Core War Techniques and Their Role in Shaping Self-Adaptive Neural Networks

Core War is a programming game where two or more programs, called "warriors," written in the specialized language Redcode, battle for control over a virtual computer. Redcode, a simplified assembly language, allows programmers to write code that occupies, defends, or attacks memory locations within a shared environment. The objective is for warriors to eliminate opponents by overwriting or altering their instructions while keeping their own processes running for as long as possible. These warriors execute in a cyclic manner on a virtual machine called MARS (Memory Array Redcode Simulator), which continuously executes the code in the shared memory until only one program remains functional.

In Core War, warriors implement various strategies like replication, targeted attacks, scanning for opponents, and even self-repair to stay alive. The coding techniques involve self-modifying and self-replicating code, where warriors often adapt their own behavior dynamically to respond to threats or maximize their survival. Some warriors employ "scanners" to locate opponents in memory and launch attacks, while others use "bombers" to disrupt large regions of the memory indiscriminately. This dynamic competition requires clever use of Redcode's instructions, such as different addressing modes (direct, indirect, and relative) to manage memory interactions. Programs evolve to become more resilient by combining offense and defense tactics, leading to an engaging and unpredictable battle environment.

The concepts from Core War—self-replication, self-modification, adaptive strategies, and competitive evolution—are highly applicable to evolving artificial neural networks (ANNs). Techniques like redundancy, dynamic behavior adjustments, and even adversarial competition are key elements that could help ANN structures evolve in a robust and adaptive way. The game’s use of modularity, replication, and continual adaptation provides a unique perspective on how evolving neural networks could be designed to be more self-sustaining and resilient in the face of changing environments, much like the strategies used by successful warriors in Core War.

Below, I'll outline techniques from Core War that can potentially inspire or directly inform approaches to evolving neural networks:

1. Self-Replication (Copying Code)

• Core War Technique: One of the key techniques in Core War is "replication," where warriors replicate their own instructions throughout the memory to increase survival chances.
• Application to ANNs: This concept can be implemented as a mechanism for neural module replication—individual subnetworks or sets of neurons could replicate and be distributed throughout the evolving model. This might facilitate redundancy, robustness, and the possibility of one subnet being modified independently to perform specialized tasks. You could implement "copy" operations as part of a genetic algorithm or a reinforcement learning mechanism that identifies effective subnetworks and duplicates them.

2. Mutation and Self-Modification

• Core War Technique: Warriors use self-modifying code to adjust their behavior dynamically based on their environment, often altering instructions to respond to opponents.
• Application to ANNs: Self-modifying weights could be applied to ANNs where, instead of static weights, certain connections between neurons could change dynamically based on the input data or even other layers in the network. This would require implementing meta-learning techniques that allow for weights to evolve over time in response to environmental feedback, similar to evolutionary algorithms that modify themselves for higher fitness.

3. Scanners and Probes

• Core War Technique: Scanners are warriors that search for their opponents’ code in the memory and then launch targeted attacks. They represent a more efficient, adaptive strategy compared to randomly bombing memory cells.
• Application to ANNs: This can translate into adaptive exploration strategies for ANNs, where the model searches for promising directions to evolve toward by exploring subsets of the solution space. An evolving ANN could have modules that act as "scanners"—actively exploring potential inputs or new architectures to adapt to specific tasks in a more targeted way, much like how adversarial networks find weaknesses.

4. Bombing (Creating Disruption)

• Core War Technique: Bombers leave destructive instructions in memory, making it more difficult for other warriors to run successfully.
• Application to ANNs: The concept of injecting controlled disruptions could be useful when designing neural networks that need to avoid overfitting or to promote robustness. Injecting "noise" or random disruptions into the network might facilitate more generalized learning. This idea can be extended to add adversarial regularization to the evolving ANN so that it becomes robust against unpredictable perturbations.

5. Imps (Cycle-Based Survival)

• Core War Technique: Imps are minimalistic programs that move cyclically in memory, designed to be hard to catch and destroy.
• Application to ANNs: You can create minimal, cyclic neural components that replicate over time and maintain a minimal state while facilitating core functions, similar to how reservoir computing models work. This type of subnetwork could serve as an always-active component of the ANN, maintaining essential behaviors or monitoring the environment for external triggers.

6. Competitive Coevolution

• Core War Technique: Warriors coevolve against each other, improving as they face more challenging opponents. This generates an arms race where programs are constantly improving to keep up with each other.
• Application to ANNs: Implementing competitive coevolution between neural networks or between different subnetworks might help evolve highly adaptive ANNs. For example, introducing adversarial subnetworks that try to disrupt or challenge the current network's training (similar to GANs) could facilitate more diverse, adaptive growth. These competitive pressures will force the self-replicating ANNs to adapt to increasingly challenging environments, ultimately producing a more capable model.

7. Addressing Modes (Direct, Indirect, Relative)

• Core War Technique: Redcode allows warriors to use different addressing modes, such as direct, indirect, and relative addressing to access memory. This allows for sophisticated and flexible control over memory.
• Application to ANNs: Dynamic addressing in the neural architecture can be related to the idea of dynamic routing of signals through the network. This can evolve the architecture to learn how to best utilize its own components (neurons/layers) by controlling where signals are routed based on the current state or input. Techniques similar to dynamic routing in capsule networks could be used where nodes evolve to optimally pass information to others.

8. Redundancy as a Survival Mechanism

• Core War Technique: Many successful warriors create redundant versions of themselves in memory so that even if part of their code is overwritten or damaged, other parts can continue to function.
• Application to ANNs: Encouraging redundant connections and structures within evolving neural networks could help enhance fault tolerance and robustness. You could add redundancy in such a way that different subnetworks carry out similar computations. If one part is damaged or pruned during evolution, others could maintain functionality.

9. Modular Code and Task-Specific Warriors

• Core War Technique: Some warriors employ a modular design, with separate parts handling different tasks like scanning, bombing, or self-repair.
• Application to ANNs: Modular neural evolution is analogous to having specialized components in the ANN for different functions, allowing parts of the network to specialize. This could translate into submodules in the neural network, each with specialized tasks like feature detection, anomaly detection, or reinforcement learning. This kind of modularity can evolve more complex behaviors over time.

10. Offensive and Defensive Mechanisms

• Core War Technique: Warriors often implement a combination of offensive and defensive mechanisms—some scan for opponents, others replicate defensively, and some attack once they locate their targets.
• Application to ANNs: The idea of combining offensive (active learning) and defensive (robustness) mechanisms in neural evolution is key to creating resilient self-replicating networks. Offensive mechanisms can include seeking out the most informative data to train on, while defensive mechanisms involve adaptation to avoid catastrophic forgetting or data poisoning attacks.

Integrating Core War Principles into Self-Replicating ANNs

To implement these principles, you could follow an evolutionary algorithm approach:

1. Start with Population Initialization: Begin with a population of simple neural networks.
2. Self-Modification Operations: Allow the networks to modify their own weights, topologies, or even the number of neurons.
3. Replication and Mutation: Introduce copying and mutation rules where successful subnetworks can copy themselves with some form of mutation (e.g., altered weights, added/deleted neurons).
4. Fitness Evaluation in Dynamic Environments: Use an environment where each ANN competes against others or has to solve dynamic tasks, similar to how warriors compete in Core War.
5. Competitive Coevolution: Allow networks to compete, where the fitness of a network is partially determined by its performance against other networks in the environment.

These techniques should help evolve a more adaptive, robust, and self-sustaining neural architecture, capable of dynamically modifying itself to meet the needs of a changing environment, much like the warriors in Core War dynamically modify themselves to stay ahead of their competitors.