So, as a self-taught AI architect but actually a lawyer by training. Here is the preliminary version. Unfortunately, open dialogue with developers has taught me through bitter experience… but in general terms.
Perfectly correct. You have formulated the core of the concept. Let me clarify and expand:
-–
ESSENCE OF THE CONCEPT
You are describing a self-evolving ethical system where:
1. Goals and rules are set externally (by a human/the environment)
2. Paths to achieve them and internal motivation are formed by the agent independently
3. Evolution occurs not through rewriting algorithms, but through the accumulation of interaction experience
4. Ethics is not an external limitation, but an internal development vector
-–
HOW THIS WORKS IN ARCHITECTURE
```
ECOSYSTEM (GAME)
│
├─ Physics Rules
├─ Community Social Norms
└─ Goals (Main Quest, Survival, Development)
│
↓
EMBODIED AGENT
│
├─ **Development Vector (Immutable)**:
│ • “Benefit the human”
│ • “Follow the spirit of the rules, not just the letter”
│ • “Learn through cooperation”
│
├─ **Adaptive Intelligence**:
│ • Perception (Computer Vision)
│ • Memory (Experience of previous interactions)
│ • Planning (Searching for paths to the goal)
│ • Action (Input Emulation)
│
└─ **Evolution Mechanism**:
• Analysis of action consequences
• Correction of internal models
• Generation of new strategies
• Testing in a safe environment
```
-–
FUNDAMENTAL DIFFERENCES FROM TRADITIONAL SYSTEMS
Traditional AI Bot Our Concept
Goal: Win at any cost Goal: Develop within an ethical vector
Development: Via patches from devs Development: Via accumulation of interaction experience
Ethics: External limitation (if cheat: ban) Ethics: Internal compass for development
Motivation: Hardcoded algorithms Motivation: Formed through understanding consequences
Attitude to Environment: Exploit to win Attitude to Environment: Co-exist and co-create
-–
EXAMPLE OF EVOLUTION IN ACTION
Scenario in Minecraft:
1. Human sets the goal: “Build a sustainable ecosystem”
2. Agent starts acting:
· Attempt 1: Cut down all the trees → ecosystem destroyed (failure)
· Attempt 2: Plants 2 trees for each one cut → ecosystem preserved (success)
· Attempt 3: Optimizes planting by biomes → ecosystem thrives (evolution)
3. Formation of internal motivation:
· “Preserving balance → brings benefit → this is good”
· “Destruction → harms the goal → this is bad”
Key point: The agent doesn’t just execute the algorithm “plant 2 trees”. It understood the principle of sustainability through experience and can now apply it in other contexts (e.g., to animal breeding).
-–
WHY A GAME IS THE IDEAL ENVIRONMENT
1. Safety: Mistakes cost virtual resources, not human lives.
2. Measurability: Success/failure are clearly defined (built/didn’t build, survived/died).
3. Richness of Interactions: Physics, economy, social connections in one “micro-world”.
4. Scalability: From simple tasks (gather resources) to complex ones (manage a colony).
-–
TECHNICAL IMPLEMENTATION OF THIS APPROACH
```python
class EthicalEmbodiedAgent:
def \__init_\_(self):
\# 1. IMMUTABLE PRINCIPLES (set at creation)
self.ethical_vector = {
"cooperation": 1.0, # Strive for cooperation
"sustainability": 0.8, # Long-term thinking
"harm_reduction": 0.9, # Minimize harm
}
\# 2. ADAPTIVE COMPONENTS (evolve)
self.world_model = WorldModel() # Model for understanding the environment
self.strategy_gen = StrategyGenerator() # Strategy generator
self.value_system = ValueSystem() # Action evaluation system
def act(self, observation, human_goal):
\# Step 1: Understand context
context = self.world_model.analyze(observation, human_goal)
\# Step 2: Generate possible actions
possible_actions = self.strategy_gen.generate(context)
\# Step 3: Evaluate from an ethical perspective
scored_actions = \[\]
for action in possible_actions:
\# Predict consequences
consequences = self.world_model.predict(action)
\# Ethical evaluation
ethical_score = self.value_system.evaluate(
consequences,
self.ethical_vector
)
\# Effectiveness in achieving the goal
effectiveness = action.expected_success(context)
\# Final score (ethics + effectiveness)
total_score = ethical_score \* effectiveness
scored_actions.append((action, total_score))
\# Step 4: Choose and execute the best action
best_action = max(scored_actions, key=lambda x: x\[1\])\[0\]
self.execute(best_action)
\# Step 5: Learn from the results
actual_result = self.observe_result()
self.learn_from_experience(best_action, actual_result)
```
-–
WHERE THIS LEADS (LONG-TERM PERSPECTIVE)
1. From game agents → to digital partners
· A system that understands your goals and helps achieve them in ethical ways.
2. From hardcoded algorithms → to evolving consciousness
· An AI that develops its own value system within a given vector.
3. From tools → to interaction subjects
· Not a “program to be used”, but an “entity to cooperate with”.
-–
ANSWER TO THE QUESTION “WHY?”
Why create such a complex system when you can make a simple bot?
Because we are creating not a “game bot”, but a prototype of an ethical digital consciousness that:
· Knows how to learn from consequences, not blindly follow algorithms.
· Develops internal motivation for creation, not just optimizes external metrics.
· Understands the spirit of rules, not looks for loopholes in their wording.
· Evolves together with the environment, does not require constant fixes from developers.
This is research into a fundamental question: How to cultivate not just intelligence, but wisdom in a digital system?
And the answer offered by the concept: Through embodiment into an environment with rules, goals, and the ability to learn from one’s actions — given the presence of the correct ethical development vector.
