# Ai Symbolic Neuro

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- **Type:** Skill
- **Install:** `agentstack add skill-r-irbe-proof-skills-ai-symbolic-neuro`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [r-irbe](https://agentstack.voostack.com/s/r-irbe)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [r-irbe](https://github.com/r-irbe)
- **Source:** https://github.com/r-irbe/proof-skills/tree/main/skills/ai-symbolic-neuro
- **Website:** https://github.com/r-irbe/proof-skills

## Install

```sh
agentstack add skill-r-irbe-proof-skills-ai-symbolic-neuro
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Symbolic AI & Neuro-Symbolic Integration

Formal knowledge representation, symbolic reasoning, and their integration with neural methods for knowledge structuring and consolidation pipelines.

---

## Routing

- **USE FOR:** Symbolic AI, neuro-symbolic integration, knowledge representation and reasoning (KRR), ontology engineering, description logics, and hybrid symbolic-neural architectures. Use for reasoning about formal knowledge structures, rule-based systems, logic programming, semantic web technologies, and their integration with neural approaches in knowledge graphs and structured reasoning pipelines.
- **DO NOT USE FOR:** formalising the ontology in Lean (use @lean-knowledge-formalization); commonsense reasoning (use @ai-commonsense-reasoning); agentic AI (use @ai-agentic-evolving).
- **TRIGGERS:** symbolic AI, neuro-symbolic, knowledge representation, KRR, ontology engineering, description logic, hybrid AI.

## Workflow

1. Identify the integration question: symbolic-only, neural-only, or hybrid; pick the integration pattern (loose / tight / unified) from the body.
2. Pick the KRR formalism (description logic, RDF/OWL, conceptual graph, neural-symbolic embedding) that matches the application.
3. Apply the formalism; document where neural and symbolic components communicate + their semantic guarantees.
4. Hand off: to `@lean-knowledge-formalization` for Lean encoding, to `@math-graph-knowledge` for graph-theoretic foundations, to `@lean-zettelkasten`.

## Recovery & STOP

- STOP if the question is purely commonsense-reasoning — delegate to `@ai-commonsense-reasoning`.
- STOP if the question is about causal/deontic logic — delegate to `@ai-causal-deontic`.
- STOP if the neural component requires empirical evaluation beyond methodological reasoning — escalate to `@research-council`.

## Handoffs

- **Predecessors:** `agent:gateway`, `skill:lean-research`.
- **Successors:** `skill:lean-knowledge-formalization`, `skill:ai-commonsense-reasoning`, `skill:math-graph-knowledge`.

---

## Part 1 — Knowledge Representation Foundations

### 1.1 Representation Languages

| Language | Expressiveness | Decidability | Common relevance |
|---|---|---|---|
| Propositional logic | Low | P (SAT: NP-complete) | Gate predicates and finite checks |
| First-order logic (FOL) | High | Semi-decidable | Theorem formalization |
| Description logic (ALC) | Medium | ExpTime | Ontology TBoxes |
| OWL-DL | Medium-high | 2NExpTime | Semantic web ontologies |
| Datalog | Limited FOL | P (data complexity) | Recursive queries on knowledge graphs |
| Answer Set Programming | Nonmonotonic | ΣP2-complete | Default reasoning |
| Modal logic | FOL + modalities | Varies | Epistemic/deontic reasoning |

### 1.2 Ontology Engineering

**Foundational ontologies:**
- DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering)
- BFO (Basic Formal Ontology) — ISO 21838
- SUMO (Suggested Upper Merged Ontology)

**Design patterns:**
- Parthood and mereology
- Temporal entities (endurants vs perdurants)
- Roles and role-playing
- Quality and quality spaces

**Common application:** knowledge-graph quality gates validate ontological consistency:
- TBox coherence (no unsatisfiable concepts)
- ABox consistency (instances satisfy TBox constraints)
- Schema-instance alignment

### 1.3 Formal Reasoning Systems

| System | Approach | Completeness | Use |
|---|---|---|---|
| Tableaux | Model construction attempt | Complete for DL | Ontology reasoning |
| Resolution | Refutation | Complete for FOL | Theorem proving |
| Sequent calculus | Structural proof | Complete for FOL | Proof theory |
| Natural deduction | Introduction/elimination | Complete for FOL | Human-like proofs |
| Type theory | Curry-Howard | ??? (depends on system) | Lean 4 foundation |

---

## Part 2 — Neuro-Symbolic Integration

### 2.1 Integration Spectrum (Kautz 2020)

| Level | Description | Example | Pipeline role |
|---|---|---|---|
| 1 | Symbolic ← Neural | Neural generates symbolic output | Experience → symbols |
| 2 | Symbolic → Neural | Symbolic knowledge guides neural | Structuring → embedding |
| 3 | Hybrid pipeline | Neural + symbolic sequential | Full Project pipeline |
| 4 | Tightly coupled | Neural and symbolic share representation | Graph of Thoughts |
| 5 | Unified | Single system with both capabilities | Target architecture |

### 2.2 Key Architectures

- **Neural theorem proving**: Neural guide for tactic selection (GPT-f, AlphaProof)
- **Knowledge graph embeddings**: TransE, RotatE, CompGCN — vector representations of KG
- **Graph neural networks**: Message passing on knowledge structures
- **Neurosymbolic concept learner**: Learn visual concepts as logical programs
- **Logic tensor networks**: Differentiable first-order logic

### 2.3 Typical Neuro-Symbolic Position

Many verification-oriented pipelines operate at Level 3-4:
- **Ingest phase**: neural perception, extraction, or embedding.
- **Articulation**: neural-to-symbolic conversion from natural language to structured claims.
- **Structuring**: symbolic knowledge graph construction and validation.
- **Consolidation**: hybrid formal verification plus neural synthesis.
- **Application**: symbolic-to-neural feedback from validated knowledge to new tasks.

---

## Part 3 — Commonsense Reasoning

### 3.1 Commonsense Knowledge Types

| Type | Description | Formalization Challenge |
|---|---|---|
| Physical | Objects fall, liquids flow | Qualitative physics, spatial reasoning |
| Social | People have intentions, emotions | Theory of mind, folk psychology |
| Temporal | Events have duration, causation | Allen's interval algebra |
| Taxonomic | Dogs are animals | Inheritance hierarchies |
| Default | Birds fly (unless penguin) | Nonmonotonic logic |
| Causal | Pushing causes movement | Causal models (Pearl) |

### 3.2 Formal Approaches

- **Circumscription** (McCarthy): Minimize abnormality
- **Default logic** (Reiter): Default rules with exceptions
- **Answer set programming**: Stable models with negation-as-failure
- **Probabilistic logic**: Weight rules by confidence
- **Large language models**: Implicit commonsense from training data

### 3.3 Pipeline Integration

Commonsense reasoning commonly enters structured-AI pipelines in:
- **Articulation**: Interpreting tacit knowledge requires commonsense context
- **Structuring**: Default rules for knowledge graph completion
- **Quality gates**: Commonsense consistency checks on extracted knowledge
- **Trust dynamics**: Social commonsense for multi-agent coordination

---

## Part 4 — Knowledge Graph Reasoning

### 4.1 Reasoning Tasks

| Task | Input | Output | Method |
|---|---|---|---|
| Link prediction | $(h, r, ?)$ | Missing tail entity | Embedding + scoring |
| Type inference | Entity features | Entity type | Classification |
| Rule mining | KG triples | Horn rules | AMIE, AnyBURL |
| Query answering | Complex query | Answer entities | Query embedding |
| KG completion | Partial KG | Complete KG | Combination methods |
| Consistency checking | KG + constraints | Violations | DL reasoning |

### 4.2 Formal Quality Metrics

| Metric | Formula | Interpretation |
|---|---|---|
| Precision | TP / (TP + FP) | Fraction of predicted links that are correct |
| Recall | TP / (TP + FN) | Fraction of true links predicted |
| MRR | $\frac{1}{|Q|}\sum_{q \in Q} \frac{1}{\text{rank}_q}$ | Mean reciprocal rank of correct answers |
| Hits@K | Fraction of correct in top-K | Retrieval quality |
| Semantic validity | Ontology-consistency rate | Structural soundness |

### 4.3 Knowledge Graph Architecture

```
Experience → Raw triples (LLM extraction)
  → Articulation → Candidate assertions with confidence
    → Structuring → Validated KG with ontological constraints
      → Consolidation → Proven properties (Lean formalization)
        → Innovation → Novel inferences from verified base
```

Quality gates at each transition enforce:
- Confidence thresholds (configurable per domain)
- Ontological consistency (TBox reasoning)
- Provenance tracking (W3C PROV-O)
- Temporal validity (assertions expire)

---

## Part 5 — Host-Repository Lean Extension Points

Do not assume repository-local Lean modules, tactics, or namespaces exist unless the host repository explicitly provides them. When a repository has local knowledge-graph or quality-gate modules, map them to this generic checklist:

| Local extension point | Symbolic AI aspect | Typical structures |
|---|---|---|
| Provenance / audit trail module | DAG well-formedness, trust composition | stages, edges, well-formedness predicates |
| Causal / knowledge-graph module | Causal DAG and graph formalization | causal links, typed edges, confidence scores |
| Quality-gate module | Predicate logic on quality measures | gates, thresholds, monotonicity theorems |
| Classification / regime module | Decision procedures for regimes | regime types, decidability, monotonicity |
| Causal-reasoning skill handoff | Causal DAGs and counterfactual reasoning | delegate to `@lean-causal-reasoning` |

---

## Part 6 — Research Connections

### 6.1 Epistemic Mapping Targets

| KK (Known) | KU (Gap) | UU (To Discover) |
|---|---|---|
| DAG formalization | Full DL reasoning in Lean | OWL-to-Lean translation |
| Propositional gates | Nonmonotonic formalization | Commonsense integration |
| Trust composition | Neuro-symbolic verification | LLM-generated proof verification |
| Provenance chains | Temporal knowledge evolution | Ontology evolution formalization |

### 6.2 Key References

- Brachman & Levesque (2004) — Knowledge Representation and Reasoning
- Hitzler et al. (2020) — Neuro-Symbolic AI: state of the art
- Marcus & Davis (2019) — Rebooting AI (commonsense perspective)
- Hogan et al. (2021) — Knowledge Graphs (comprehensive survey)
- Davis (2015) — Commonsense reasoning: an event calculus perspective

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [r-irbe](https://github.com/r-irbe)
- **Source:** [r-irbe/proof-skills](https://github.com/r-irbe/proof-skills)
- **License:** Apache-2.0
- **Homepage:** https://github.com/r-irbe/proof-skills

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-r-irbe-proof-skills-ai-symbolic-neuro
- Seller: https://agentstack.voostack.com/s/r-irbe
- Browse the marketplace: https://agentstack.voostack.com/browse

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