GPT Generated: ALIEN Supplementary How-To Manual

[TheBarret]

I hope this little How-To written by GPT can supplement the current documentation, it needs an update but since the author is doing this completely on his own, he is unable to do all of it.
I am more then happy to help out as best as I can so everyone can enjoy this wonderful project.

GPT analyzed project files, old documents + partial source code and was able to generate a comprehensive how-to.

Remark:

• GPT is known to make errors and/or mistakes, if that's the case here, let me know and I will correct it.
• GPT used only the available data (outdated) and then I disassembled the binary and fed this again to GPT to overlap the outdated parts
  and cross reference it.

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Artificial Life Environment

The ALIEN simulation system, with its diverse cell types and capabilities,
allows for the creation of a wide range of artificial organisms with various behaviors and functions.
Here are some examples of the types of organisms that can be designed and simulated using ALIEN cells

1. Simple Organisms
   These may consist of a few basic cell types, such as Transmitter cells for energy transport, Constructor cells for building a basic structure, and Muscle cells for basic movements.
   Simple organisms could be designed for mobility and energy collection.

2. Predatory Organisms By incorporating Attacker cells, organisms can be designed to hunt and attack other cells or organisms.
   These predators may use energy gained from their prey to sustain themselves and reproduce.

3. Defensive Organisms Organisms equipped with Defender cells can defend against attacks from predatory organisms. Defender cells can reduce the strength of enemy attacks, providing a form of protection.

4. Self-Replicating Organisms With the help of Constructor cells and Injector cells, organisms can be designed to self-replicate.
   Constructor cells build copies of the organism, and Injector cells can override the genome of other Constructor cells,enabling mutations and evolution.

5. Information Processing Organisms Neuron cells and Sensor cells can be used to create organisms capable of sensing their environment, processing information, and responding to specific stimuli.
   These organisms may exhibit complex behaviors based on sensory input.

6. Cooperative Organisms Organisms can be designed to cooperate with other organisms of the same or different species.
   For example, one organism could specialize in energy collection, while another specializes in defense. Cooperative behaviors may emerge through interactions between cells.

7. Swarm Organisms Multiple organisms can work together in a swarm, coordinating their actions to achieve common goals.
   Swarm behaviors, such as collective movement and decision-making, can be simulated.

8. Evolving Organisms By introducing mutation and genetic diversity through Injector cells and allowing for the selection of successful traits, organisms can evolve over time.
   This can lead to the emergence of new and more sophisticated behaviors.

9. Task-Specific Organisms Organisms can be designed for specific tasks, such as environmental cleanup, exploration, or resource gathering.
   Sensor cells can detect the presence of target substances, and the organism can be programmed to respond accordingly.

10. Hybrid Organisms Combining different cell types and behaviors, hybrid organisms with unique and versatile functions can be created.
    These organisms may adapt to changing environmental conditions and challenges.

The versatility of the ALIEN simulation system allows for the exploration of various artificial life forms and the study of emergent behaviors.
Researchers and users can experiment with different combinations of cell types, genetic information, and environmental conditions to observe how organisms evolve and adapt to their virtual environments.

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Cell shapes and sizes

Shapes and sizes play a crucial role in determining the behavior and dynamics of artificial organisms.
The choice of cell shapes and sizes can impact the speed of evolution and the emergence of interesting results.

Here's an explanation of cell shapes, sizes, and their implications

Cell Shapes
ALIEN allows for flexibility in designing cell shapes, which can vary from simple to complex.
Cell shapes are defined by the arrangement of particles and bonds between them. Here are some considerations regarding cell shapes

1. Simple Shapes
   Simple shapes like spheres or cubes can lead to stable and predictable behaviors.
   These shapes may not deform or move as dynamically as more complex shapes.

2. Complex Shapes
   Complex shapes with irregular geometries can result in more diverse and unpredictable behaviors.
   Cells with multiple protrusions or deformable regions can exhibit interesting locomotion and interactions with other cells.

3. Intermediate Shapes
   Intermediate shapes that combine both simple and complex features may strike a balance between stability and adaptability.
   These shapes can be a good starting point for rich evolving designs.

Cell Sizes
The size of cells in ALIEN can vary, and it influences several aspects of an organism's behavior

1. Small Cells Small cells tend to be more agile and can move quickly. They may also require less energy to replicate. However, they may have limited space for internal components and genetic information.
2. Large Cells Large cells can accommodate more internal components and genetic information. They may be more robust but could move more slowly due to their size and mass.
3. Intermediate Sizes Cells of intermediate sizes can offer a balance between agility and capacity. They provide enough space for internal complexity while still allowing for relatively efficient movement.

Designing for Rapid Evolution
If you want to design cells that are prone to evolve quickly with interesting results, consider the following strategies

1. Genetic Diversity Create cells with genetic diversity by introducing random variations or mutations in their genetic code. This diversity can lead to a wide range of traits and behaviors.
2. Dynamic Interactions Design cells that interact with each other and their environment in dynamic ways. Cells with communication abilities (e.g., Transmitter cells) can exchange information and energy, driving evolutionary changes.
3. Selective Pressure Introduce selective pressures in the environment, such as limited resources or competition with other organisms. This encourages cells to evolve traits that enhance their survival and reproduction.
4. Feedback Mechanisms Implement feedback mechanisms that reward certain behaviors or traits. Cells that exhibit desirable behaviors (e.g., efficient energy collection) can receive benefits, promoting the evolution of those behaviors.
5. Modularity Create cells with modular designs, allowing for the addition or removal of functional modules. This modularity enables cells to adapt by adding new features or discarding less useful ones.
6. Randomness Allow for randomness in cell behaviors or genetic mutations. Random events can lead to surprising and innovative solutions during evolution.

Remember that the pace and direction of evolution in ALIEN simulations can be influenced by the parameters you set, the environmental conditions, and the initial genetic makeup of the organisms. Experimentation and observation of evolving populations will help you fine-tune your design for interesting and rapid evolution.

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Available Cell Types

1. Neuron Cells

   • Structure Small network of 8 neurons with configurable weights and biases.
   • Function Process input from channels 0 to 7 using a sigmoid activation function.
   • Role Information processing and data transformation.

2. Transmitter Cells

   • Function Transport excess energy from the cell and connected cells to others nearby.
   • Activation No need for activation, but they transport activity states from input cells.
   • Role Energy distribution and support functions.

3. Constructor Cells

   • Function Build cell networks based on contained genomes.
   • Activation Can be triggered manually or automatically.
   • Energy Requirement Require energy for each new cell constructed.
   • Role Cellular construction and expansion.

4. Sensor Cells

   • Function Scan the environment for concentrations of cells of a specific color.
   • Activation Activated based on input from channel 0.
   • Outputs
     • Channel 0 Match detection (0 for no match, 1 for a match).
     • Channel 1 Density of the last match.
     • Channel 2 Distance to the last match.
     • Channel 3 Angle to the last match.
   • Role Environmental perception and data collection.

5. Nerve Cells

   • Function Forward activity states received as input from connected cells.
   • Activation Optionally generate activity pulses in channel 0 at regular intervals.
   • Role Information relay and trigger for other cells.

6. Attacker Cells

   • Function Attack surrounding cells from other networks by stealing energy.
   • Activation Activated based on input from channel 0.
   • Output Provides a value proportional to the gained energy.
   • Role Hostile actions against other cells.

7. Injector Cells

   • Function Override the genome of other constructor or injector cells.
   • Activation Activated based on input from channel 0.
   • Output Indicates injection progress (0 for no cells found, 1 for injection in process or completed).
   • Role Genetic manipulation of other cells.

8. Muscle Cells

   • Function Perform various movements and deformations based on input.
   • Inputs
     • Channel 0 Controls movement strength, bending, or expansion/contraction.
     • Channel 1 Controls acceleration due to bending.
     • Channel 3 Determines relative angle of movement.
   • Role Physical motion and deformation.

9. Defender Cells

   • Function Passive defense cells that reduce enemy attack strength involving attacker cells.
   • Activation No need for activation.
   • Role Defense against hostile actions.

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Distribution and Cell Execution Order (State)

Each cell is assigned an "execution number" ranging from 0 to 5. This number represents the time steps at which the cell's functions will be executed.
Cells with an execution number of 0 are executed at time points 0, 6, 12, 18, and so on...
Cells with execution numbers 1, 2, 3, 4, and 5 are executed with an offset of one time step each.

For example
Cell with execution number 0 is executed at time steps 0, 6, 12, 18, ...
Cell with execution number 1 is executed at time steps 1, 7, 13, 19, ...
Cell with execution number 2 is executed at time steps 2, 8, 14, 20, ...
And so on...

Input State
The input state of a cell is based on the activity states of all connected cells whose execution numbers match the input execution number of the current cell.
The activity states of these connected cells are summed up, and the result is set as the input state for the current cell. In other words,
if there's only one input cell, its activity is simply forwarded to the current cell.
This mechanism allows cells to collaborate and share information by synchronizing their execution times and sharing their activity states.

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Energy consumption and distribution

Energy consumption and distribution are critical aspects of the ALIEN simulation system, as they play a fundamental role in the behavior and evolution of artificial organisms. The simulator provides various mechanisms for adding energy points and defining energy zones, which can significantly influence the dynamics of the virtual ecosystem. Here's an explanation of energy consumption, distribution, and the impact of energy points and zones

Energy consumption refers to how cells expend or utilize energy in the simulation.
Cells in ALIEN require energy to perform various functions, such as movement, replication, and cell-to-cell communication.
Energy is essential for the survival and reproduction of artificial organisms. Here are some key points regarding energy consumption

1. Function Costs Each cell type in ALIEN may have different energy requirements for performing specific functions.
   For example, muscle cells may consume energy to produce movement, while constructor cells may expend energy to build new cells.
2. Replication Replicating cells require a substantial amount of energy.
   This energy investment is necessary for the creation of offspring cells.
3. Defense Defender cells may reduce the energy consumption of nearby cells by resisting attacks from foreign cells,
   effectively conserving energy for the organism.

Energy Distribution
Energy distribution involves how energy is allocated and shared within the simulated ecosystem.
ALIEN offers different ways for energy to flow and be distributed among cells. Here are two primary modes of energy distribution

1. Connected Cells Energy is distributed evenly across all cells that are directly connected to the cell generating or radiating energy.
   This distribution mode ensures that energy flows through the cellular network.
2. Transmitters and Constructors In this mode, energy is transferred primarily to nearby constructor or transmitter cells within the same cell network.
   This can lead to more efficient energy transfer over greater distances, especially from cells like attacker cells to constructor cells.

Energy Points and Zones
Energy points and zones are special features in the simulator that allow you to control and manipulate energy-related aspects of the environment

1. Energy Points Energy points are locations in the simulation where cells can collect or radiate energy.
   Cells near energy points can absorb excess energy, while those emitting energy points release energy into the environment.
   Energy points can influence the energy balance of nearby cells.

2. Energy Zones Energy zones are areas with specific settings that can modify the simulation environment.
   They can increase or decrease energy levels, modify mutation rates, or affect other environmental conditions. Energy zones can create gradients of energy availability, influencing where cells thrive or struggle to survive.

These energy-related features allow you to design complex ecosystems with varying energy landscapes, influencing the selection pressures on cells and their evolutionary trajectories. By strategically placing energy points and zones, you can shape the dynamics of the virtual world and observe how organisms adapt to changing energy conditions.
Experimenting with different energy distribution patterns, energy point placements, and energy zone settings can lead to fascinating insights into the evolution and behavior of artificial life forms within the ALIEN simulation.

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Special Sensors and Neuron Cells

Sensor and Neuron cells play critical roles in the ALIEN simulation and contribute to the evolution and mutation of cells in unique ways.
Let's explore these two cell types and their special abilities

Sensor Cell

1. Scanning the Environment
   Sensor cells are responsible for scanning their environment for concentrations of cells of a specific color. They are like the sensory organs of the organism.

2. Detection Criteria
   Sensor cells are dedicated to a specific color, and they can only detect cells that match that color. The sensitivity of the sensor can be adjusted using the "minimum density" parameter, which controls how many cells of the corresponding color need to be present for a detection to occur.

3. Output Information
   When a sensor cell detects cells of the specified color, it provides valuable information to the organism, including

   • Match Detection (Output Channel 0) It outputs a 1 when a match is detected and a 0 when there's no match.
   • Density of Match (Output Channel 1) It indicates the density or concentration of the detected cells.
   • Distance to Match (Output Channel 2) It provides the distance to the closest matching cell.
   • Angle to Match (Output Channel 3) It specifies the angle to the closest matching cell.

4. Evolutionary Role
   Sensor cells are crucial for the organism's ability to sense its environment and react to specific conditions. They enable the organism to adapt to different scenarios and evolve by detecting and responding to specific cues in its surroundings.

Neuron Cell

1. Neural Network
   Neuron cells are unique in that they have a small neural network embedded within them. This network consists of 8 neurons, each with 8x8 configurable weights and 8 bias values. These weights and biases determine how the neuron processes inputs and produces outputs.

2. Activation Function
   The neurons within a neuron cell use a sigmoid activation function to process inputs and generate outputs. The sigmoid function helps in nonlinear transformations of input data.

3. Input and Output
   Neuron cells can receive inputs from other cells and send outputs to other cells. They connect inputs and outputs onto their neural network. The configuration of these connections can be preset or randomized upon constructing a replicate of the neuron cell or its blueprint.

4. Evolutionary Role
   Neuron cells have a significant impact on the organism's ability to process information and make decisions. The configurable weights and biases in their neural networks can evolve over time through genetic mutations, allowing the organism to adapt to changing conditions and optimize its behavior.

Evolution and Mutation

• The configurable parameters within sensor cells and the neural networks of neuron cells provide opportunities for genetic mutations and evolution. Mutations can alter the sensitivity of sensors or the processing capabilities of neurons, leading to potentially improved or specialized functions.
• Over time, through genetic algorithms or other evolutionary mechanisms, cells with advantageous mutations may become more prevalent in the organism's population. This process of evolution enables the organism to adapt to its environment and improve its chances of survival and reproduction.

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"Time Steps Per Second" (TSP) and "Total Time Steps"

These play a crucial role in understanding the simulation's performance and stability. Here's an explanation of these terms

1. Time Steps Per Second (TSP)

   • Time Steps Per Second (TSP) is a measure of the simulation's speed and how many simulation steps or frames are processed per second. It represents the rate at which the simulation updates its state.
   • TSP is a key metric for monitoring the real-time performance of the simulation. It determines how fast or slow the simulation progresses.
   • Higher TSP values indicate that the simulation is running smoothly and processing a large number of time steps per second. Conversely, lower TSP values indicate a slower simulation.

2. Total Time Steps

   • Total Time Steps represents the cumulative number of time steps that the simulation has executed since it started.
   • This value provides an overview of the simulation's progress and how many time steps have been processed throughout its runtime.
   • Monitoring the Total Time Steps is essential for understanding the scale and duration of the simulation.

Interpreting TSP and Total Time Steps

• High TSP (e.g., 1000 TSP) indicates that the simulation is running smoothly, and each second, a significant number of time steps are processed, making it suitable for real-time observation and debugging.
• Low TSP (e.g., <100 TSP) suggests that the simulation is running slowly, which may be due to a complex or resource-intensive simulation. In such cases, the simulation may lag, making real-time observation challenging.
• Total Time Steps can be used to estimate the duration of the simulation. For example, if the Total Time Steps is 100,000, and the TSP is 100, it means that the simulation has been running for approximately 1,000 seconds (or around 16.7 minutes).
• If the Total Time Steps is increasing very slowly, it may indicate a performance issue or an inefficient configuration of the simulation. It's essential to keep an eye on this value to ensure that the simulation is progressing as expected.

Debugging and Performance Optimization

• Monitoring TSP and Total Time Steps is valuable for debugging and optimizing the simulation.
• If TSP drops significantly or Total Time Steps increases very slowly, it could be a sign that the simulation is experiencing performance problems, possibly due to complex interactions or resource limitations.
• In such cases, users should consider optimizing their simulation settings, such as reducing the number of cells, adjusting energy parameters, or simplifying the environment to improve performance.
• Frequent monitoring of these metrics can help users identify issues early and take corrective actions to maintain stable and efficient simulations.

Understanding TSP and Total Time Steps is essential for managing and troubleshooting ALIEN simulations, ensuring that they run smoothly and provide meaningful insights into the behavior and evolution of artificial organisms.

Predator Genome Example

A good base (Predator) design

Download (zipped genome file): alien_genome_predator.zip

It uses a neuron to produce a consistent input value from a set bias value, this signal is ideally a constant value of 1
once it outputs a 1 the sensor activates, and starts to produce the output values for our next phase inline.

After the sensor cell, two neurons are placed that take the data from the sensor cell and performs a function to control
the muscle groups on either side (hence the negative/positive sides), this is a base for a 'hunting' or 'attracting' effect.

Once the muscles receive that signal, they will start to engage according to their sensor output.
You can top it off with an attacker cell that also takes the same signal and will be activated,
but truth be told, you can activate any cell that requires an activation (muscles, sensors, attacker, etc)
using a neuron cell and set a bias value of 1 and link it, now its activated permanently.

(This is not my design, this design came straight from the community upload archives, if this is your design let me know and I will properly credit where credit is due.)

Foot note:
Everything in life can be broken down to a single function, and this principle is the key to unlocking the simplicity hidden within life's complexity, reminding us that even the most intricate puzzles can be solved with a dash of elegance and a sprinkle of understanding

[chrxh]

Cool idea!
I suppose ALIEN is too recent for ChatGPT.
Most of the GPT-text is based on the content of the 'Getting started' window, which provides a general overview.
The Petri net logic is no longer in use and the documentation (https://alien-project.gitbook.io/docs) is not up to date anymore. One would have to rewrite significant portions. Since it's a lot of work and not particularly enjoyable to write documentation, I decided to offer more in-program help and support. Currently implemented through numerous tooltips and the help window at program startup.