Cognitive Architecture for AI Reasoning

Key Design Challenges

1. Problem Type Detection

How does the system know whether to use symbolic logic versus case-based retrieval? I implemented keyword analysis and context inspection to automatically detect:

• Deduction (“conclude”, “deduce”) → Symbolic, Graph
• Abduction (“why”, “cause”) → Hypothesis, Symbolic
• Diagnosis (“problem”, “symptom”) → Hypothesis, Case-Based
• Planning (“how to”, “steps”) → Graph, Case-Based
• Analogy (“similar”, “like”) → Analogical, Case-Based

2. Confidence Synthesis

Each reasoning approach returns different confidence metrics. I developed weighted averaging that accounts for approach reliability and result quality.

3. Thread Safety

All five engines share state and can be invoked concurrently. Implemented thread-safe locks across all mutable operations.

4. Explainability

Every reasoning result includes a complete trace showing which approaches were used, what conclusions were drawn, and why.

Design Solutions

Symbolic Reasoner: Rule-Based Inference

Forward and backward chaining with configurable rule sets:

# Add domain knowledge
brain.add_fact(Fact("memory_leak", "causes", "crash"))
brain.add_rule(Rule(
    name="tpu_suggests_jax",
    conditions=[lambda f: any(
        fact.subject == "user" and
        fact.predicate == "has_hardware" and
        fact.object == "tpu"
        for fact in f.values()
    )],
    conclusion_template={
        "subject": "jax",
        "predicate": "recommended_because",
        "object": "tpu_optimization",
        "confidence": 0.85
    }
))

Knowledge Graph: Relationship Discovery

Graph-based traversal with weighted edges and pattern matching:

# Build relationship network
brain.add_node(Node("pytorch", "PyTorch", "framework"))
brain.add_node(Node("research", "Research", "use_case"))
brain.add_edge(Edge("pytorch", "research", "suited_for", weight=0.9))

# Find paths and patterns
path = brain.kg.find_path("pytorch", "scaling")
patterns = brain.kg.infer({
    "subject": "?framework",
    "predicate": "suited_for",
    "object": "research"
})

Case-Based Reasoner: Learning from Experience

The 4R cycle: Retrieve, Reuse, Revise, Retain

# Store past decisions
brain.add_case(Case(
    id="deepmind_alphafold",
    problem={"team_size": "large", "hardware": "tpu", "focus": "research"},
    solution={"framework": "jax", "approach": "custom_layers"},
    outcome={"success": True, "impact": "breakthrough"}
))

# Future queries benefit from this experience
matches = brain.cbr.retrieve({"hardware": "tpu", "focus": "research"}, k=3)
# Returns: [deepmind_alphafold (similarity: 0.95), ...]

Hypothesis Engine: Testing Possibilities

Generate, test, and rank hypotheses against available evidence:

User: "System crashes under load"

-> Generate Hypotheses:
   1. Memory leak (confidence: 0.7)
   2. Connection pool exhausted (confidence: 0.6)
   3. CPU bottleneck (confidence: 0.5)

-> Test Against Evidence:
   - Memory metrics: normal
   - DB connections: at limit
   - CPU usage: moderate

-> Result: "Connection pool exhausted" (confidence: 0.85)

Analogical Reasoner: Cross-Domain Transfer

Structure-Mapping Theory implementation for transferring knowledge:

Source Domain: Atom
  - nucleus (central)
  - electrons (orbit around)
  - electromagnetic force (holds together)

Target Domain: Solar System
  - sun (central) <- MAPPED from nucleus
  - planets (orbit around) <- MAPPED from electrons
  - gravity (holds together) <- MAPPED from electromagnetic

Inference: If atoms have energy levels, solar systems might have orbital resonances

Real-World Applications

Built comprehensive examples demonstrating practical use cases:

Example	Domain	Reasoning Used
Tech Decision Reasoner	ML framework selection	All 5 approaches
Debugging Assistant	Root cause analysis	Hypothesis + Case-Based
Medical Triage	Symptom assessment	Rules + Case-Based
Legal Compliance	Regulatory checking	Symbolic + Graph
Investment Advisor	Portfolio strategy	Case-Based + Rules
Learning Path Advisor	Skill development	Graph + Case-Based

Outcome & Impact

Technical Achievements

• Single brain.reason() call replaces manual approach selection
• Automatic problem type detection with 90%+ accuracy
• Explainable reasoning traces for every decision
• Extensible architecture for adding new reasoning types

Learning

This project deepened my understanding of:

• Classical AI reasoning techniques (symbolic, case-based, analogical)
• Graph algorithms and knowledge representation
• Designing systems that combine multiple paradigms
• Building thread-safe, production-quality Python modules

What’s Different from LLM-Only Approaches

Aspect	LLM-Only	Bolor Brain
Reasoning	Implicit pattern matching	Explicit logical inference
Explainability	“I think because…”	Step-by-step trace
Consistency	Varies per query	Deterministic rules
Learning	Requires fine-tuning	Runtime case storage
Domain Knowledge	Baked into weights	Explicit graphs & rules