A.R.C.A.I - Adaptive Response Combat Artificial Intelligence Mod

[MON7RCH 5]

**Introducing ARCAI: Adaptive Response Combat AI**

 

Welcome to the development showcase of ARCAI (Adaptive Response Combat AI), an innovative mod for ARMA 3 designed to revolutionize AI behavior and enhance the overall gaming experience. ARCAI is currently in the **Alpha** development stage, and while a specific release date has not been set, I am committed to providing regular updates as development progresses. This project is a one-man effort, and as such, development may be slower than usual, but rest assured that the quality and functionality of the mod are being meticulously observed.

 

**Mod Overview:**

ARCAI utilizes novel AI techniques to significantly enhance enemy behavior, offering a dynamic and unpredictable combat environment for players. This mod is designed to cater specifically to mission creators by introducing a high degree of unpredictability in enemy actions while ensuring that the difficulty remains balanced and engaging. We understand that there may be other mods available that attempt to perform similar tasks. Our objective with ARCAI is to tackle these challenges with a different and novel approach, aiming to benefit the community with a fresh perspective and innovative solutions.

 

**Comprehensive List of Targeted Enhancements and Considerations:**

 

1. **Core Mechanics and Systems:**
   - **Learning Components:** Implementing Q-learning components for AI decision-making and updating functions to utilize these components.
   - **Behavior Trees and State Machines:** Utilizing behavior trees and state machines for more sophisticated AI behavior modeling.
   - **Version Tracking:** Keeping track of mod version and function revisions as updates and changes are made.
   - **Logging and Analytics:** Adding logging mechanisms to track AI performance and decision-making processes for further analysis and improvement.
   - **Oversight Prevention Measures:** Implementing comprehensive requirements gathering, thorough code review and comparison, enhanced communication, comprehensive testing and validation, version control and backup, detailed checklists, and final review and approval processes.

 

2. **AI Behavior and Tactics:**
   - **Adaptive Cover System:** AI units dynamically use cover based on the environment, enemy positions, and incoming fire.
   - **Advanced Communication:** AI units communicate more effectively, sharing information about enemy locations, movements, and strategic positions.
   - **Variable Engagement Ranges:** AI units adjust their engagement ranges based on weaponry, health, and situational awareness.
   - **Strategic Withdrawal:** AI units know when to retreat and regroup when facing overwhelming odds.
   - **Tactical Formations:** AI units use various formations based on the mission type and enemy threat level.
   - **Distraction and Deception Tactics:** AI units employ tactics to mislead and confuse the player, such as feigned retreats or ambushes.
   - **Flanking and Ambushing:** AI units use flanking maneuvers and ambush tactics to outmaneuver the player.
   - **Suppression Fire:** AI units use suppressive fire to pin down the player and reduce combat effectiveness.
   - **Enhanced Stealth Detection:** Improved detection capabilities make it harder for players to sneak past AI units.
   - **Objective-Based Behavior:** AI units prioritize mission objectives over simply engaging the player.
   - **Health and Resource Management:** AI units manage their health and resources, such as ammunition, to remain effective in combat.
   - **Morale and Fatigue System:** AI units have a morale and fatigue system affecting their performance based on combat stress and physical exhaustion.
   - **Persistent AI Memory:** AI units retain memory of previous encounters, adapting tactics based on past engagements.
   - **Prisoner Interrogation:** AI units can interrogate captured players for information, adding depth to hostage situations.
   - **Adaptive Behavior Based on Player Actions:** AI units adapt their behavior based on player actions, creating a dynamic and challenging environment.
   - **AI Personality Profiles:** Different personality profiles for AI units result in varied and unpredictable behavior patterns.

 

3. **Environmental Adaptation:**
   - **Dynamic Weather Adaptation:** AI units adapt tactics based on weather conditions, such as reduced visibility in fog or rain.
   - **Environment Interaction:** AI units interact with the environment, using buildings, vegetation, and terrain for strategic advantages.
   - **Time Period Adaptation:** The mod adapts to different time periods, adjusting equipment, factions, and tactics accordingly (e.g., no drones in the Cold War era).

 

4. **Optimization and Performance:**
   - **Resource Allocation Management:** Ensuring efficient use of computational resources, balancing load between server and client machines.
   - **Server and Client Optimization:** Optimizing the mod to minimize performance impact on both server and client machines.
   - **Enhanced Pathfinding Algorithms:** Improving pathfinding capabilities to navigate complex environments more effectively.

 

5. **Coordination and Integration:**
   - **Coordination with Vehicles and Aircraft:** AI units coordinate actions with supporting vehicles and aircraft for combined arms tactics.
   - **Support for Custom Scenarios and Mods:** Ensuring compatibility with other mods and custom scenarios, providing mission creators with more flexibility.
   - **Real-Time Strategy Integration:** Incorporating RTS elements to allow players to command AI units more effectively.
   - **Mission Editor Integration:** Integrating the mod's parameters and settings within the mission editor for easy customization by mission creators.
   - **Comprehensive Testing Framework:** Developing a robust testing framework to ensure all aspects of the mod function as intended and to facilitate future updates and enhancements.
   - **Community Feedback and Iteration:** Engaging with the community to gather feedback and iterating on the mod based on this feedback to improve quality and effectiveness.

 

6. **Usability and Customization:**
   - **Customizable Difficulty Settings:** Players can adjust AI difficulty to suit their preferences.
   - **Detailed README:** Comprehensive instructions for server administrators and players, covering installation, setup, and usage of the mod.
   - **User Interface Enhancements:** Improving the in-game UI for better control and feedback regarding AI behaviors and mod settings.

7. **Realistic Combat Mechanics:**
   - **Realistic Combat Mechanics:** Incorporating realistic combat mechanics, such as ballistics and cover penetration, to enhance immersion and challenge for players.

 

**Future Plans:**

The development of ARCAI is ongoing, with plans to integrate additional features and enhancements based on community feedback and testing results. The mod will be posted on the Steam Workshop for players to test, and we encourage feedback to help refine and improve the experience. We are committed to providing regular updates and keeping the community informed about progress.

 

Stay tuned for more updates and thank you for your support!

---

I am an ARMA 3 enthusiast with a passion for cooperative gameplay, having been actively involved with members of Seal Team 3 MilSim (callsign MONARCH). My appreciation for the game's immersive experience and its unpredictability has led me to embark on a project aimed at enhancing the native enemy AI. Recognizing that the current AI could benefit from improvements, I am dedicated to addressing this challenge to elevate the overall gaming experience.

[froggyluv 2136]

Sounds overwhelmingly good -but gotta be honest -is this a pipe dream wishlist orfeatures youve actually completed on some level because thats a monumental amount of work

[MON7RCH 5]

Hello @froggyluv, thanks for your post.

 

At this stage, the list is more of a development plan. I have created some of the core functions but more are still needed. I won't post anything on the steam workshop until I have a working model that I am comfortable sharing without having it blow back in my face. After all, I want to make the journey enjoyable for everyone.

 

My approach is simple, build the core then start building on it, so far, am doing a pretty good job 🙂

[MON7RCH 5]

Development status update:
 

1. Core Mechanics and Systems:
   - Q-learning Algorithm: Partially implemented 
   - Experience Replay: Partially implemented 
   - Behavior Trees: Basic implementation 
   - Finite State Machine: Implemented 
   - Logging System: Implemented 
   - Version Tracking: Not yet implemented
   - Oversight Prevention Measures: Not yet implemented

    

2. AI Behavior and Tactics:
   - Adaptive Cover System: Basic implementation 
   - Advanced Communication: Not yet implemented
   - Variable Engagement Ranges: Implemented 
   - Strategic Withdrawal: Basic implementation 
   - Tactical Formations: Not yet implemented
   - Distraction and Deception Tactics: Not yet implemented
   - Flanking and Ambushing: Not yet implemented
   - Suppression Fire: Not yet implemented
   - Enhanced Stealth Detection: Not yet implemented
   - Objective-Based Behavior: Not yet implemented
   - Health Management: Implemented using ACE medical system
   - Morale and Fatigue System: Not yet implemented
   - Persistent AI Memory: Not yet implemented
   - Prisoner Interrogation: Not yet implemented
   - Adaptive Behavior Based on Player Actions: Not yet implemented
   - AI Personality Profiles: Not yet implemented
   - Team Coordination: Basic implementation 

 

3. Environmental Adaptation:
   - Dynamic Weather Adaptation: Basic implementation 
   - Environment Interaction: Not yet implemented
   - Time Period Adaptation: Not yet implemented

 

4. Optimization and Performance:
   - Resource Allocation Management: Not yet implemented
   - Server and Client Optimization: Not yet implemented
   - Enhanced Pathfinding Algorithms: Not yet implemented
   - Basic performance monitoring: Implemented

 

5. Coordination and Integration:
   - ACE Medical Integration: Implemented
   - CBA Framework Usage: Partially implemented
   - Coordination with Vehicles and Aircraft: Not yet implemented
   - Support for Custom Scenarios and Mods: Not yet implemented
   - Real-Time Strategy Integration: Not yet implemented
   - Mission Editor Integration: Basic implementation (module for setting ARCAI-controlled units)
   - Comprehensive Testing Framework: Not yet implemented
   - Community Feedback and Iteration: Not yet implemented

 

6. Usability and Customization:
   - Customizable Difficulty Settings: Not yet implemented
   - Detailed README: Not yet implemented
   - User Interface Enhancements: Not yet implemented

 

7. Realistic Combat Mechanics:
   - Advanced ballistics, weapon handling, and damage models: Not yet implemented

 

8. Error Handling and Robustness:
   - Basic error handling and input validation: Implemented 

 

Development Status Summary:
- Implemented: Basic core systems (FSM, logging), some fundamental AI behaviors, and ACE medical integration
- Partially Implemented: Learning components (Q-learning, experience replay), CBA framework usage, some tactical behaviors
- Basic Implementation: Behavior trees, adaptive cover, strategic withdrawal, team coordination, dynamic weather adaptation
- Not Yet Implemented: Most advanced features, complex behaviors, and many proposed enhancements including oversight prevention measures, advanced AI tactics, environmental interactions, optimization features, and realistic combat mechanics.

 

At this stage i will re focus on core functionality before additional enhancements.

 

 

[rautamiekka 15]

This is definitely a very massive project. I'm surprised you've already done a lotta work on the "core mechanics and systems" section, some of which I haven't even heard of.

 

You're gonna need a whole team on this cuz I know from having helped with the partial rival AI project LAMBS Danger that you're looking at such a problematic project that you'll need more than the like 2-3 ppl that they have, who I think work on it for many hours daily and yet take a while understanding the quirks of 'Arma 3'. I'm sure you already know the game has so many of them that it's almost not even funny.

 

And then there's ACE3, which has done the "realistic combat mechanics" part well for so many years, so this'd partially overlap with it.

 

But if you can make this happen, I'll absolutely be trying it 👍

[froggyluv 2136]

One thing I didnt notice and maybe you already have it planned is the Skill level or type of troop. Meaning, all troops "feel" the same in Vanilla all running around with their scopes up with a few guys sent to flank. Maybe scale the degree of effectiveness to Spec Ops as compared to a totally untrained Militia

[MON7RCH 5]

23 minutes ago, froggyluv said:

One thing I didnt notice and maybe you already have it planned is the Skill level or type of troop. Meaning, all troops "feel" the same in Vanilla all running around with their scopes up with a few guys sent to flank. Maybe scale the degree of effectiveness to Spec Ops as compared to a totally untrained Militia

Will surely add that on my list

[MON7RCH 5]

1 hour ago, rautamiekka said:

This is definitely a very massive project. I'm surprised you've already done a lotta work on the "core mechanics and systems" section, some of which I haven't even heard of.

 

You're gonna need a whole team on this cuz I know from having helped with the partial rival AI project LAMBS Danger that you're looking at such a problematic project that you'll need more than the like 2-3 ppl that they have, who I think work on it for many hours daily and yet take a while understanding the quirks of 'Arma 3'. I'm sure you already know the game has so many of them that it's almost not even funny.

 

And then there's ACE3, which has done the "realistic combat mechanics" part well for so many years, so this'd partially overlap with it.

 

But if you can make this happen, I'll absolutely be trying it 👍

 

You are correct on both fronts; I am using AI tools to help me code quicker since I am not a professional coder. This is more of a side project for me with no release time commitment.  If I succeed, great; if i don't, no harm is done.  On another note, I already have ACE3 and CBA as required dependencies to make life easy for me. I am aware of LAMBS, while it has some good functionalities, it is a bit different from what I have ventured to create.

[MON7RCH 5]

Having considered the feedback I was given here and detailed review of available mods, I have decided to take ARCAI mod in a slightly different direction.  The direction that ARCAI will take is summarized in the plan below.

 

ARCAI Development Plan - Updated 13AUG2024

Objective:
ARCAI is a groundbreaking AI enhancement for ARMA 3, designed to serve as the "brain" for NPCs, enabling them to learn, adapt, and grow in experience over time. By integrating seamlessly with existing mods and being adaptable for future expansions, ARCAI aims to make AI smarter, more effective, and resilient.

 

Key Features:

Reinforcement Learning:
ARCAI implements a learning engine that continuously improves AI tactics and decision-making based on engagement outcomes.

 

Memory and Experience Growth:
AI units grow in experience over time, with knowledge retained across missions, making them increasingly effective and adaptive.

 

Modular and Extensible Framework:
Designed with a plugin-based architecture, ARCAI allows for easy integration of current and future mods, ensuring compatibility and enhancing their functionality.

 

Dynamic Decision-Making:
AI units assess threats, environments, and mission objectives in real-time, making strategic decisions that align with both immediate and long-term goals.

 

ALiVE Integration:
ARCAI leverages ALiVE’s persistent campaign features to create a dynamic environment where AI can evolve across multiple engagements.

 

Accessible and Transferable Experience Data:
ARCAI ensures that accumulated AI experience is accessible for review and can be transferred in case of hardware changes, game reinstallation, or upgrades, protecting the user’s investment in AI development.

 

Development Phases:

Core Integration:

Integration with existing mods like LAMBS Danger.fsm, ASR AI, ACE, CUP,  and ALiVE, ensuring seamless tactical execution and environmental realism.

 

Implementation of Core Functions:

Development of ARCAI’s reinforcement learning, memory systems, and dynamic decision-making capabilities.

 

Testing and Optimization:

Comprehensive testing for mod compatibility and iterative refinement of AI behavior to ensure smooth performance and seamless integration.

 

Final Integration and Deployment:

Completion of the integration process, followed by a final round of testing and public release, with ongoing support and updates.

 

Future-Proofing:

Continuous development of custom behavior trees, advanced AI memory functions, and open-source collaboration with the modding community.

 

Conclusion:
ARCAI represents a significant leap forward in AI development for ARMA 3, providing a smarter, more adaptive, and resilient AI experience. Whether you’re a mission maker or a player, ARCAI ensures that AI units continue to challenge, adapt, and evolve, making each engagement unique and immersive.

 

Stay tuned for more updates as ARCAI continues to evolve!

[MON7RCH 5]

ARCAI Development Plan - Update 27 AUG 2024
 

1. Enhance Reward Signals:

Status: Partially Completed

Details: The current implementation includes basic reward calculation mechanisms, but more detailed and nuanced reward structures could be introduced. This would allow the AI to differentiate between varying degrees of success and failure, leading to more effective learning.

Further Enhancement Needed: Yes. Additional layers of reward signals and more detailed feedback loops could be integrated.

 

2. Integrate with Decision-Making:

Status: Completed

Details: The learned behaviors are directly influencing decision-making processes in the behavior tree and other decision-making structures, such as adaptive cover and strategic withdrawal.

Further Enhancement Needed: No. The integration appears robust, but continuous testing is necessary to ensure optimal performance.

 

3. Dynamic Decision-Making:

Improve Behavior Tree Flexibility:

Status: Partially Completed

Details: The behavior tree has been enhanced with mechanisms for dynamic adaptation. However, there’s room for more flexibility based on real-time data.

Further Enhancement Needed: Yes. Additional logic could be added to allow the tree to reconfigure itself dynamically based on new information learned by the AI.

 

Contextual Adaptation:

Status: Completed

Details: The decision-making process has been improved to incorporate environmental and situational context, ensuring that the AI adapts to the current environment, resources, and historical data.

Further Enhancement Needed: No. The current adaptation mechanisms are effective.

 

4. Enhance Adaptation and Recall:

Memory System:

Status: Not Yet Completed

Details: While some basic recall mechanisms are in place, a more developed memory system that allows the AI to recall and apply past experiences in similar situations is still needed.

Further Enhancement Needed: Yes. Implementation of a dedicated memory system integrated with hierarchical learning is required.

 

Real-Time Adaptation:

Status: Completed

Details: The AI is capable of adapting in real-time based on evolving scenarios, adjusting tactics accordingly.

Further Enhancement Needed: No. This aspect is well-implemented.

 

5. Skill Acquisition and Evolution:

Complex Skill Development:

Status: Partially Completed

Details: The skill acquisition process has been expanded, but further development of more complex and evolving skills can enhance the AI’s proficiency over time.

Further Enhancement Needed: Yes. More sophisticated algorithms for skill development and evolution could be added.

 

Skill Application:

Status: Completed

Details: The AI effectively applies acquired skills in relevant situations, improving its overall effectiveness.

Further Enhancement Needed: No. This functionality is solid and well-integrated.

 

6. Optimization of AI Updates:

Streamline Update Processes:

Status: Completed

Details: The fn_UpdateAI.sqf has been optimized to ensure efficient updates, reducing potential performance issues.

Further Enhancement Needed: No. The update processes are streamlined effectively.

 

Cross-Component Integration:

Status: Completed

Details: The updates are well-integrated across different AI components, maintaining consistency in AI behavior.

Further Enhancement Needed: No. The cross-component integration is functioning as intended.

[john111 76]

This integration of how to make the AI learn,can this be added to it`s flying behavior too?   To make it fly like in a old Vietnam-war movie,and use rapelling and all that based on number of times of success previous. It would learn zones with labels and use patterns like the RTS Mod,I just Control+Mouse and set a FlyHeight in meters,and it will remember if it crashed somewhere and avoid pos_X if so.

 

Cesar (air rescue) would be possible with this ,even directing Player / AI  it is to hot a LZ.   Would it be possible to  add a Flight School feature or even the Alexa/Cloude AI if it can somehow analyze a video and copy how the helicopters would land and so on?   

 

Can it somehow copy the way the helicopters fly in Arma Reforger?  Or use the AI to write a FSM and Point/Drag in map ?  Or simply copy the helicopters from Reforger?  

 

Use Take ON Helicopters FSM+ Reforgers FSM?    

 

 - Coordination with Vehicles and Aircraft  :  Could it be possible to Cesar with Combined Arms High Command to make small troops to be able to stay hidden and find and retrive and hit and run -type missions . 

[john111 76]

// Initiera helikoptern
_helicopter = createVehicle ["B_Heli_Transport_01_F", getMarkerPos "start", [], 0, "FLY"];
_helicopter setVehicleVarName "myHeli";
myHeli = _helicopter;

// Styr helikoptern
_myPilot = createVehicleCrew _helicopter;
_myPilot moveInDriver _helicopter;

// Flyg till en specifik position
_targetPos = getMarkerPos "target";
_myPilot doMove _targetPos;
 

 

// Skapa helikoptrar
_heli1 = createVehicle ["B_Heli_Transport_01_F", getMarkerPos "start1", [], 0, "FLY"];
_heli2 = createVehicle ["B_Heli_Transport_01_F", getMarkerPos "start2", [], 0, "FLY"];

// Skapa besättningar
_crew1 = createVehicleCrew _heli1;
_crew2 = createVehicleCrew _heli2;

// Tilldela piloter
_crew1 select 0 moveInDriver _heli1;
_crew2 select 0 moveInDriver _heli2;
 

// Definiera landningspositioner
_landPos1 = getMarkerPos "land1";
_landPos2 = getMarkerPos "land2";
 

// Flyg till landningspositioner
_heli1 doMove _landPos1;
_heli2 doMove _landPos2;
 

 

// Skapa vakter
_guard1 = createGroup west;
_guardUnit1 = _guard1 createUnit ["B_Soldier_F", _landPos1, [], 0, "FORM"];
_guardUnit2 = _guard1 createUnit ["B_Soldier_F", _landPos2, [], 0, "FORM"];

// Tilldela vakter att skydda området
_guardUnit1 doMove _landPos1;
_guardUnit2 doMove _landPos2;
 

 

// Skapa en trigger för att starta räddningsuppdraget
_trigger = createTrigger ["EmptyDetector", getMarkerPos "rescueStart"];
_trigger setTriggerArea [50, 50, 0, false];
_trigger setTriggerActivation ["WEST", "PRESENT", true];
_trigger setTriggerStatements ["this", "{_x doMove (getMarkerPos 'rescueTarget')} forEach units _guard1;", ""];
 

// Definiera landningspositioner
_landPos1 = getMarkerPos "land1";
_landPos2 = getMarkerPos "land2";
_landPos3 = getMarkerPos "land3";

// Definiera formation (triangel)
_formation = [_landPos1, _landPos2, _landPos3];
 

 

// Flyg till landningspositioner
{
    _heli = _x select 0;
    _pos = _x select 1;
    _heli doMove _pos;
} forEach [[_heli1, _landPos1], [_heli2, _landPos2], [_heli3, _landPos3]];
 

 

// Skapa vakter
_guardGroup = createGroup west;
_guardUnits = [
    _guardGroup createUnit ["B_Soldier_F", _landPos1, [], 0, "FORM"],
    _guardGroup createUnit ["B_Soldier_F", _landPos2, [], 0, "FORM"],
    _guardGroup createUnit ["B_Soldier_F", _landPos3, [], 0, "FORM"]
];

// Tilldela vakter att skydda området
{
    _unit = _x;
    _unit doMove (getPos _unit);
    _unit setCombatMode "RED";
    _unit setBehaviour "COMBAT";
} forEach _guardUnits;
 

// Skapa en trigger för att starta räddningsuppdraget
_trigger = createTrigger ["EmptyDetector", getMarkerPos "rescueStart"];
_trigger setTriggerArea [50, 50, 0, false];
_trigger setTriggerActivation ["WEST", "PRESENT", true];
_trigger setTriggerStatements ["this", "{_x doMove (getMarkerPos 'rescueTarget')} forEach units _guardGroup;", ""];
 

 

// Landning och evakuering
{
    _heli = _x;
    _heli land "LAND";
    sleep 10; // Vänta på att helikoptern landar
    _heli action ["getIn", _pilot]; // Evakuera piloten
} forEach [_heli1, _heli2, _heli3];
 

 

[Rydygier 1317]

 

On 8/27/2024 at 10:55 PM, MON7RCH said:

: The current implementation includes basic reward calculation mechanisms, but more detailed and nuanced reward structures could be introduced. This would allow the AI to differentiate between varying degrees of success and failure, leading to more effective learning.

Interesting, can you share more details, how exactly learning works here, what the exact principles are (if not the secret)?

[rautamiekka 15]

15 hours ago, john111 said:

    _heli = _x select 0;
    _pos = _x select 1;
 

 

Don't use `select` like this, use `params`.

[MON7RCH 5]

On 9/5/2024 at 4:04 AM, john111 said:

This integration of how to make the AI learn,can this be added to it`s flying behavior too?   To make it fly like in a old Vietnam-war movie,and use rapelling and all that based on number of times of success previous. It would learn zones with labels and use patterns like the RTS Mod,I just Control+Mouse and set a FlyHeight in meters,and it will remember if it crashed somewhere and avoid pos_X if so.

 

Cesar (air rescue) would be possible with this ,even directing Player / AI  it is to hot a LZ.   Would it be possible to  add a Flight School feature or even the Alexa/Cloude AI if it can somehow analyze a video and copy how the helicopters would land and so on?   

 

Can it somehow copy the way the helicopters fly in Arma Reforger?  Or use the AI to write a FSM and Point/Drag in map ?  Or simply copy the helicopters from Reforger?  

 

Use Take ON Helicopters FSM+ Reforgers FSM?    

 

 - Coordination with Vehicles and Aircraft  :  Could it be possible to Cesar with Combined Arms High Command to make small troops to be able to stay hidden and find and retrive and hit and run -type missions . 

Very interesting idea; however, it is out of the scope of current development. Nevertheless, i will add it to the whiteboard.

[MON7RCH 5]

On 9/5/2024 at 5:53 AM, Rydygier said:

 

Interesting, can you share more details, how exactly learning works here, what the exact principles are (if not the secret)?

 

The **Adaptive Core AI System** in **ARCAI** enhances AI behavior by using advanced learning and decision-making processes. Here’s a concise breakdown of the key technical features:

 

1. **Learning & Adaptation**:
   - **Reinforcement Learning**: AI learns from previous actions through Q-learning, storing outcomes in a Q-table to improve future decisions.
   - **Experience Replay**: AI revisits past encounters to reinforce learning and better adapt to similar situations in the future.

 

2. **Dynamic Behavior**: (basic behavior)
   - **Adaptive Cover**: AI evaluates surroundings in real-time to find cover based on learned behavior.
   - **Strategic Withdrawal**: AI retreats when outmatched, using past experiences to judge when to withdraw.
   - **Engagement Range**: AI adjusts engagement distances based on learned encounters and tactical advantage.

 

3. **Team Coordination**:
   - **Shared Learning**: AI units share information with teammates, improving group tactics like flanking or engaging threats.
   - **Coordinated Actions**: Teams make strategic decisions based on shared learned experiences.

 

4. **Environment Awareness**:
   - **Terrain Analysis**: AI continuously evaluates terrain to optimize positioning for combat or defense.
   - **Dynamic Reactions**: AI adjusts behavior in real-time based on changes in the environment like weather or enemy movements.

 

5. **Custom Integration**:
   - **Modular Design**: The objective is to have ACRACI integrate with vanilla ARMA function in addition to other third-party mods.
   - **Behavior Trees and FSMs**: Governs AI decision-making through behavior trees and state machines, adaptable for mission-specific scenarios.

 

This system allows AI to learn, adapt, and improve over time, creating more intelligent and challenging enemies in ARMA 3.

 

Progress has been slow; I am hitting many roadblocks within ARMA engine and the nuances required to make things work. Will have a more comprehensive update soon(ish).

[Kosmo_ 39]

fascinating, what are the reward conditions for the tables? Kills/survival time/damage? Do you store and use position data, wouldn't that be map specific?