Andy Dixon
9eb873cbe6
Phase 9. Goals form a mind-scoped tree with kind, priority, progress and blockers; their deterministic relevance to a candidate feeds the goal_relevance attention feature and a goal monitor raises stalled goals and observations that touch one. Drives are a closed set of eight scalars with operator-configured ranges and personality-derived baselines; there is no self-preservation, replication or acquisition drive and no way to add one at runtime. Affect is ten scalars moved by an explicit rule table over events and decaying towards baseline; both modulate attention weights each cycle and the effective weights are recorded with every cycle and shown beside the configured ones. Predictions carry a window and machine-checkable criteria (event terms occurring or staying absent, a belief reaching a confidence, or operator judgement). A resolver job judges them deterministically, keeps an append-only outcome history for calibration (accuracy, Brier score, confidence buckets, overconfidence flag), emits prediction_error events scaled by how confident the miss was, and feeds the outcome back into the linked belief as evidence. Causal hypotheses derive predictions automatically. Curiosity turns misses, contradictions and unexplained observations into questions that compete for attention and become investigation goals when admitted. Brain gains after-cycle hooks and goal/motivation state. API, UI (goals, prediction dashboard, cognitive state panel, effective weights) and CLI (goal, prediction, state). Demo scenario 4 verified end to end.
105 lines
3.4 KiB
Go
105 lines
3.4 KiB
Go
// Package cognition holds the built-in cognitive processes. Each is a small,
|
|
// mostly deterministic Process; LLM-assisted ones arrive with the provider
|
|
// layer and always degrade to deterministic behaviour when no model is
|
|
// available (spec §46, §62).
|
|
package cognition
|
|
|
|
import (
|
|
"context"
|
|
"encoding/json"
|
|
"strings"
|
|
|
|
"github.com/andydixon/noema/internal/attention"
|
|
"github.com/andydixon/noema/internal/brain"
|
|
"github.com/andydixon/noema/internal/goals"
|
|
"github.com/andydixon/noema/internal/workspace"
|
|
"github.com/andydixon/noema/pkg/events"
|
|
"github.com/andydixon/noema/pkg/ids"
|
|
)
|
|
|
|
// Attend turns each new external event into an "attend" candidate whose
|
|
// features are computed from the event itself and the current context.
|
|
type Attend struct{}
|
|
|
|
// Name implements brain.Process.
|
|
func (Attend) Name() string { return "attend" }
|
|
|
|
// urgent words raise urgency deterministically; a model may refine this later
|
|
// but the floor is always computed here.
|
|
var urgentWords = map[string]float64{"error": 0.7, "errors": 0.7, "fail": 0.8, "failed": 0.8, "failure": 0.8, "down": 0.8, "outage": 0.9, "alert": 0.8, "alarm": 0.8,
|
|
"critical": 0.9, "urgent": 0.9, "immediately": 0.8, "crash": 0.8, "crashed": 0.8, "timeout": 0.6, "latency": 0.5, "spike": 0.6, "smart": 0.4, "corrupt": 0.8, "lost": 0.6}
|
|
|
|
// Observe implements brain.Process.
|
|
func (Attend) Observe(_ context.Context, s *brain.State) ([]brain.Candidate, error) {
|
|
ctxTokens := s.Workspace.Tokens()
|
|
var out []brain.Candidate
|
|
for _, ev := range s.NewEvents {
|
|
if strings.HasPrefix(ev.Source, "brain") {
|
|
continue
|
|
}
|
|
switch ev.Type {
|
|
case events.Observation, events.Perception, events.OperatorAction, events.ToolExecuted, events.ToolFailed, events.PredictionError, events.ContradictionDetected:
|
|
default:
|
|
continue
|
|
}
|
|
text := ev.Summary
|
|
var trust events.Trust
|
|
if ev.Type == events.Observation {
|
|
var p events.ObservationPayload
|
|
if json.Unmarshal(ev.Payload, &p) == nil {
|
|
if p.Text != "" {
|
|
text = p.Text
|
|
}
|
|
trust = p.Trust
|
|
}
|
|
}
|
|
toks := workspace.Tokenise(text)
|
|
f := attention.Features{Salience: ev.Salience, Confidence: ev.Confidence, Consequence: 0.4}
|
|
f.Urgency = urgency(toks)
|
|
f.ContextRelevance = workspace.Overlap(toks, ctxTokens)
|
|
f.GoalRelevance, _ = goals.Relevance(text, s.Goals)
|
|
f.Novelty = 1 - f.ContextRelevance*0.7 // things we are already holding are less novel
|
|
switch trust {
|
|
case events.TrustOperator:
|
|
f.OperatorPriority = 1
|
|
case events.TrustUser:
|
|
f.OperatorPriority = 0.7
|
|
default:
|
|
f.OperatorPriority = 0.3
|
|
}
|
|
if ev.Type == events.OperatorAction {
|
|
f.OperatorPriority, f.Salience = 0.9, max(ev.Salience, 0.5)
|
|
}
|
|
if ev.Type == events.ContradictionDetected {
|
|
f.Contradiction = 1
|
|
}
|
|
if ev.Type == events.PredictionError {
|
|
f.Consequence = 0.8
|
|
}
|
|
out = append(out, brain.Candidate{Kind: "attend", Subject: string(ev.ID), Summary: ev.Summary,
|
|
Content: map[string]any{"source_event": string(ev.ID), "event_type": ev.Type, "text": truncate(text, 2000), "trust": trust},
|
|
Features: f, Confidence: ev.Confidence, SourceEvents: []ids.ID{ev.ID}, Evidence: []events.EvidenceRef{{Kind: "event", ID: string(ev.ID)}}})
|
|
}
|
|
return out, nil
|
|
}
|
|
|
|
func urgency(tokens []string) float64 {
|
|
var u float64
|
|
for _, t := range tokens {
|
|
if v, ok := urgentWords[t]; ok && v > u {
|
|
u = v
|
|
}
|
|
}
|
|
if u == 0 {
|
|
return 0.2
|
|
}
|
|
return u
|
|
}
|
|
|
|
func truncate(s string, n int) string {
|
|
if len(s) > n {
|
|
return s[:n]
|
|
}
|
|
return s
|
|
}
|