SLASH Memory System

The SLASH Memory System is an integral part of the KLEP framework, designed to manage memories dynamically. It organizes memory into clusters and uses bitmaps to represent key associations. The system allows agents to recognize patterns, prioritize relevant experiences, and adapt their behavior based on past interactions. Philosophical Foundation

Memory makes us who we are. It informs our decisions, shapes our behaviors, and allows us to adapt to the world around us. The SLASH Memory System mimics this human capacity by leveraging patterns and relevance in stored experiences.

Key concepts include:

Clusters as Contexts: Memory is grouped into clusters, each representing a theme, event, or behavior.
Bitmap Transparency: Layers of memory can be queried like stacked transparencies to identify overlapping patterns, enabling rapid pattern recognition.
Middle-Out Prioritization: Instead of emphasizing fleeting or obsolete experiences, the system prioritizes consistent and relevant memories.

This approach ensures that recent but irrelevant experiences don’t override critical, recurring patterns. Core Components

1. Memory Clusters

Clusters are conceptual groups of related memories. They store individual snapshots and provide a framework for querying associations and patterns.

Cluster Heat: Measures the activity or relevance of a cluster. Higher heat indicates recent or frequent use.
Bitmap Indexing: Uses bitmaps for efficient storage and querying of key and transform associations.

1. Memory Snapshots

Snapshots represent individual events or moments, capturing details such as:

Keys Involved: Which behaviors or actions occurred.
Mood: Emotional context during the snapshot.
Location: The agent’s position in the environment.

1. Bitmaps

Bitmaps assign unique identifiers to keys and transforms, allowing for:

Fast lookup and comparison.
Identification of patterns across clusters.

How It Works Pattern Recognition

By layering memories using bitmaps, the system can identify patterns like:

Behavioral Associations: For example, recognizing that shooting an ally often coincides with certain conditions.
Contextual Similarities: Understanding that a specific environment triggers similar behaviors.

Middle-Out Prioritization

The system emphasizes:

Relevant Memories: Memories that recur or align with current behavior are prioritized.
Consistent Patterns: Memories with repeated exposure are maintained over fleeting experiences.

Even if something happens recently, it may not be retained unless it shows long-term significance. Key Features

1. Dynamic Memory Management

   Short-Term Memory: Stores recent events for immediate analysis. Long-Term Memory: Preserves critical experiences for ongoing use. Cooling Mechanism: Gradually reduces the relevance of memories over time, simulating natural forgetting.

2. Cluster Operations

   Snapshot Matching: Identifies snapshots with overlapping key or transform associations. Cluster Heat Management: Adjusts the heat of clusters based on usage and relevance.

3. Certainty Metrics

Measures how closely the current situation matches stored memories, aiding in decision-making. Example: Behavioral Adjustment

Imagine an agent repeatedly shoots an ally when a specific condition occurs (e.g., being next to a friendly unit). The system:

Recognizes this pattern by querying memory clusters.
Deprioritizes behaviors releasing the associated keys.
Adapts future behavior to avoid similar mistakes.

Code Example: Memory Cluster Initialization

public class MemoryCluster
{
    public string memory; // Cluster name
    public float clusterHeat; // Relevance score
    public HashSet<MemorySnapshot> snapShotEntries = new HashSet<MemorySnapshot>();

    // Assign unique bitmaps to keys
    public Bitmap AssignIdentifierToName(string name, bool isKey)
    {
        var targetDict = isKey ? keyNamesToBit : transNamesToBit;
        if (!targetDict.ContainsKey(name))
        {
            byte newId = (byte)targetDict.Count;
            if (newId < 255)
            {
                Bitmap newBitmap = new Bitmap(newId);
                targetDict[name] = newBitmap;
                return newBitmap;
            }
        }
        return targetDict[name];
    }
}

Applications

1. Weighted Decision-Making

Memory impacts decision weights by:

Highlighting past successes or failures.
Adjusting key and lock attraction values.

1. Dynamic Adaptation

The system evolves with the agent, creating:

Personalized Behavior: Tailored responses based on memory.
Learning and Growth: Adapting to new contexts without forgetting critical patterns.

1. Realistic AI

The SLASH Memory System enhances realism by:

Providing context-aware behavior.
Simulating memory decay and prioritization.

Future Directions

1. Improved Eviction Strategies

   Refine middle-out memory eviction to better balance relevance and capacity.

2. Adaptive Clustering

   Allow clusters to dynamically evolve, merging or splitting based on context.

3. Integration with Ethics and Emotion

   Leverage emotional states and ethical evaluations to further influence memory retention and recall.

Conclusion

The SLASH Memory System represents an ambitious effort to mimic human memory in AI. By layering memories like transparencies, prioritizing relevance, and leveraging pattern recognition, it creates an agent capable of meaningful adaptation. In doing so, it lays a foundation for dynamic, memory-driven decision-making.