Distributed Cognitive Network
Volume V
DCN Runtime Specification 0.1
Part 2
Cognitive Scheduler, System 1 / System 2 Dispatcher, Novelty Detection, Surprise Estimation, and Capability Resolution
29. Purpose
The Cognitive Kernel must continually decide:
- what deserves attention;
- what can be handled cheaply;
- what requires deliberate reasoning;
- what should wait;
- what should be forgotten;
- what should become future cognition.
This document defines those scheduling mechanisms.
30. Scheduling Philosophy
Traditional operating systems schedule:
- CPU time
- memory
- I/O
- interrupts
DCN Runtime schedules:
attention
reasoning
experts
artifacts
learning
network cognition
human interruption
Attention is therefore the primary scheduling primitive.
31. The Cognitive Scheduler
The scheduler continuously chooses:
Which cognitive process should receive the next unit of attention?
Candidate processes include:
System 1 execution
System 2 deliberation
Background learning
Compilation
Evaluation
Remote expert call
Memory indexing
Human interaction
Observation processing
The scheduler is always active.
32. Scheduling Inputs
Scheduling decisions depend on:
goal priority
urgency
novelty
confidence
attention budget
battery
latency
cost
network
authority
expected value
user interruption cost
These inputs continuously evolve.
33. Scheduling Outputs
The scheduler produces:
activate
defer
pause
resume
cancel
escalate
parallelize
serialize
No reasoning occurs before scheduling.
34. Cognitive Priority
Every runnable cognitive process possesses a dynamic priority.
Conceptually:
[ Priority = f( Goal, Urgency, Novelty, ExpectedValue, Risk, Deadline, AttentionCost ) ]
Priority is local.
There is no universal ordering.
35. Attention Budget
Attention is explicitly budgeted.
Example:
attention_budget
=
250 units
Each process consumes estimated attention.
Attention is replenished over time.
High-attention activities SHOULD justify themselves through expected value.
36. Cognitive Cost
Every candidate execution estimates:
money
latency
battery
network
memory
human interruption
future learning value
The scheduler reasons over all of them.
37. System 1 Dispatcher
The System 1 dispatcher attempts:
reuse before discovery.
Its purpose is to maximize compiled cognition.
38. System 1 Preconditions
System 1 execution requires:
matching trigger
trusted K-line
valid artifact
authority available
acceptable confidence
acceptable novelty
Failure of any condition escalates.
39. System 1 Pipeline
Observe
↓
Recognize
↓
Retrieve K-line
↓
Resolve capabilities
↓
Execute
↓
Validate
No exploration occurs.
40. System 2 Dispatcher
System 2 activates when:
novel
ambiguous
insufficient confidence
unexpected failure
conflicting experts
new goals
replanning
explicit user request
System 2 performs search.
41. System 2 Pipeline
Problem
↓
Goal analysis
↓
Expert discovery
↓
Capability planning
↓
Alternative generation
↓
Evaluation
↓
Selection
↓
Execution
↓
Learning
System 2 is expected to generate future System 1 cognition.
42. Dispatcher State Machine
Observe
↓
Recognition
↓
Known?
/ \
Yes No
| |
S1 S2
| |
Success? Success?
| |
Learn Learn
| |
Done Candidate K-line
Every successful execution contributes learning.
43. Recognition Engine
Recognition answers:
Have I solved this before?
Inputs:
semantic fingerprint
goal
context
entities
constraints
world state
Outputs:
candidate K-lines
confidence
distance
coverage
44. Candidate Ranking
Candidate K-lines are ranked using:
semantic similarity
historical success
evaluation quality
environment
trust
cost
latency
energy
The highest similarity does not automatically win.
45. Novelty Detector
Novelty estimates:
How unfamiliar is this problem?
Novelty is distinct from difficulty.
A difficult problem may have low novelty.
A trivial problem may be novel.
46. Novelty Inputs
Suggested inputs:
semantic distance
graph distance
missing capabilities
new entities
unexpected state
new goals
environment drift
Novelty is continuous.
47. Novelty Output
Conceptually:
0
known
↓
1
completely unfamiliar
Thresholds remain local policy.
48. Surprise Estimator
Surprise differs from novelty.
Novelty:
before execution.
Surprise:
after execution.
49. Surprise Examples
Expected:
confidence
↓
high
↓
success
Surprise:
low.
Unexpected:
confidence
↓
99%
↓
failure
Surprise:
very high.
50. Surprise Uses
Surprise influences:
revalidation
learning
attention
promotion
trust
artifact decay
Large surprise should never be ignored.
51. Escalation
Escalation occurs when:
novelty
or
surprise
or
authority
or
validation
requires deeper reasoning.
Escalation is explicit.
52. Capability Resolver
Capability resolution occurs after planning.
The resolver asks:
How should this capability be satisfied?
53. Resolution Order
Recommended order:
local artifact
↓
local expert
↓
organization
↓
Actum Compute
↓
human
This minimizes unnecessary remote execution.
54. Locality Preference
Everything else equal:
local
>
private organization
>
trusted remote
>
public market
Local execution reduces:
- latency;
- cost;
- privacy exposure.
55. Capability Cache
Resolved capabilities MAY be cached.
Cache entries SHOULD contain:
capability
binding
quality
expiry
environment
cost
Cache invalidation follows K-line decay.
56. Parallel Cognition
Independent capabilities MAY execute concurrently.
Example:
Reasoning
Evidence retrieval
Planning
can execute together.
Dependencies remain explicit.
57. Serial Cognition
Dependent reasoning remains ordered.
Example:
Diagnosis
↓
Treatment planning
↓
Communication
Serialization preserves causality.
58. Background Scheduling
Low-priority cognition includes:
evaluation
compilation
indexing
embedding
sync
artifact download
benchmarking
These SHOULD not interrupt foreground reasoning unnecessarily.
59. Goal Scheduler
Goals possess:
priority
deadline
dependencies
value
attention budget
The scheduler allocates cognition to goals, not merely requests.
60. Deadline Awareness
Deadlines increase urgency.
They do not automatically override:
authority,
privacy,
or
trust constraints.
61. Human Interruption Cost
The runtime estimates:
Should I interrupt the user?
Human attention is treated as an expensive resource.
62. Energy Awareness
Battery becomes a scheduling signal.
Example:
100%
↓
remote reasoning acceptable
15%
↓
prefer local artifacts
5%
↓
critical cognition only
This is essential for mobile Minds.
63. Money Awareness
Expensive experts require justification.
Repeated expensive execution SHOULD trigger compilation.
64. Learning Value
Every execution estimates:
How much future cognition might this create?
High learning value justifies greater System 2 investment.
65. Frontier Budget
A Mind SHOULD explicitly reserve resources for exploration.
Otherwise:
all attention becomes exploitation.
System 2 eventually disappears.
66. Exploration vs Exploitation
Scheduler balances:
known cognition
vs
new cognition
Both are required.
67. Opportunity Queue
Potential future work enters:
Opportunity Queue
Examples:
compile artifact
re-evaluate K-line
download artifact
benchmark expert
Executed when resources permit.
68. Cognitive Starvation
Long-running background work SHOULD eventually receive attention.
Fairness matters.
69. Scheduler Invariants
The scheduler SHALL preserve:
-
Locality before remote execution.
-
Reuse before rediscovery.
-
Proposal before mutation.
-
Validation before commitment.
-
Learning after execution.
-
Exploration remains possible.
-
Human interruption minimized.
-
Authority never bypassed.
70. Strategic Observation
Traditional schedulers maximize CPU utilization.
The DCN Runtime scheduler maximizes growth of reusable cognition.
The most valuable execution is not necessarily the fastest one.
It is often the execution that permanently expands the region of problems that System 1 can solve in the future.
That is the scheduler's deepest optimization objective.