Symbolic Reasoning

SmartMemory's symbolic reasoning layer routes queries to the cheapest effective method, builds auditable proof trees, and scores confidence across multiple dimensions.

Components

Query Router

Routes queries to the best retrieval method based on pattern analysis:

Query Type	Method	Cost	Example
Symbolic	Cypher graph traversal	Free	"Who created Python?"
Semantic	Vector search	~$0.0001	"Find things related to machine learning"
Hybrid	Both + merge	~$0.0001	"Why did we choose PostgreSQL?"

Classification priority: hybrid patterns > semantic patterns > symbolic patterns > default (semantic).

Proof Trees

Build auditable reasoning chains by traversing the graph from a decision back to its evidence:

Decision: "Use PostgreSQL"
├── DERIVED_FROM: "PostgreSQL supports JSONB"
│   └── DERIVED_FROM: "Database comparison analysis"
├── DERIVED_FROM: "Team has PostgreSQL experience"
└── CONTRADICTS: "MySQL is simpler to set up"

Fuzzy Confidence

Score decisions across 4 dimensions:

Dimension	Weight	Description
Evidence	0.4	How many supporting edges exist
Recency	0.2	How recently the decision was made/reinforced
Consensus	0.2	Reinforcements vs contradictions
Directness	0.2	Depth of evidence chain (direct = 1.0)

Residuation

Pause decisions when data is incomplete:

Create a pending decision with requirements
When new data arrives, check if requirements are satisfied
Auto-activate the decision when all requirements are resolved
Pending decisions have confidence 0.0 (won't appear in high-confidence queries)

REST API

Method	Endpoint	Description
POST	`/memory/reasoning/query`	Route and execute a query
POST	`/memory/reasoning/proof`	Build proof tree for a decision
POST	`/memory/reasoning/fuzzy-confidence`	Get multi-dimensional confidence