Artificial Mind in the Game: Implementation

Practical Artificial Mind System: Minimal Implementation in a Game

The artificial mind system is implemented in a simple shooter game to clearly demonstrate core functions: perception, memory, process classification, and action synthesis. The agent (learning subject) avoids projectiles fired by a hunter controlled by the user. Both entities move along parallel lines: the hunter freely left or right, while the agent can take up to three steps per turn.

The world consists of four objects: the agent, the hunter, the gun, and the projectile. World perception is represented as a list of ~30 parameters (coordinates, projectile type, etc.), forming the state. Any change in these parameters registers as a new state, and a sequence of states forms a process.

Knowledge Generation Through Classification

Process classification transforms sequences of states into algorithms—rules of the form: input (parameter ranges), action (parameter changes), output (functional dependencies). The knowledge base starts empty.

The first rule emerges after three hits: generalization of the projectile approach pattern. The agent detects danger and responds:

• Trial action: A random step (left or right) from available options.
• Simulated action: Algorithm synthesis for predicting outcomes.
• Verified action: Reflex based on a generalized successful avoidance rule.

Generalizing the agent’s movement steps yields a coordinate adjustment rule. Analyzing projectile trajectories produces a flight path rule. These insights allow modeling scenarios several steps ahead.

Synthesis and Action Mechanism

The algorithm synthesis module generates possible actions for both the agent and the hunter using existing rules. Outcomes are evaluated using criteria like success probability and energy cost. The best option is executed by the action module.

Successful models are generalized into verified rules. Full adaptation means avoiding damage using only proven actions.

The game features two projectile types:

• Straight-line: evasion by moving off the line.
• Guided (limited range): retreat to a safe distance.

When steps are insufficient, stress mode activates: analysis of past behavior over more turns. Knowledge about hunter movements and firing patterns enables simulation up to 3 turns ahead, maximizing escape chances.

Agent Responses to Threats

• Trial: In new situations, before data accumulation.
• Simulated: Outcome prediction with optimal choice selection.
• Verified: Instant reflex response.

This mirrors learning stages—from chaotic exploration to skill mastery.

Scalability and Universality

The core mind (perception, memory, classification, synthesis, action) is universal and adapts to environments via peripheral modules. Future possibilities include:

• Increasing object count and action complexity.
• Deeper simulation depth (more steps, longer reaction times).
• Creativity: solving problems with incomplete knowledge (hypotheses as partial rule matches).
• Active exploration: planning experiments (currently limited to influencing objects).

Hypotheses are tested through low-cost risk-taking or controlled experiments. By version 3–4, direct object influence will be introduced.

Key Takeaways:

• The system generates knowledge from real-time processes in a hostile environment without prior training.
• Three response levels enable adaptation from trial-and-error to instinctive reflexes.
• Multi-step ahead simulation mimics foresight.
• Stress mode extends historical analysis for complex threats.
• The core architecture scales toward strong AI with creativity and experimental reasoning.

— Editorial Team