HERMES serves as a critical "two-way bridge" between experiential learning systems and symbolic reasoning frameworks. This bidirectional knowledge transfer mechanism addresses a fundamental gap in current AGI architectures by:

1. Input Path: Transforms raw experiential data (perceptions and actions) from agents into structured causal graphs in Atomspace. This includes processing both historically grounded states and predicted future states.
2. Output Path: Translates symbolic plans, goals, and motivations from reasoning systems like MAGUS or PLN back into actionable commands and instructions for agents.

This bridge enables experiential learning to inform symbolic reasoning and vice versa, creating a powerful feedback loop that accelerates AGI development. The system dynamically builds, refines, and utilizes knowledge graphs that represent causal relationships between actions, environmental effects, and goal satisfaction.

Challenge and Opportunity

Current AGI frameworks struggle with the integration of experiential learning and symbolic reasoning. Systems like OpenPsi, PLN, and ECAN require structured experiential data to function effectively, but there is no standardized mechanism for translating agent experiences into symbolic structures or for applying symbolic reasoning results to guide agent behavior.

HERMES addresses this critical need by providing a systematic approach to:

• Transform messy, high-dimensional experiential data into clean, structured causal graphs
• Capture both the explicit actions and their effects on the environment
• Link these effects to changes in goal satisfaction or curiosity reduction
• Return actionable instructions derived from symbolic reasoning

Key Technical Features

1. Causal Graph Construction

HERMES transforms experience traces (action → state change → goal satisfaction) into symbolic triplets that capture the causal relationships between agent behavior and outcomes. These representations are:

• Expressed in MeTTa and stored in Atomspace for inference
• Annotated with confidence values based on observation frequency and consistency
• Structured to support both forward planning and backward reasoning

The system identifies and records state transitions that correlate with goal satisfaction changes, building a knowledge base of effective action patterns that grows and refines over time.

2. Multi-level Confidence Modeling

HERMES implements a sophisticated confidence system to handle uncertainty in both perception and action consequences:

• Action-State Confidence: Overall confidence about complete state transitions (e.g., "I'm 75% confident this action leads to this new state")
• Action-Value Confidence: Granular confidence about individual input/value changes (e.g., "I'm 100% confident this specific input will change by this amount")

This multi-level approach allows the system to reason effectively in uncertain environments, understanding both the overall likelihood of success and the specific details of expected changes.

3. AIRIS Integration

HERMES seamlessly integrates with AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism) to create a robust experiential learning pipeline:

• Translates AIRIS experiential data (time series of states, actions, and rewards) into atom structures
• Processes both grounded (observed) and predicted (expected) states
• Maintains confidence metrics for state transitions and value changes
• Returns processed plans and goals to AIRIS for action execution and world model updating

This integration creates a complete cycle where experiences inform symbolic reasoning, and reasoning results guide future experiences.

4. Atomspace & MeTTa Integration

HERMES is designed from the ground up to work within the Hyperon ecosystem:

• Constructs symbolic causal subgraphs directly in the Atomspace memory structure
• Serializes all graph structures as MeTTa expressions
• Supports direct querying and modification in MeTTa scripts
• Leverages pattern-matching and transformation primitives for symbolic compression
• Optimizes storage with MORK for persistence and efficient retrieval

Evaluation Approach

HERMES will be evaluated across four key dimensions:

1. Causal Fidelity and Goal Correlation

• Statistical correlation between constructed causal paths and actual agent success
• Mutual information scores between predicted and observed outcomes
• False positive/negative causal link ratios in various test environments

2. Reasoning Utility in MAGUS

• Integration with MAGUS decision-making processes
• Measurement of action success rate improvements compared to baseline
• Planning depth efficiency and reduction in search space

3. Symbolic Compression and Representational Quality

• Quantitative assessment of graph compactness and cohesion
• Evaluation of symbolic redundancy scores
• Analysis of how effectively the system distills long traces into reusable substructures

4. Performance and Scalability

• Benchmarking of construction and export times with increasing graph complexity
• Memory usage tracking during operation
• Integration latency with MORK and Atomspace under various load conditions

Applications and Impact

HERMES will enable a new generation of applications that bridge experiential learning and symbolic reasoning:

• Intelligent NPCs with believable behaviors that learn from their experiences
• Explainable AI systems with transparent causal reasoning about their decisions
• Goal-oriented assistants that understand user satisfaction and optimize accordingly
• Adaptive planning systems that refine their strategies based on outcomes

In the specific context of the SingularityNET ecosystem, HERMES addresses a critical missing piece that will dramatically accelerate AGI development by enabling:

• OpenPsi motivational systems to leverage structured experiential data
• PLN reasoning to operate on grounded causal relationships
• ECAN attention allocation based on goal-relevant experience patterns
• Neoterics agents to develop sophisticated causal models of their virtual worlds

By functioning as a two-way bridge between experience and reason, HERMES will help unite the subsymbolic and symbolic approaches to AGI, creating more capable, adaptive, and aligned intelligent systems.

For detailed technical specifications and mathematical formalism, please see the attached Technical Appendix.