AnyCog is a comprehensive framework for building integrated, multi-paradigm simulation models using AnyLogic with an OpenCog-inspired cognitive architecture. It enables seamless integration of Discrete-Event Simulation (DES), Agent-Based Modeling (ABM), System Dynamics (SD), Material Handling (MH), Movement, and Fluid dynamics into a unified cognitive system.
NEW: AnyCog now includes a Niche Construction Engine that enables self-assembly of intelligence density around domains of inquiry using modular simulation components as a cognitive grammar.
- AtomSpace: Hypergraph knowledge representation for all simulation components
- Multiple Cognitive Processes: Specialized engines (DES, ABM, SD, MH, Movement, Fluid) working in harmony
- Meta-Cognitive Layer: Pattern recognition and insight generation across paradigms
- Autognosis Layer: Self-awareness and continuous self-optimization
- Components communicate through a unified AtomSpace
- Cross-paradigm feedback loops are first-class citizens
- Integration patterns handle synchronization and state consistency
- Message-based communication ensures loose coupling
- Insights emerge from component interactions that would be invisible in single-paradigm models
- Meta-cognitive processes discover patterns across integrated subsystems
- Higher-order understanding from multiple levels of abstraction
- Autognosis layer monitors its own architecture
- Automatically identifies integration inefficiencies
- Proposes and executes optimizations
- Continuously evolves toward better performance
- 18+ Reusable Components: Perception, Reasoning, Action, Memory, Communication, Meta components
- Domain-Agnostic Design: Components work across different problem domains
- Composable Architecture: Build complex systems from simple, tested parts
- Self-Describing: Components publish capabilities and requirements
- Formal Composition Rules: Type checking, temporal ordering, resource conservation
- Validation: Automatic checking of architecture correctness
- Pattern Library: Proven component combinations for common scenarios
- Extensible: Add new rules and patterns as needed
- Self-Assembly: Architectures construct themselves from domain descriptions
- Domain Analysis: Automatically characterizes problem domains
- Pattern Matching: Finds optimal architectural patterns
- Component Selection: Chooses best components for the niche
- Intelligence Density Optimization: Maximizes capability per unit complexity
- Continuous Learning: Improves from experience
anycog/
├── SKILL.md # Main workflow documentation
├── README.md # This file
├── references/
│ ├── opencog_architecture.md # Core architecture specification
│ ├── integration_guide.md # Detailed integration patterns
│ ├── autognosis_integration.md # Self-optimization documentation
│ ├── modular_components.md # NEW: Modular component library
│ ├── cognitive_grammar.md # NEW: Composition rules and patterns
│ ├── niche_construction.md # NEW: Self-assembly engine
│ ├── domain_patterns.md # NEW: Pattern catalog
│ ├── paradigms.md # Modeling paradigm reference
│ ├── psystem_membrane_computing.md # P-system reference documentation
│ ├── process_modeling_library.md # DES blocks reference
│ ├── material_handling_library.md # MH blocks reference
│ ├── quick_reference.md # Developer quick reference
│ └── other_libraries.md # Movement & Fluid blocks reference
├── examples/
│ ├── integrated_supply_chain.md # Complete integrated example
│ ├── psystem_sat_solver.md # P-system SAT solver example
│ ├── torch_nn_neural_network.md # Neural network training with torch/nn
│ ├── self_assembly_emergency_dept.md # NEW: Self-assembly demonstration
│ ├── basicmodels/ # AnyLogic basic examples
│ ├── bigbookmodels/ # AnyLogic advanced examples
│ ├── models/ # Additional examples
│ └── sdmodels/ # System dynamics examples
For automatic architecture construction:
- Describe your domain in natural language
- Run the niche construction engine to auto-generate architecture
- Validate and deploy the assembled model
See:
- Niche Construction Engine: Self-assembly process
- Self-Assembly Example: Complete demonstration
Start by reading the core architecture document:
- OpenCog Architecture: Learn about AtomSpace, cognitive processes, and the layered architecture
Learn about reusable building blocks:
- Modular Components: 18+ component types
- Cognitive Grammar: Composition rules
- Domain Patterns: 8 proven patterns
The complete workflow is documented in:
- SKILL.md: Step-by-step guide to building integrated models
The 7-step workflow:
- Identify Required Components: Which cognitive processes are needed?
- Design the AtomSpace Schema: Define nodes and links for your domain
- Structure the Integrated Model: Plan how components work together
- Implement Component Integration: Build with proper registration and communication
- Enable Meta-Cognition: Activate pattern recognition and coherence checking
- Analyze Results with Cognitive Synergy: Extract cross-paradigm insights
- Apply Autognosis: Let the system optimize itself
Learn how to integrate different paradigms:
- Integration Guide: Detailed patterns and protocols
Common patterns:
- DES + ABM: Agents with behaviors moving through processes
- SD + DES: Aggregate dynamics affecting process parameters
- ABM + SD: Individual decisions affecting system-level stocks
- Multi-Engine: Full cognitive synergy with all components
See complete implementations:
- Self-Assembly Emergency Department: NEW - Automated architecture construction
- Integrated Supply Chain Example: Full model with all cognitive layers
- P-System SAT Solver: Massively parallel computation for NP-complete problems
- Torch/nn Neural Network: Neural network training as a cognitive architecture
The examples demonstrate:
- Self-assembly of cognitive architectures from domain descriptions (NEW)
- Multi-paradigm integration (SD + DES + ABM + MH + P-System)
- Cross-paradigm feedback loops
- Meta-cognitive pattern mining
- Autognosis-driven optimization
- Neural architecture search and self-improvement
A hypergraph containing all simulation knowledge:
Nodes:
ConceptNode: Types and conceptsEntityNode: Individual instancesProcessNode: Active processesStateNode: System statesParameterNode: Configuration valuesMetricNode: Measurements
Links:
InheritanceLink: Type hierarchiesTransitionLink: State changesInfluenceLink: Causal relationshipsInteractionLink: Agent communicationsContainmentLink: Hierarchical structureTemporalLink: Time ordering
Reusable building blocks for cognitive architectures:
Component Categories:
- Perception: Sensors, counters, pattern detectors
- Reasoning: Classifiers, optimizers, predictors, decision-makers
- Action: Generators, transformers, routers, allocators
- Memory: Buffers, databases, caches
- Communication: Broadcasters, subscribers, aggregators
- Meta: Monitors, learners, reflectors
Each component:
- Is domain-agnostic (works across problem domains)
- Has clear interfaces (declared inputs/outputs)
- Is composable (combines with other components)
- Is self-describing (publishes capabilities)
See Modular Components for full catalog.
Formal rules for composing components into valid architectures:
Core Rules:
- Type Matching: Output types must match input types
- Temporal Ordering: Causality preserved in execution order
- Resource Conservation: All generated entities have lifecycle endpoints
- Feedback Validity: Cycles must have break conditions
- State Consistency: Shared state must be synchronized
Composition Patterns:
- Linear Pipeline (sequential processing)
- Branching Decision (conditional routing)
- Feedback Loop (iterative refinement)
- Parallel Composition (concurrent operations)
- Hierarchical Structure (nested systems)
- Observer Pattern (passive monitoring)
See Cognitive Grammar for complete specification.
Automated system for self-assembling cognitive architectures:
Process:
- Domain Analysis: Extract characteristics from description
- Pattern Matching: Find optimal architectural patterns
- Component Selection: Choose best components for niche
- Architecture Composition: Assemble according to grammar
- Validation: Ensure correctness
- Performance Prediction: Estimate fitness
- Evolution: Iteratively improve
- Meta-Learning: Capture experience for future use
Intelligence Density:
Intelligence Density = (Capability + Synergy) / Complexity
Higher density = more problem-solving power per unit of model complexity.
See Niche Construction for complete specification.
Specialized engines operating on the AtomSpace:
- Process Flow Engine (DES): Sequential processes, queues, resources
- Agent Behavior Engine (ABM): Autonomous agents, statecharts, interactions
- System Dynamics Engine (SD): Stocks, flows, feedback loops
- Material Flow Engine (MH): Conveyors, transporters, warehouses
- Movement Engine: Pedestrians, vehicles, trains
- Fluid Dynamics Engine: Tanks, pipelines, continuous flows
- P-System Membrane Computing: Bio-inspired parallel computation, membrane hierarchies, massively parallel rule execution
Higher-order pattern recognition:
- Pattern Miner: Discovers recurring patterns
- Coherence Checker: Validates consistency
- Optimization Detector: Identifies improvements
- Insight Generator: Produces human-readable insights
Self-awareness and optimization:
- Self-Monitor: Observes all cognitive processes
- Self-Modeler: Builds hierarchical self-images
- Meta-Cognitive Analyzer: Generates insights about architecture
- Self-Optimizer: Proposes and executes improvements
- All components share knowledge through the AtomSpace
- Single source of truth for all simulation state
- Typed nodes and links ensure semantic clarity
- Components communicate through well-defined interfaces
- Message-based communication via MessageBus
- No direct dependencies between engines
- Unified simulation clock across all paradigms
- Strict event ordering preserves causality
- Synchronization strategies handle different time scales
- Components designed to enhance each other
- Cross-paradigm feedback loops encouraged
- Emergent insights from component interactions
- SD: Demand forecasting, inventory policies
- DES: Order fulfillment, production processes
- ABM: Customer purchasing behavior
- MH: Warehouse and transportation operations
- DES: Production process flow
- MH: Material handling (AGVs, conveyors)
- Fluid: Chemical batch processing
- ABM: Worker behavior and learning
- SD: Overall throughput dynamics
- P-System: Parallel scheduling optimization
- Movement: Pedestrian, vehicle, and rail flows
- SD: Infrastructure capacity modeling
- ABM: Individual travel decisions
- DES: Transit scheduling
- DES: Patient flow through departments
- ABM: Individual patient and staff behaviors
- SD: Resource capacity planning
- Meta-Cognitive: Bottleneck identification
- P-System: Cellular processes and membrane dynamics
- ABM: Cell-to-cell interactions
- SD: Population-level biological dynamics
- Meta-Cognitive: Emergent pattern discovery
- P-System: Massively parallel search (NP-complete problems)
- DES: Sequential constraint checking
- ABM: Distributed algorithm coordination
- Meta-Cognitive: Solution quality analysis
- DES: Training loop iterations, batch processing
- ABM: Individual neuron behaviors, weight updates
- SD: Learning rate schedules, gradient dynamics
- P-System: Parallel layer computations, backpropagation
- Meta-Cognitive: Architecture optimization, hyperparameter tuning
- Autognosis: Neural architecture search, self-improving networks
- Reduced Complexity: Framework handles integration boilerplate
- Higher Quality: Coherence checking catches errors early
- Better Insights: Meta-cognitive layer discovers non-obvious patterns
- Continuous Improvement: Autognosis optimizes over time
- More Accurate Models: Multi-paradigm integration captures reality better
- Faster Development: Reusable patterns and proven architectures
- Maintainable Code: Clear separation of concerns and documentation
- Future-Proof: Extensible architecture adapts to new requirements
- Novel Insights: Cognitive synergy reveals emergent phenomena
- Reproducible: Well-documented architecture and integration patterns
- Extensible: Easy to add new cognitive processes or meta-cognitive rules
- Self-Documenting: AtomSpace schema serves as living documentation
- AtomSpace caching strategies
- Message batching for reduced overhead
- Synchronization strategy selection
- Profiling and bottleneck identification
- Extending the framework with new engines
- Defining custom node and link types
- Creating specialized integration patterns
- Writing custom pattern mining rules
- Defining coherence checking constraints
- Creating domain-specific optimizations
- Manual vs. automatic optimization modes
- Risk assessment for proposed changes
- Continuous improvement cycles
When extending AnyCog:
- Follow the Architecture: Use AtomSpace for all shared state
- Document Integration Points: Specify what your component reads/writes
- Enable Meta-Cognition: Make your component observable
- Support Autognosis: Allow architectural introspection
- Test Integration: Validate with existing components
- Add Examples: Demonstrate your contribution in context
- OpenCog Project: https://opencog.org
- AnyLogic Simulation Software: https://www.anylogic.com
- Cognitive Architecture Literature: See
references/directory
This framework is based on analysis of official AnyLogic examples and implements an OpenCog-inspired architecture for simulation modeling.
AnyCog Framework v1.0 - OpenCog-Inspired Multi-Paradigm Simulation Architecture
Built on: Analysis of 400+ AnyLogic example models
Inspired by: OpenCog cognitive architecture
Purpose: Differentiate all features and functions to integrate as a single cohesive whole with all components as a unified cognitive simulation architecture