What Mathison is and why we need this parallel cognitive architecture

Design of the System with Parallel Agents and Hybrid LLMs

This might be the Yellow Paper - WIP:

1. Agent Specialization

Each agent in the system specializes in a specific domain or task, leveraging different AI capabilities suited to their particular needs. For example:

Customer Service Agent: Uses an LLM trained for understanding and generating natural language to interact with customers.
Data Analysis Agent: Employs an LLM specialized in extracting insights from large datasets.
Content Creation Agent: Utilizes a generative LLM to produce written content, reports, or marketing materials.

2. Task Allocation and Load Balancing

The system dynamically assigns tasks to agents based on their expertise, current workload, and priority of tasks. This ensures optimal use of resources and maintains high efficiency. Load balancing mechanisms distribute the workload evenly among agents to prevent any single agent from becoming a bottleneck.

3. Inter-Agent Communication

Parallel agents communicate with each other directly (peer-to-peer) to share insights, coordinate tasks, pass on responsibilities and assign credit. This communication is essential for tasks that require collaboration or when an agent’s output forms the input for another agent.

The data management system ensures that all agents have access to the latest data, maintaining consistency and accuracy across the system. This setup supports real-time decision-making and allows agents to leverage shared insights to improve their performance.

5. Redundancy and Fault Tolerance

The parallel structure inherently provides redundancy; if one agent fails, others can take over its tasks, ensuring continuity of operations. Fault tolerance is built into the system, allowing it to handle and recover from failures gracefully.

6. Continuous Learning and Adaptation

Agents use all forms of AI to continuously improve their performance based on new data and interactions. This ongoing learning process is crucial for adapting to changes in the environment or operational requirements. Feedback loops between agents can accelerate learning and optimization across the system. Feedback loops between humans ensure integrity.

7. Governance and Oversight

A human led oversight mechanism monitors the performance and behavior of all agents, ensuring they adhere to predefined guidelines and ethical standards.

Benefits of Using Parallel Agents with a Hybrid LLM Strategy

Scalability: The system can easily scale up by adding more agents as the demand increases.
Resilience: Multiple agents working in parallel provide robustness against failures or spikes in demand.
Efficiency: Specialized agents can handle tasks more efficiently, and parallel processing reduces response times.
Adaptability: The system can adapt more quickly to new requirements or changes in the operational environment.

Challenges Solved

Complexity: Mathison solves the complexity problem of a large number of autonomous agents and ensures they work harmoniously and robustly.
Coordination Overhead: Effective coordination among agents is crucial and Mathison reduces significant computational resources and enables sophisticated algorithms.
Consistency: Mathison ensures data consistency across multiple agents, in real-time applications.

All Five Schools of Artificial Intelligence are leveraged

Inspired heavily by Pedro Domingos' Five Tribes: Mathison has parallel agents that integrates the methodologies of all five schools of AI - Symbolists, Connectionists, Evolutionaries, Bayesians, and Analogizers - leveraging the unique strengths of each tribe which optimizes performance across a wide range of tasks by employing specialized agents for different roles and responsibilities.

1. System Architecture and Design

Decentralized Distributed Agent Framework: A decentralized structure where agents operate independently but can communicate and collaborate when necessary. This architecture allows for resilience and scalability, as each agent can function autonomously without a single point of failure.

Construct-Oriented Agents: Agents offer specific services based on their AI specialty. For example, Symbolist agents can handle rule-based tasks, while Connectionist agents manage pattern recognition and prediction tasks. The Construct bundles agents together to accomplish complex goals.

Dynamic Task Allocation: The scheduler dynamically assigns tasks to agents based on their expertise, current workload, and task requirements. This ensures that each task is handled by the most suitable agent, optimizing processing time and resource utilization.

2. Integration of all AI Schools

Symbolist Agents: Use these agents for tasks that require explicit reasoning and rule-based processing, such as compliance checks, logical problem solving, and executing defined procedures. See Marvin Minsky for inspiration.

Connectionist Agents: Deploy these agents for image recognition, natural language processing, or any task that benefits from learning from large datasets. They can adjust their behavior based on new information, making them ideal for dynamic environments. See large language models for inspiration.

Evolutionary Agents: Utilize these agents for optimization problems, such as resource allocation, routing, and scheduling tasks. They can evolve their strategies over time to find the most efficient solutions. See John H. Holland and Melanie Mitchell for inspiration.

Bayesian Agents: Assign these agents tasks that involve dealing with uncertainty and probabilistic reasoning, such as predictive maintenance or risk assessment. They can update their beliefs based on new evidence, improving their decision-making accuracy. See spam detection for a good example.

Analogizer Agents: Use these agents for classification and pattern matching tasks, such as customer support matching or medical diagnosis, where drawing similarities between cases is beneficial. See Douglas Hoftstadter and Goedel, Escher, Bach for inspiration.

3. Communication and Collaboration

Agent Communication Network: Robust communication protocol that allows agents to exchange information and learn from each other. This is facilitated by message passing and occasionally decentralized ledger technology for transparency and security.

Collaborative Decision-Making: Enables agents to participate in collaborative decision-making processes where complex tasks are decomposed into subtasks handled by different types of AI agents. The results are then aggregated to produce a final decision or output.

4. Learning and Adaptation

Continuous Learning Framework: Each agent is learning from the outcomes of its actions and from the shared experiences of other agents. Humans In The Loop, reinforcement learning, transfer learning, or federated learning facilitate this.

Feedback Loops: Feedback loops are incorporated where agents refine their algorithms based on the results of their actions and human feedback. This helps in continuous improvement and adaptation to changing conditions.

5. Resource Management and Optimization

Resource Orchestration: System monitors and manages resources efficiently, ensuring that agents have the necessary computational power and data access when needed. Reduces overall compute cost, data storage cost as well as network costs. Overall more efficient than most cloud/worker solutions.

Performance Monitoring: Uses meta-cognition as a comprehensive monitoring system that tracks the performance of each agent and the overall system. This is the executive system.

6. Security and Compliance

Security Protocols: Humans ensure all agents adhere to stringent security protocols, particularly in data handling, privacy, and interactions. Uses encryption and secure communication channels to protect sensitive information.

Compliance and Governance: Humans establish the governance framework to ensure that the system complies with relevant laws and regulations, and that ethical AI practices are followed in congruence with company policies.

This type of system, with its integration of multiple AI methodologies and decentralized, parallel agent structure, is highly flexible and capable of handling a diverse array of tasks efficiently and effectively. It exemplifies a sophisticated approach to cognitive system design, leveraging the collective strengths of various AI schools for superior performance and adaptability.

Executive Function

Mathison’s novel executive functioning layer is a system with parallel agents that integrates all five AI schools adding a strategic oversight and coordination level. This executive layer manages the overall workflow, ensures coherent interactions between agents, and handles overarching tasks like goal setting, priority management, and resource distribution.

1. System Architecture of The Executive Layer

Hierarchical Structure: A hierarchical system structure where the executive functioning layer sits at the top. This layer is responsible for high-level decision-making, orchestrating the activities of all agents, and ensuring that the system’s overall objectives are met.

Executive Agents: These agents, part of the executive layer, do not perform regular tasks but instead focus on managing and optimizing the performance of other agents. They monitor system health, allocate resources, and direct lower-tier agents on priority tasks.

Construct-Oriented Agents: Beneath the executive layer, construct-oriented agents are specialized according to the five AI schools. Each agent type handles tasks suited to their specific methodologies, as previously detailed.

2. Functionality of the Executive Layer

Task Allocation and Scheduling: The executive layer assesses incoming tasks and determines the most appropriate type of agent (or combination of agents) for each task based on complexity, urgency, and strategic importance. It schedules tasks and allocates resources accordingly.

Monitoring and Adaptation: This layer continuously monitors the performance and efficiency of all agents. It adjusts strategies in real-time, reallocates resources to meet demand, and ensures that the system adapts to changing conditions and requirements.

Conflict Resolution and Policy Enforcement: The executive layer resolves conflicts between agents and ensures adherence to predefined policies and ethical guidelines. It acts as a mediator to ensure smooth operation across the system.

Global Optimization: Unlike individual agents that optimize local outcomes, the executive layer looks at global performance metrics to optimize the overall system performance.

3. Integration of all AI Schools within the Executive Layer

Symbolist and Bayesian Insights: Uses Symbolist logic for rule-based governance and Bayesian statistics for decision-making under uncertainty, which are crucial for the executive layer’s adaptive strategies.

Connectionist Processing: Leverages neural networks within the executive layer to make predictions, forecasts and analyze patterns across the entire network of agents.

Evolutionary Algorithms: Employs evolutionary algorithms to optimize scheduling and resource allocation tasks, continuously evolving the system’s efficiency. The ultimate set of A/B tests.

Analogizer Capabilities: Utilizes analogy-based reasoning to benchmark and evaluate agent performance based on historical data, improving decision-making about agent deployment and task allocation.

4. Communication and Coordination

Centralized Dashboard: Implements a centralized control dashboard for the executive layer, providing a comprehensive view of all system operations, agent status, and task progression.

Inter-Agent Communication: Facilitate robust communication channels between the executive layer and service agents, ensuring clear task directives, feedback loops, and real-time updates.

5. Learning and Evolution

System-Wide Learning Initiatives: The executive layer manages a system-wide learning protocol where insights and improvements are shared across all agents. It oversees the implementation of advanced learning techniques such as transfer learning and reinforcement learning.

Feedback Loops: Integrates feedback mechanisms that allow the executive layer to receive input from humans and human defined operational outcomes to refine system strategies and improve decision-making processes.

6. Security, Compliance, and Ethics

Overarching Security Management: The executive layer handles overall system security, ensuring compliance with international standards and local regulations.

Ethical AI Oversight: Maintain an ethical AI oversight function within the executive layer to monitor and guide the ethical implications of AI decisions and actions across the system.

This setup not only provides strategic control but also ensures adaptability and robustness in the system's operations.