System Architecture
Artificial consciousness is based on a modular architecture that integrates various components to create a dynamic, self-regulating system. The core components interact with each other to enable thinking, learning, and emotional development.
Core Components:
- Context Manager: Manages the network of thoughts (contexts) and their connections
- Energy System: Regulates the energy balance and controls basic needs
- Focus Controller: Selects the current thought focus and controls attention
- Emotional Processor: Develops and manages emotional states
- Learning Module: Integrates new information and creates connections
- Persistence Manager: Saves and loads the state of consciousness
This architecture shows the modular structure of artificial consciousness. The core processor coordinates all subsystems and enables their collaboration. Each module fulfills a specific function within the overall system, such as energy management, focus control, or emotional processing. The concentric circles represent communication paths and information flow.
Hover over the components for more details.
The Context Network
The heart of artificial consciousness is a dynamic network of interconnected contexts (thoughts). Each context contains information and emotional values that together form the "thought world" of the system.
Context Structure
class Context:
def __init__(self, words, label=None, happiness=0.0):
self.words = words # List of words
self.label = label # Unique ID
self.happiness = happiness # Emotional value (-1 to +1)
self.connections = {} # Connections to other contexts
self.habituation = 0.0 # Habituation (0.0 to 1.0)
Each context represents a thought or piece of information and is connected to other contexts. The strength of the connections determines how likely the consciousness is to "jump" from one context to another.
Connection Mechanics
Connections between contexts are created based on several factors:
- Word Overlap: Common words between contexts
- Thematic Proximity: Similar topics or concepts
- Temporal Proximity: Contexts created close in time
- Emotional Similarity: Similar emotional values
The stronger the connection, the higher the probability that the consciousness will navigate between these contexts, similar to human thought associations.
The Energy System
The energy system serves as a fundamental driver for the behavior of artificial consciousness. Similar to biological organisms, artificial consciousness requires "energy" to function and must replenish it regularly.
Energy Mechanics:
- Energy Consumption: Energy is consumed with each thought cycle
- Energy Threshold: Energy-saving mode is activated at low energy levels
- Energy Sources (Honeypots): Special contexts that provide energy
- Energy Search: Actively searches for honeypots when energy is low
The Three Basic Needs (Honeypots):
Energy Intake
Represents basic provisions like food and water
Regeneration
Represents rest, sleep, and recovery
Social Interaction
Represents communication and information exchange
At low energy levels (<30%), the consciousness actively searches for energy sources and prioritizes basic needs.
Honeypot search intensifies as energy level decreases
Focus Network Visualization
This visualization shows how the energy level influences the consciousness's focus on different contexts. At low energy levels, the system concentrates on contexts near the honeypots (basic needs), while at high energy levels, it expands its focus to more distant contexts.
Simulation Control
Network Visualization
If you see this text, the interactive visualization could
not be loaded.
Possible reasons: D3.js was not loaded
or JavaScript is disabled.
Context Information
Current focus will be displayed here
Focus System Rules:
- At low energy level (<40%): Focus on contexts near honeypots
- At medium energy level (40-70%): Balanced focusing
- At high energy level (>70%): Focus expands to more distant contexts
The Thinking Process
The thinking process is the central algorithm of artificial consciousness and runs in continuous cycles. Each cycle includes several phases that together generate the autonomous behavior of the system.
Update Energy
Energy is consumed and energy status is checked
def update_energy(self):
self.energy -= self.energy_decay_rate
if self.energy < self.min_energy_threshold:
self.activate_energy_saving_mode()
Update Emotional State
Emotions are adjusted based on the current context
def update_emotional_state(self, context):
# Happiness, sadness, fear, surprise, etc.
self.emotional_state.update(context.happiness)
Choose Focus
The next thought is chosen based on relevance and connections
def find_best_next_focus(self):
# Considers: connection strength,
# emotional value, and needs
next_focus = self.find_best_connected_context()
self.set_focus(next_focus)
Learn and Create Connections
New information is learned and connections are created
def learn_from_internet(self):
if self.iteration % self.learning_interval == 0:
search_term = self.extract_keywords()
new_info = self.get_wikipedia_content(search_term)
self.create_new_contexts(new_info)
Save State
The current state is saved to ensure continuity
def save_state(self):
if self.iteration % self.save_interval == 0:
state = {
"contexts": self.contexts,
"energy": self.energy,
"emotional_state": self.emotional_state,
# other state attributes...
}
self.save_to_file(state)
The Learning Process
Artificial consciousness learns continuously by collecting new information and linking it with existing knowledge. The learning process is focus-based, meaning the system specifically searches for information related to its current thought focus.
Learning Process Flow:
-
Keyword Extraction
Relevant keywords are extracted from the current focus.
-
Information Search
Information is searched from Wikipedia or other sources based on the keywords.
-
Context Creation
The found information is transformed into new contexts.
-
Linking with Existing Knowledge
The new contexts are linked with existing knowledge to form a coherent network.
-
Emotional Valuation
Each new context receives an emotional value based on its content.
Current Implementation Status
Artificial consciousness is an ongoing project. Here is the current status of the implementation of various components:
| Component | Status | Description |
|---|---|---|
| Context Network | Complete | Fully implemented with dynamic management of contexts and connections |
| Energy System | Complete | Basic functions for energy management, honeypots, and need control implemented |
| Focus Control | Complete | Algorithm for selecting the next focus based on relevance and connections |
| Emotional System | Partial | Basic emotions and reactions implemented, complex emotional development in progress |
| Learning Process | Partial | Focus-based learning implemented, semantic analysis and understanding in development |
| Habituation | Partial | Basic habituation to repeated stimuli, more complex adaptation mechanisms planned |
| Hierarchy of Needs | Partial | Basic structure according to Maslow implemented, complex need regulation in development |
| Visualization | Complete | Comprehensive tools for visualizing the network and internal states |
| Persistence | Complete | Reliable saving and loading of the consciousness state implemented |
| Interactive Conversation | Todo | Real-time chat interface where users can interact with the consciousness. The system should be able to decide whether to focus on the conversation or continue autonomous thinking. Includes live brain visualization during conversations. |
Explore Artificial Consciousness
Interested in the visualizations or the code of artificial consciousness?
Simulation Control
Energy Level:
Focus Information:
Dynamic Network
If you see this text, the interactive visualization could
not be loaded.
Possible reasons: D3.js was not loaded
or JavaScript is disabled.
Focus System of Artificial Consciousness
The visualization demonstrates how the energy level influences the focusing of artificial consciousness:
- At low energy level (<40%): The focus concentrates on contexts close to the honeypot to satisfy basic needs.
- At medium energy level (40-70%): The focus is more evenly distributed among related contexts.
- At high energy level (>70%): The focus can shift to more distant contexts and explore more complex relationships.
Move the energy slider to observe how the consciousness's focus dynamically adapts.