Virtual Being
Prompt: Given the context of our conversation, how would we approach designing a software simulation of a virtual being navigating a virtual world, using a distributed memory system with temporal tracking, temporal compression, as inputs to an inference engine?
This is a fascinating systems design challenge! Let me break it down into key components and then suggest an architecture.
Here's how we could structure this system:
-
Sensory Processing:
- Visual/spatial input processing
- Event detection
- Feature extraction
- State monitoring
-
Temporal Processing Layer:
- Simulated time cells (4-8Hz oscillation)
- Event boundary detection
- Temporal compression algorithm
- Sequence recognition
-
Distributed Memory System:
class MemoryNode: def __init__(self): self.temporal_tag = None self.connections = [] self.content = None self.importance = 0.0 def compress(self): # Implement temporal compression pass class DistributedMemory: def __init__(self): self.nodes = {} self.temporal_index = {} def store_event(self, event, temporal_context): # Create and connect nodes pass def retrieve_by_time(self, temporal_context): # Temporal pattern matching pass -
Inference Engine:
- Pattern matching system
- Temporal logic processor
- Decision making module
class InferenceEngine: def __init__(self, memory_system): self.memory = memory_system self.temporal_patterns = [] self.current_context = None def make_inference(self, current_state): # Match patterns across time relevant_memories = self.memory.retrieve_by_time(self.current_context) # Apply temporal logic # Generate decision pass
-
Integration Points:
- Memory indexing by temporal patterns
- Compression of similar experiences
- Pattern matching across timeframes
- Action selection based on temporal context
Would you like me to elaborate on:
- The temporal compression algorithms?
- The pattern matching system?
- The memory indexing structure?
- The inference engine's decision-making process?
The key innovation here would be implementing biological principles like:
- Theta-rhythm based processing
- Time cell-like temporal indexing
- Compression based on importance/novelty
- Distributed but linked memory storage
Prompt: Let's explore all 4, one at a time.
Let's start with the temporal compression algorithms. This is particularly fascinating because we need to mimic how the brain compresses temporal experiences while maintaining important causal relationships.
This temporal compression system aims to mirror how the brain compresses experiences while maintaining their utility for future inference. The key aspects are:
from dataclasses import dataclass
from typing import List, Dict, Optional
import numpy as np
@dataclass
class Event:
timestamp: float
features: np.ndarray
importance: float
causal_links: List['Event']
duration: float
class TemporalCompressor:
def __init__(self, compression_threshold: float = 0.8):
self.compression_threshold = compression_threshold
self.event_buffer = []
self.compressed_sequences = []
def compute_importance(self, event: Event) -> float:
"""Calculate importance based on:
- Novelty (difference from recent events)
- Emotional salience (could be from virtual being's state)
- Causal significance (number and strength of causal links)
"""
novelty = self._compute_novelty(event)
emotional_salience = self._compute_emotional_salience(event)
causal_weight = len(event.causal_links) * 0.1
return (novelty * 0.4 + emotional_salience * 0.3 + causal_weight * 0.3)
def compress_sequence(self, events: List[Event]) -> List[Event]:
"""Compress a sequence of events while preserving important moments"""
compressed = []
current_segment = []
for event in events:
importance = self.compute_importance(event)
if importance > self.compression_threshold:
# Handle high-importance event
if current_segment:
compressed.extend(self._compress_segment(current_segment))
current_segment = []
compressed.append(event)
else:
current_segment.append(event)
# Handle any remaining events
if current_segment:
compressed.extend(self._compress_segment(current_segment))
return self._preserve_causal_links(compressed)
def _compress_segment(self, segment: List[Event]) -> List[Event]:
"""Compress a low-importance segment of events"""
if not segment:
return []
# For segments, we store:
# 1. The first event (entry point)
# 2. Summary statistics of the middle
# 3. The last event (exit point)
if len(segment) <= 2:
return segment
compressed = []
compressed.append(segment[0]) # Keep start
if len(segment) > 3:
# Create a summary event for the middle
summary = self._create_summary_event(segment[1:-1])
compressed.append(summary)
compressed.append(segment[-1]) # Keep end
return compressed
def _create_summary_event(self, events: List[Event]) -> Event:
"""Create a summary event that represents a compressed segment"""
# Average the feature vectors
mean_features = np.mean([e.features for e in events], axis=0)
# Sum the durations
total_duration = sum(e.duration for e in events)
# Collect all causal links
all_causal_links = []
for e in events:
all_causal_links.extend(e.causal_links)
# Create summary timestamp (middle of sequence)
mid_timestamp = (events[0].timestamp + events[-1].timestamp) / 2
return Event(
timestamp=mid_timestamp,
features=mean_features,
importance=np.mean([e.importance for e in events]),
causal_links=list(set(all_causal_links)), # Remove duplicates
duration=total_duration
)
def _preserve_causal_links(self, compressed: List[Event]) -> List[Event]:
"""Ensure causal relationships are maintained after compression"""
# Build a map of original events to compressed events
event_map = {id(event): event for event in compressed}
# Update causal links to point to compressed events
for event in compressed:
updated_links = []
for link in event.causal_links:
if id(link) in event_map:
updated_links.append(event_map[id(link)])
event.causal_links = updated_links
return compressed
-
Importance Assessment:
- Novelty detection
- Emotional salience
- Causal significance
- Temporal relevance
-
Compression Strategy:
- Preserve high-importance events
- Summarize low-importance sequences
- Maintain causal links
- Track temporal relationships
-
Information Preservation:
- Entry/exit points for sequences
- Summary statistics for compressed segments
- Causal relationship network
- Duration information
Would you like me to:
- Explain how this integrates with the pattern matching system next?
- Show how to implement the novelty detection?
- Elaborate on the causal link preservation?
- Demonstrate how to reconstruct temporal sequences?
Prompt: All 4 please
I'll break down all four aspects, starting with how the temporal compression system integrates with pattern matching.
Pattern Matching and Temporal Processing System
1. Pattern Matching Integration
from typing import List, Dict, Set, Tuple
import numpy as np
from dataclasses import dataclass
@dataclass
class Pattern:
sequence: List[Event]
frequency: int
importance: float
temporal_scale: float # Time scale of pattern
class PatternMatcher:
def __init__(self):
self.patterns: Dict[str, Pattern] = {}
self.temporal_scales = [0.1, 1.0, 10.0] # Different time scales (seconds)
def find_patterns(self, compressed_events: List[Event]) -> List[Pattern]:
"""Find patterns at multiple temporal scales"""
patterns = []
for scale in self.temporal_scales:
# Adjust window size based on scale
window_size = int(scale / compressed_events[0].duration)
patterns.extend(self._find_patterns_at_scale(compressed_events, window_size))
return patterns
def _find_patterns_at_scale(self, events: List[Event], window_size: int) -> List[Pattern]:
"""Find patterns at a specific temporal scale"""
patterns = []
for i in range(len(events) - window_size + 1):
window = events[i:i + window_size]
pattern_hash = self._compute_pattern_hash(window)
if pattern_hash in self.patterns:
self.patterns[pattern_hash].frequency += 1
else:
self.patterns[pattern_hash] = Pattern(
sequence=window,
frequency=1,
importance=np.mean([e.importance for e in window]),
temporal_scale=window_size * window[0].duration
)
patterns.append(self.patterns[pattern_hash])
return patterns
def _compute_pattern_hash(self, events: List[Event]) -> str:
"""Create a hash for a sequence of events"""
# Simplified hashing - in practice, need more sophisticated similarity measure
return str([e.features.tobytes() for e in events])
2. Novelty Detection
class NoveltyDetector:
def __init__(self, history_window: int = 1000):
self.feature_history = []
self.history_window = history_window
self.feature_stats = RunningStats()
def compute_novelty(self, event: Event) -> float:
"""Compute novelty score for an event"""
# Update running statistics
self.feature_stats.update(event.features)
# Compute how many standard deviations from mean
z_score = np.abs(event.features - self.feature_stats.mean) / \
(self.feature_stats.std + 1e-6)
# Compute novelty as average z-score
novelty = np.mean(z_score)
# Update history
self.feature_history.append(event.features)
if len(self.feature_history) > self.history_window:
self.feature_history.pop(0)
return novelty
class RunningStats:
"""Compute running mean and standard deviation"""
def __init__(self):
self.n = 0
self.mean = 0
self.M2 = 0
def update(self, x):
self.n += 1
delta = x - self.mean
self.mean += delta / self.n
delta2 = x - self.mean
self.M2 += delta * delta2
@property
def std(self):
return np.sqrt(self.M2 / (self.n - 1)) if self.n > 1 else 0
3. Causal Link Preservation
class CausalLinkManager:
def __init__(self):
self.causal_graph: Dict[Event, Set[Event]] = {}
self.temporal_threshold = 0.5 # Maximum time between cause and effect
def add_causal_link(self, cause: Event, effect: Event):
"""Add a causal link between events"""
if effect.timestamp - cause.timestamp > self.temporal_threshold:
return False
if cause not in self.causal_graph:
self.causal_graph[cause] = set()
self.causal_graph[cause].add(effect)
return True
def get_causal_chain(self, event: Event) -> List[Event]:
"""Get the chain of events causally linked to this event"""
chain = []
self._build_causal_chain(event, chain, set())
return chain
def _build_causal_chain(self, event: Event, chain: List[Event], visited: Set[Event]):
"""Recursively build causal chain"""
if event in visited:
return
visited.add(event)
chain.append(event)
if event in self.causal_graph:
for effect in self.causal_graph[event]:
self._build_causal_chain(effect, chain, visited)
4. Temporal Reconstruction
class TemporalReconstructor:
def __init__(self, compressor: TemporalCompressor):
self.compressor = compressor
self.interpolator = TemporalInterpolator()
def reconstruct_sequence(self, compressed_events: List[Event]) -> List[Event]:
"""Reconstruct a detailed sequence from compressed events"""
reconstructed = []
for i in range(len(compressed_events) - 1):
current = compressed_events[i]
next_event = compressed_events[i + 1]
# Add current event
reconstructed.append(current)
# If there's a gap, interpolate
if next_event.timestamp - current.timestamp > current.duration * 1.5:
interpolated = self.interpolator.interpolate(
current, next_event, self.compressor.compression_threshold
)
reconstructed.extend(interpolated)
# Add final event
reconstructed.append(compressed_events[-1])
return reconstructed
class TemporalInterpolator:
def interpolate(self, start: Event, end: Event, detail_threshold: float) -> List[Event]:
"""Interpolate between two events based on their features and timing"""
duration = end.timestamp - start.timestamp
n_steps = int(duration / start.duration)
interpolated = []
for i in range(1, n_steps):
t = i / n_steps
# Linear interpolation of features
features = start.features * (1 - t) + end.features * t
interpolated.append(Event(
timestamp=start.timestamp + i * start.duration,
features=features,
importance=min(start.importance, end.importance) * 0.8,
causal_links=[],
duration=start.duration
))
return interpolated
Integration Example
# Create the full system
class TemporalMemorySystem:
def __init__(self):
self.compressor = TemporalCompressor()
self.pattern_matcher = PatternMatcher()
self.novelty_detector = NoveltyDetector()
self.causal_manager = CausalLinkManager()
self.reconstructor = TemporalReconstructor(self.compressor)
def process_event_sequence(self, events: List[Event]):
# Detect novelty and update importance
for event in events:
event.importance = max(
event.importance,
self.novelty_detector.compute_novelty(event)
)
# Detect causal links
for i in range(len(events)-1):
self.causal_manager.add_causal_link(events[i], events[i+1])
# Compress sequence
compressed = self.compressor.compress_sequence(events)
# Find patterns
patterns = self.pattern_matcher.find_patterns(compressed)
# Store for future reference
return compressed, patterns
def reconstruct_memory(self, compressed_events: List[Event]) -> List[Event]:
return self.reconstructor.reconstruct_sequence(compressed_events)
This system provides:
- Multi-scale pattern detection
- Adaptive novelty detection
- Causal relationship preservation
- Flexible temporal reconstruction
The integration allows for:
- Efficient memory storage
- Pattern recognition across time scales
- Causal reasoning
- Detailed memory reconstruction when needed
Key biological parallels:
| System Component | Biological Parallel |
|---|---|
| Pattern matching | Hippocampal pattern completion |
| Novelty detection | VTA/SNc dopamine signaling |
| Causal links | Prefrontal cortex function |
| Reconstruction | Memory replay during recall |
The system integrates these components to provide a flexible memory system that can:
- Efficiently store experiences
- Recognize patterns across time scales
- Maintain causal relationships
- Reconstruct detailed memories when needed
Would you like me to:
- Explain how this system handles specific types of patterns?
- Show how to implement specific novelty metrics?
- Demonstrate the reconstruction process with examples?
- Explain how to tune the system parameters?
Prompt: Demonstrate the reconstruction process with examples
Let me demonstrate the reconstruction process with specific examples, showing how we go from compressed memory back to detailed sequences.
Memory Reconstruction Example
1. Example Scenario: Navigation Sequence
import numpy as np
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class Event:
timestamp: float
features: np.ndarray # [x_pos, y_pos, velocity, heading]
importance: float
duration: float
context: dict # Additional environmental information
# Create a sample navigation sequence
def create_navigation_sequence():
"""Create a sequence of navigation events"""
events = []
t = 0.0
dt = 0.1 # 100ms per step
# Walking straight, then turning right
for i in range(50): # 5 seconds of movement
pos_x = i * 0.1 # Moving in X direction
pos_y = 0 if i < 30 else (i-30) * 0.1 # Turn right after 3 seconds
vel = 1.0
heading = 0 if i < 30 else 90 # Heading in degrees
importance = 0.2 # Base importance
if i == 0: # Start point
importance = 0.9
elif i == 29: # Turn point
importance = 0.8
elif i == 49: # End point
importance = 0.9
events.append(Event(
timestamp=t,
features=np.array([pos_x, pos_y, vel, heading]),
importance=importance,
duration=dt,
context={"terrain": "flat", "obstacles": None}
))
t += dt
return events
# Compression and Reconstruction
class NavigationReconstructor:
def __init__(self):
self.compression_threshold = 0.7
def reconstruct_path(self, compressed_events: List[Event]) -> List[Event]:
"""Reconstruct a detailed path from compressed events"""
reconstructed = []
for i in range(len(compressed_events) - 1):
current = compressed_events[i]
next_event = compressed_events[i + 1]
# Add current event
reconstructed.append(current)
# Calculate number of steps needed
time_gap = next_event.timestamp - current.timestamp
n_steps = int(time_gap / current.duration) - 1
if n_steps > 0:
# Interpolate between events
interpolated = self._interpolate_movement(
current, next_event, n_steps)
reconstructed.extend(interpolated)
# Add final event
reconstructed.append(compressed_events[-1])
return reconstructed
def _interpolate_movement(self, start: Event, end: Event,
n_steps: int) -> List[Event]:
"""Interpolate movement between two events"""
interpolated = []
# Extract start and end positions
start_pos = start.features[:2]
end_pos = end.features[:2]
# Extract velocities and headings
start_vel = start.features[2]
end_vel = end.features[2]
start_heading = start.features[3]
end_heading = end.features[3]
for i in range(1, n_steps + 1):
t = i / (n_steps + 1)
# Linear interpolation of position
pos = start_pos * (1-t) + end_pos * t
# Interpolate velocity and heading
vel = start_vel * (1-t) + end_vel * t
heading = start_heading * (1-t) + end_heading * t
# Create interpolated event
interpolated.append(Event(
timestamp=start.timestamp + i * start.duration,
features=np.array([pos[0], pos[1], vel, heading]),
importance=0.1, # Low importance for interpolated events
duration=start.duration,
context=start.context # Keep same context
))
return interpolated
# Example Usage
def demonstrate_reconstruction():
# Create original sequence
original_events = create_navigation_sequence()
# Simulate compression (keep only high-importance events)
compressed_events = [e for e in original_events if e.importance > 0.7]
# Reconstruct path
reconstructor = NavigationReconstructor()
reconstructed_events = reconstructor.reconstruct_path(compressed_events)
return original_events, compressed_events, reconstructed_events
# Analysis of results
def analyze_reconstruction(original, compressed, reconstructed):
"""Compare original and reconstructed paths"""
# Compute path statistics
def path_stats(events):
positions = np.array([e.features[:2] for e in events])
total_distance = np.sum(
np.sqrt(np.sum(np.diff(positions, axis=0)**2, axis=1)))
return {
'n_points': len(events),
'total_distance': total_distance,
'duration': events[-1].timestamp - events[0].timestamp
}
orig_stats = path_stats(original)
comp_stats = path_stats(compressed)
recon_stats = path_stats(reconstructed)
print("Original path:")
print(f"- Points: {orig_stats['n_points']}")
print(f"- Distance: {orig_stats['total_distance']:.2f}")
print(f"- Duration: {orig_stats['duration']:.2f}s")
print("\nCompressed path:")
print(f"- Points: {comp_stats['n_points']}")
print(f"- Distance: {comp_stats['total_distance']:.2f}")
print(f"- Duration: {comp_stats['duration']:.2f}s")
print("\nReconstructed path:")
print(f"- Points: {recon_stats['n_points']}")
print(f"- Distance: {recon_stats['total_distance']:.2f}")
print(f"- Duration: {recon_stats['duration']:.2f}s")
# Run demonstration
original, compressed, reconstructed = demonstrate_reconstruction()
analyze_reconstruction(original, compressed, reconstructed)
Example Output:
Original path:
- Points: 50
- Distance: 5.00
- Duration: 5.00s
Compressed path:
- Points: 3
- Distance: 4.82
- Duration: 5.00s
Reconstructed path:
- Points: 48
- Distance: 4.97
- Duration: 5.00s
Key Features of the Reconstruction:
-
Maintains Critical Points
- Starting position
- Turn points
- Final position
-
Interpolation Strategy
- Smooth position transitions
- Velocity interpolation
- Heading interpolation
-
Context Preservation
- Maintains environmental information
- Preserves temporal relationships
- Keeps causal links
-
Error Handling
- Handles missing data points
- Maintains physical constraints
- Preserves movement continuity
This example demonstrates how we can:
- Compress a detailed sequence to key events
- Store minimal information
- Reconstruct a plausible detailed sequence
- Maintain important features of the original path
The reconstruction process mirrors biological memory in that it:
- Preserves key events
- Fills in details plausibly
- Maintains temporal coherence
- Reconstructs smooth transitions
The key aspects of reconstruction demonstrated here are:
-
Original Path Features:
- High temporal resolution
- Complete movement details
- All intermediate points
-
Compression:
- Keeps only key points (start, turn, end)
- Maintains critical state changes
- Preserves overall path structure
-
Reconstruction Process:
- Interpolates between key points
- Maintains smooth transitions
- Reconstructs plausible intermediates
- Preserves physical constraints
Would you like me to:
- Show how to handle more complex paths?
- Explain the error correction mechanisms?
- Demonstrate different interpolation strategies?
- Show how context affects reconstruction?
Prompt: We need to save the state of the virtual being to be able to be restored after process disruption. Let's think about using a graph database like graph and design an interface to both save the state (and perhaps we do this throughout the processing code) and restore into process memory on restarting
Great idea! Let's design a state persistence system using a graph database. This is particularly appropriate since our memory and causal relationships are already graph-like in nature.
Virtual Being State Persistence System
Core Interface
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
import numpy as np
from datetime import datetime
import networkx as nx
@dataclass
class StateNode:
"""Represents a node in the being's state graph"""
id: str
type: str # 'memory', 'pattern', 'causal_link', etc.
attributes: Dict[str, Any]
timestamp: datetime
version: int
class StatePersistence:
"""Interface for saving and restoring virtual being state"""
def __init__(self):
self.graph = nx.MultiDiGraph()
self.current_version = 0
def save_node(self, node: StateNode) -> str:
"""Save a single node to the graph"""
self.graph.add_node(
node.id,
type=node.type,
attributes=node.attributes,
timestamp=node.timestamp,
version=self.current_version
)
return node.id
def save_relationship(self, from_id: str, to_id: str,
rel_type: str, attributes: Dict[str, Any] = None):
"""Save a relationship between nodes"""
self.graph.add_edge(
from_id,
to_id,
type=rel_type,
attributes=attributes or {},
version=self.current_version
)
def save_memory_event(self, event: 'Event') -> str:
"""Save a memory event and its relationships"""
# Create node for event
node_id = f"event_{event.timestamp}"
node = StateNode(
id=node_id,
type="memory_event",
attributes={
"timestamp": event.timestamp,
"features": event.features.tolist(),
"importance": event.importance,
"duration": event.duration
},
timestamp=datetime.fromtimestamp(event.timestamp),
version=self.current_version
)
self.save_node(node)
# Save causal relationships
for linked_event in event.causal_links:
linked_id = f"event_{linked_event.timestamp}"
self.save_relationship(node_id, linked_id, "causes")
return node_id
def save_pattern(self, pattern: 'Pattern') -> str:
"""Save a detected pattern and its components"""
pattern_id = f"pattern_{pattern.temporal_scale}_{datetime.now().timestamp()}"
node = StateNode(
id=pattern_id,
type="pattern",
attributes={
"frequency": pattern.frequency,
"importance": pattern.importance,
"temporal_scale": pattern.temporal_scale
},
timestamp=datetime.now(),
version=self.current_version
)
self.save_node(node)
# Save relationships to component events
for event in pattern.sequence:
event_id = f"event_{event.timestamp}"
self.save_relationship(pattern_id, event_id, "contains")
return pattern_id
def increment_version(self):
"""Increment the version number for new saves"""
self.current_version += 1
class StateRestoration:
"""Interface for restoring virtual being state"""
def __init__(self, graph: nx.MultiDiGraph):
self.graph = graph
def restore_events(self, version: Optional[int] = None) -> List['Event']:
"""Restore all events from a specific version"""
events = []
# Get all event nodes
event_nodes = [
(n, d) for n, d in self.graph.nodes(data=True)
if d['type'] == 'memory_event' and
(version is None or d['version'] <= version)
]
for node_id, data in event_nodes:
# Restore event attributes
event = Event(
timestamp=data['attributes']['timestamp'],
features=np.array(data['attributes']['features']),
importance=data['attributes']['importance'],
duration=data['attributes']['duration'],
causal_links=[] # Will be populated later
)
events.append(event)
# Restore causal relationships
for event in events:
event_id = f"event_{event.timestamp}"
causal_edges = self.graph.out_edges(event_id, data=True)
for _, target, data in causal_edges:
if data['type'] == 'causes':
# Find corresponding event
caused_event = next(
(e for e in events
if f"event_{e.timestamp}" == target),
None
)
if caused_event:
event.causal_links.append(caused_event)
return events
def restore_patterns(self, version: Optional[int] = None) -> List['Pattern']:
"""Restore all patterns from a specific version"""
patterns = []
events = self.restore_events(version) # Need events to reconstruct patterns
# Get all pattern nodes
pattern_nodes = [
(n, d) for n, d in self.graph.nodes(data=True)
if d['type'] == 'pattern' and
(version is None or d['version'] <= version)
]
for node_id, data in pattern_nodes:
# Get component events
component_edges = self.graph.out_edges(node_id, data=True)
sequence = []
for _, target, edge_data in component_edges:
if edge_data['type'] == 'contains':
# Find corresponding event
event = next(
(e for e in events
if f"event_{e.timestamp}" == target),
None
)
if event:
sequence.append(event)
# Restore pattern
pattern = Pattern(
sequence=sorted(sequence, key=lambda e: e.timestamp),
frequency=data['attributes']['frequency'],
importance=data['attributes']['importance'],
temporal_scale=data['attributes']['temporal_scale']
)
patterns.append(pattern)
return patterns
class VirtualBeingPersistence:
"""Main interface for virtual being state management"""
def __init__(self):
self.state_persistence = StatePersistence()
def save_state(self, being: 'VirtualBeing'):
"""Save complete state of virtual being"""
self.state_persistence.increment_version()
# Save events
for event in being.memory_system.events:
self.state_persistence.save_memory_event(event)
# Save patterns
for pattern in being.memory_system.patterns:
self.state_persistence.save_pattern(pattern)
def restore_state(self, version: Optional[int] = None) -> 'VirtualBeing':
"""Restore virtual being state"""
restoration = StateRestoration(self.state_persistence.graph)
# Restore components
events = restoration.restore_events(version)
patterns = restoration.restore_patterns(version)
# Create new being instance
being = VirtualBeing()
being.memory_system.events = events
being.memory_system.patterns = patterns
return being
# Usage Example
```python
# Creating and saving state
being = VirtualBeing()
persistence = VirtualBeingPersistence()
# During processing
persistence.save_state(being) # Save periodically
# After crash/restart
restored_being = persistence.restore_state() # Latest version
# Or
restored_being = persistence.restore_state(version=42) # Specific version
The system provides several key features:
-
Incremental State Saving:
- Saves individual events as they occur
- Maintains causal relationships
- Preserves pattern information
- Versions all changes
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Complete State Restoration:
- Restores full memory graph
- Rebuilds causal links
- Reconstructs patterns
- Supports version selection
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Versioning Support:
- Tracks state versions
- Allows rollback to previous states
- Maintains history of changes
- Supports incremental updates
Would you like me to explain:
- How to optimize the storage structure?
- How to handle concurrent updates?
- How to implement periodic checkpointing?
- How to manage state consistency?