**Purpose:** Establish a precise conceptual vocabulary for adaptive information-processing networks operating on high-level semantic information objects.
### 1. Intent
An **Adaptive Information Processing Network (AIPN)** is a persistent network of semantic information-processing entities whose internal state changes in response to information arriving from its environment.
The network is intended to learn regularities in its own operation and in the environment with which it interacts. Learning modifies future activation, prediction, selection, and eventually network structure.
Unlike conventional artificial neural networks, the primary entities of an AIPN are intended to retain explicit domain semantics.
Typical nodes may represent concepts such as:
-`DocumentType`
-`Invoice`
-`EmployeeRole`
-`WorkstationClass`
-`SupplierClass`
-`Process`
-`Capability`
-`Goal`
-`Risk`
-`Observer`
Concrete entities such as a particular invoice, employee, document, machine, message, or material occur as information objects that activate parts of the network.
The network therefore operates at a semantic level suitable for organizational, business, technical, and socio-technical systems.
### 2. Foundational Idea
The system distinguishes between:
1.**persistent semantic structure**, and
2.**temporary concrete activation**.
A node such as:
```text
Invoice
```
may persist for years.
A concrete invoice:
```text
Invoice #4711
```
may activate that node during a processing episode.
The persistent network accumulates knowledge from many such episodes.
The foundational processing cycle is:
```text
Environment
↓
Information Object
↓
Semantic Activation
↓
Activation Propagation
↓
Prediction / Selection / Action
↓
Environmental Consequence
↓
Observation
↓
Feedback Signal
↓
Learning
↓
Modified Network
```
The network is therefore both:
- a model of its environment, and
- an adaptive mechanism participating in that environment.
### 3. Design Principles
#### 3.1 Semantic explicitness
Nodes and edges SHOULD have explicit meaning.
The system SHOULD prefer interpretable representations such as:
```text
Invoice → requires → Approval
```
over anonymous latent dimensions whenever doing so does not fundamentally prevent useful learning.
#### 3.2 Structure and activation are distinct
Persistent knowledge MUST be distinguishable from temporary processing state.
The existence of:
```text
Invoice → Approval
```
does not imply that either node is currently active.
#### 3.3 Observation and evaluation are distinct
The system MUST distinguish:
```text
what happened
```
from:
```text
whether what happened was desirable.
```
An observation is evidence.
A signal is an evaluation or regulatory consequence derived from evidence.
#### 3.4 Prediction and utility are distinct
An event can be highly probable but undesirable.
For example:
```text
Complaint → ManualEscalation
```
may have:
```text
probability = 0.92
utility = -0.63
```
The network MUST therefore avoid reducing expectation and desirability to a single weight.
#### 3.5 Learning is local where possible
Learning SHOULD be attributable to identifiable nodes, edges, episodes, and signals.
This supports:
- explainability,
- debugging,
- governance,
- intervention,
- comparison,
- rollback.
#### 3.6 Delayed consequences are expected
Actions frequently produce consequences much later.
The network SHOULD retain temporary evidence that earlier activations contributed to later outcomes.
This mechanism is represented by **eligibility traces**.
#### 3.7 Goals belong to the modeled world
Goals SHOULD be represented within the network rather than existing solely as hidden implementation configuration.
Examples:
```text
MinimizeCost
ReduceLatency
AvoidFraud
MaintainCompliance
PreserveHumanAttention
```
Goal activation may vary with context.
#### 3.8 Viability precedes optimization
The network SHOULD distinguish:
```text
remaining within viable operating bounds
```
from:
```text
optimizing preferred outcomes.
```
Some states may therefore be unacceptable regardless of their apparent local utility.
#### 3.9 Learning and authority are distinct
Learning that an action appears beneficial MUST NOT imply that the system is authorized to perform it.
The system MUST distinguish at least:
```text
can
may
does
should
must
```
where appropriate.
This permits learning under constrained authority.
#### 3.10 Higher-order structure may emerge
Repeated or coherent patterns MAY be represented as new network entities.
The network can therefore progressively construct abstractions from recurrent processing structure.
A **Node** is a persistent semantic entity capable of participating in information processing.
A node represents a meaningful distinction recognized by the network.
Examples:
```text
Invoice
EmployeeRole
WorkstationClass
OCRCapability
FraudRisk
HumanReview
CustomerComplaint
```
A node SHOULD possess stable identity independent of its temporary activation state.
Conceptually:
```yaml
node:
id: Invoice
kind: DocumentType
```
Useful semantic kinds include:
```text
EntityType
State
Role
Capability
Process
Resource
Context
Goal
Risk
Observer
Policy
Concept
```
A node MAY maintain learned or temporary state including:
```text
activation
frequency
recency
confidence
memory
statistics
prediction models
utility estimates
```
### 6. Token
A **Token** is a concrete information object participating in a processing episode.
Examples:
```text
Invoice #4711
Employee Alice
Workstation WS-328
Shipment #91284
Customer Request #882
```
A token MAY activate multiple nodes simultaneously.
Example:
```text
Invoice #4711
```
might activate:
```text
Invoice
SupplierInvoice
PDFDocument
HighValueTransaction
Supplier:Acme
MonthEnd
```
Tokens MAY be transient, persistent, externally referenced, or reconstructed from event history.
### 7. Edge
An **Edge** is a persistent relationship between network entities.
Example:
```text
Invoice → requires → Approval
```
Edges SHOULD be typed.
Possible examples include:
```text
is-a
part-of
uses
requires
precedes
associated-with
predicts
enables
inhibits
routes-to
observed-by
contributes-to
```
An edge SHOULD NOT be reduced to a single scalar weight.
A useful conceptual form is:
```yaml
edge:
source: Invoice
target: HumanReview
relation: routes-to
association: 0.42
transition_probability: 0.18
confidence: 0.91
utility:
correctness: 0.94
latency: 0.31
cost: 0.22
activation: 0.12
```
### 8. Activation
**Activation** represents the current relevance, readiness, or excitation of a network entity.
Activation may originate from incoming tokens, neighboring nodes, current goals, context, prediction, surprise, explicit attention, regulatory state, or remembered association.
Activation SHOULD normally decay over time unless sustained.
### 9. Context
A **Context** is a semantic representation of conditions that modulate processing.
Examples:
```text
MonthEnd
EmergencyOperation
HighSystemLoad
CustomerFacing
ProductionEnvironment
NightShift
```
The network SHOULD be capable of distinguishing:
```text
A → B
```
from:
```text
A + Context X → B
```
### 10. Episode
An **Episode** is a bounded trace of related network activity.
An episode MAY contain input tokens, activated nodes, traversed edges, contexts, predictions, actions, observations, and resulting signals.
Episodes provide a unit for learning, replay, explanation, comparison, attribution, experimentation, and auditing.
### 11. Prediction
A **Prediction** expresses an expectation concerning future observations or network states.
Prediction error arises from the difference between expected and observed outcomes.
Both occurrences and absences may be informative.
### 12. Action
An **Action** is an intervention generated or selected by the network that may affect its environment.
An **Observer** is an entity that evaluates or interprets observations, states, episodes, or consequences.
Multiple observers MAY interpret the same episode differently.
### 14. Signal
A **Signal** is evaluative or regulatory information capable of changing future network behavior.
Signals may represent success, failure, reward, penalty, prediction error, novelty, surprise, constraint violation, human feedback, or viability deviation.
Signals SHOULD normally permit multiple dimensions.
### 15. Goal
A **Goal** is a modeled desired tendency, condition, outcome, or constraint.
Goals participate in processing through activation.
Goal conflicts are expected and SHOULD remain visible rather than silently flattened.
### 16. Viability Variable
A **Viability Variable** represents a system condition that should remain within an acceptable operating range.
Examples:
```text
QueueDepth
CashLiquidity
SecurityExposure
OperatorLoad
CustomerWaitingTime
ComputeCapacity
ErrorRate
```
Deviation from viable ranges MAY itself create activation and signals.
### 17. Eligibility Trace
An **Eligibility Trace** is temporary evidence that a previous activation, edge traversal, prediction, or action may have contributed to a later outcome.
The network SHOULD distinguish co-occurrence, ordering, duration, delay, recency, and periodicity.
### 27. Causality
The network MUST distinguish association from causation.
Causal confidence MAY be increased through interventions, controlled experiments, repeated counterfactual evidence, external knowledge, or structural assumptions.
### 28. Exploration and Exploitation
A purely exploitative network risks becoming trapped in existing behavior.
The system SHOULD permit controlled exploration.
### 29. Plasticity
Not every network component should learn equally quickly.
A useful distinction is:
```text
fast plasticity
slow plasticity
frozen structure
```
### 30. Consolidation
Frequently reinforced patterns MAY transition from flexible to stable representation.
### 31. Forgetting
The network SHOULD permit information to weaken.
Possible mechanisms include activation decay, confidence decay, edge weakening, evidence aging, and chunk dissolution.
The network predicts outcomes, selects actions, observes results, and updates eligible structures.
### 36. Example of Surprise
If `ApprovedInvoice → Payment` is expected but `ApprovedInvoice → CustomerComplaint` occurs, the event creates surprise and may activate investigation, fraud risk, process anomaly, or human attention.
### 37. Human Interaction
Humans MAY interact with the network by providing tokens, activating goals, providing explicit feedback, authorizing actions, adding or removing semantic structure, challenging learned relationships, annotating chunks, changing viability ranges, and constraining learning.
### 38. Agent Interaction
Artificial agents MAY occupy the same general roles.
The following distinctions SHOULD remain explicit:
```text
type ≠ instance
node ≠ token
structure ≠ activation
observation ≠ evaluation
association ≠ prediction
prediction ≠ causation
prediction ≠ utility
utility ≠ authorization
goal ≠ viability constraint
action ≠ occurrence
confidence ≠ probability
learning ≠ execution
adaptation ≠ unrestricted autonomy
```
### 46. Initial Maturity Ladder
```text
AIPN-0 — Semantic Event Graph
AIPN-1 — Activation Network
AIPN-2 — Associative Learner
AIPN-3 — Predictive Learner
AIPN-4 — Outcome Learner
AIPN-5 — Regulatory Learner
AIPN-6 — Structural Learner
AIPN-7 — Concept Former
AIPN-8 — Reflective Network
```
### 47. Research Questions
Open questions include representation, activation, credit assignment, causality, structural learning, stability, goal formation, viability, hierarchy, authority, forgetting, and explanation.
### 48. Initial Implementation Hypothesis
A first experimental implementation SHOULD remain modest:
```text
one bounded domain
10–50 node types
hundreds to thousands of instances
a small number of explicit goals
several observers
fixed topology initially
learned association and prediction
```
### 49. Minimal Experimental Kernel
```text
Node
Token
Edge
Activation
Episode
Prediction
Observer
Signal
EligibilityTrace
```
### 50. Working Definition
> A persistent semantic network whose entities are activated by concrete information objects and environmental conditions; whose activation propagates through learned relationships to generate expectations and possible actions; whose consequences are evaluated by observers and regulatory goals; and whose accumulated experience modifies the network's future activation, prediction, utility estimates, and ultimately its semantic structure.
The immediate next useful artifact is probably `NetworkObjectModel.md`, translating the conceptual entities defined here into a precise executable object model while deliberately remaining independent of implementation language or storage technology.
