour Allocation**: For each assemblage needing attention, allocate available chore hours and deduct from each agent’s daily allotment. 4. **Waste and Resource Flow Management**: Track cumulative waste and redistribute resources (such as urine, feces) to relevant processing assemblages, supporting nutrient and resource cycling in the system. This detailed breakdown will allow you to track resources, waste, and human labor requirements, adding realism and depth to the simulation of an off-grid, closed-loop ecosystem.