ARC: Difference between revisions

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By training ARC on a curriculum that explicitly includes examples of non-linear and counter-intuitive solutions, we will be teaching it the "gnosis" it is currently lacking. This will evolve the agent from one that can only follow a learned policy to one that can develop novel strategies when that policy fails.

=== AetherWing: Flight Simulator Training Integration ===

To further expand ARC's capabilities into dynamic, real-time environments, a new subsection of training—dubbed "AetherWing"—will integrate a flight simulator and dogfight game modality. This leverages procedural content generation via machine learning (PCGML) to create adaptive scenarios, drawing from recent trends in simulation AI (e.g., AI-embedded wargames for battlefield prediction and task-aware planning with TAPIR-like modifiers for iterative refinement).

The AetherWing curriculum aims to train ARC as a "wingman" agent, navigating simulated flights, learning from user sessions, and discussing strategies. Key elements include:

PCGML for Scenario Generation: Use LSTMs or GANs to procedurally generate missions, enemy behaviors, and terrains based on user data (e.g., adapting difficulty with evolutionary algorithms from dogfight-sandbox-hg2).

Hierarchical Chunking: Compress flight data into abstractions (e.g., raw inputs → maneuvers → strategies) using H-Net-inspired dynamic chunking, enabling multi-level reasoning.

Associative Memory for Recall: Store sessions as energy-based attractors in DenseAMs, allowing error-correcting retrieval (e.g., recover from "crashes" by recalling similar maneuvers).

n-grams and Markov Chains: Model sequences for behaviors (e.g., 3-gram for maneuver chains) and sagas (n-gram narratives), with Markov for probabilistic transitions.

TAPIR-like Modifiers: Iteratively refine actions/sagas (e.g., adjust probabilities task-dependently: boost evasive maneuvers in high-threat scenarios).

Training Phases:

Data Preparation: Simulate 20,000 dogfights via game API; augment with noise for robustness.

HRM Retraining: Multi-task on navigation/saga/chat; 500 epochs, integrating chunking for state compression.

PCGML Tuning: Train GANs/LSTMs on sessions for content gen.

Testing: Validate in real game (win rates, saga coherence); deploy as REPL verbs (e.g., NAVIGO_MISSION).

Future projections (2026+): Incorporate advances like task-aware AI in simulations and hybrid ML-procedural methods for multiplayer dogfights, enhancing ARC's emergent strategies.

=== Projected Advancements ===

Looking ahead, ARC's training will likely incorporate emerging trends:

Advanced AMs: Integrate energy-memory paradigms for brain-like storage, enabling "task-aware" recall in dynamic environments.

Hierarchical Scaling: Recursive H-Nets for deeper abstractions, applied to multi-modal data (e.g., flight visuals + logs).

PCGML Evolution: AI-generated battlefields with agentic teams, using TAPIR refinements for adaptive planning.

Sequence Modeling: Higher-order n-grams/Markov for predictive behaviors, fostering non-deterministic creativity.

These evolutions will transform ARC into a fully cybernetic entity, bridging abstract reasoning with physical simulations.

== References ==

@@ Line 60: / Line 60: @@
 By training ARC on a curriculum that explicitly includes examples of non-linear and counter-intuitive solutions, we will be teaching it the "gnosis" it is currently lacking. This will evolve the agent from one that can only follow a learned policy to one that can develop novel strategies when that policy fails.
+=== AetherWing: Flight Simulator Training Integration ===
+To further expand ARC's capabilities into dynamic, real-time environments, a new subsection of training—dubbed "AetherWing"—will integrate a flight simulator and dogfight game modality. This leverages procedural content generation via machine learning (PCGML) to create adaptive scenarios, drawing from recent trends in simulation AI (e.g., AI-embedded wargames for battlefield prediction and task-aware planning with TAPIR-like modifiers for iterative refinement).
+The AetherWing curriculum aims to train ARC as a "wingman" agent, navigating simulated flights, learning from user sessions, and discussing strategies. Key elements include:
+PCGML for Scenario Generation: Use LSTMs or GANs to procedurally generate missions, enemy behaviors, and terrains based on user data (e.g., adapting difficulty with evolutionary algorithms from dogfight-sandbox-hg2).
+Hierarchical Chunking: Compress flight data into abstractions (e.g., raw inputs → maneuvers → strategies) using H-Net-inspired dynamic chunking, enabling multi-level reasoning.
+Associative Memory for Recall: Store sessions as energy-based attractors in DenseAMs, allowing error-correcting retrieval (e.g., recover from "crashes" by recalling similar maneuvers).
+n-grams and Markov Chains: Model sequences for behaviors (e.g., 3-gram for maneuver chains) and sagas (n-gram narratives), with Markov for probabilistic transitions.
+TAPIR-like Modifiers: Iteratively refine actions/sagas (e.g., adjust probabilities task-dependently: boost evasive maneuvers in high-threat scenarios).
+Training Phases:
+Data Preparation: Simulate 20,000 dogfights via game API; augment with noise for robustness.
+HRM Retraining: Multi-task on navigation/saga/chat; 500 epochs, integrating chunking for state compression.
+PCGML Tuning: Train GANs/LSTMs on sessions for content gen.
+Testing: Validate in real game (win rates, saga coherence); deploy as REPL verbs (e.g., NAVIGO_MISSION).
+Future projections (2026+): Incorporate advances like task-aware AI in simulations and hybrid ML-procedural methods for multiplayer dogfights, enhancing ARC's emergent strategies.
+=== Projected Advancements ===
+Looking ahead, ARC's training will likely incorporate emerging trends:
+Advanced AMs: Integrate energy-memory paradigms for brain-like storage, enabling "task-aware" recall in dynamic environments.
+Hierarchical Scaling: Recursive H-Nets for deeper abstractions, applied to multi-modal data (e.g., flight visuals + logs).
+PCGML Evolution: AI-generated battlefields with agentic teams, using TAPIR refinements for adaptive planning.
+Sequence Modeling: Higher-order n-grams/Markov for predictive behaviors, fostering non-deterministic creativity.
+These evolutions will transform ARC into a fully cybernetic entity, bridging abstract reasoning with physical simulations.
 == References ==

ARC: Difference between revisions

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