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.
126 lines
4.2 KiB
Go
126 lines
4.2 KiB
Go
package cognition
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import (
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"context"
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"encoding/json"
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"strings"
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"github.com/andydixon/noema/internal/attention"
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"github.com/andydixon/noema/internal/beliefs"
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"github.com/andydixon/noema/internal/brain"
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"github.com/andydixon/noema/internal/memory"
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"github.com/andydixon/noema/pkg/events"
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"github.com/andydixon/noema/pkg/ids"
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)
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// Curiosity proposes questions about unexplained observations, prediction
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// errors and contradictions. Its output is questions and investigation goals
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// only; it never grants or requests permission to act (spec §12).
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type Curiosity struct{}
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// Name implements brain.Process.
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func (Curiosity) Name() string { return "curiosity" }
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// Observe implements brain.Process.
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func (Curiosity) Observe(ctx context.Context, s *brain.State) ([]brain.Candidate, error) {
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drive := 0.5
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for _, d := range s.Motiv.Drives {
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if d.Name == "curiosity" {
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drive = d.Value
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}
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}
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var out []brain.Candidate
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for _, ev := range s.NewEvents {
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if strings.HasPrefix(ev.Source, "brain") && ev.Type != events.PredictionError && ev.Type != events.ContradictionDetected {
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continue
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}
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switch ev.Type {
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case events.PredictionError:
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stmt, _ := payloadField(ev, "statement")
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q := "Why was my prediction wrong: " + truncate(stmt, 120) + "?"
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out = append(out, curiosityCandidate(ev, q, "prediction:"+ev.Metadata["prediction_id"].(string), 0.8, drive))
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case events.ContradictionDetected:
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q := "Which is right: " + truncate(ev.Summary, 140) + "?"
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out = append(out, curiosityCandidate(ev, q, "contradiction:"+fmtID(ev.Metadata["belief_id"])+"+"+fmtID(ev.Metadata["other_id"]), 0.7, drive))
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case events.Observation:
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if ev.Metadata["conversation_id"] != nil || ev.Salience < 0.5 {
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continue
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}
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text := ev.Summary
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var p events.ObservationPayload
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if json.Unmarshal(ev.Payload, &p) == nil && p.Text != "" {
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text = p.Text
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}
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if urgency(tokenise(text)) < 0.5 {
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continue
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}
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// Unexplained: no belief and no memory speaks to it.
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explained := false
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if s.Services.Beliefs != nil {
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if bs, _ := s.Services.Beliefs.Search(ctx, s.Mind.ID, text, 3); hasLive(bs) {
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explained = true
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}
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}
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if !explained && s.Services.Memory != nil {
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if hits, _ := s.Services.Memory.Retrieve(ctx, s.Mind.ID, memory.Query{Text: text, Limit: 3, Now: s.Now}); len(hits) > 0 && hits[0].Score >= 0.6 && hits[0].Memory.OriginEvent != ev.ID {
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explained = true
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}
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}
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if explained {
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continue
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}
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q := "Why did this happen: " + truncate(text, 120) + "?"
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out = append(out, curiosityCandidate(ev, q, "unexplained:"+subjectKey(text), 0.5, drive))
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}
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}
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return out, nil
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}
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func curiosityCandidate(ev events.Event, question, subject string, weight, drive float64) brain.Candidate {
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return brain.Candidate{Kind: "question", Subject: subject, Summary: "question: " + question,
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Content: map[string]any{"question": question, "subject": subject, "source_event": string(ev.ID)},
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// A failed prediction or a contradiction is an unresolved conflict between what
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// was expected and what was seen, so the contradiction feature is set for both.
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Features: attention.Features{Salience: 0.4 + 0.45*weight, Novelty: 0.6 + 0.3*drive, Consequence: 0.5 + 0.3*weight, Urgency: 0.4 * weight, Confidence: 0.5,
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Contradiction: boolf(ev.Type == events.ContradictionDetected || ev.Type == events.PredictionError)},
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Confidence: 0.5, SourceEvents: []ids.ID{ev.ID}, Evidence: []events.EvidenceRef{{Kind: "event", ID: string(ev.ID), Note: "prompted the question"}}}
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}
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func hasLive(bs []beliefs.Belief) bool {
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for _, b := range bs {
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if b.Status != beliefs.StatusRejected && b.Status != beliefs.StatusSuperseded {
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return true
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}
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}
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return false
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}
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func boolf(b bool) float64 {
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if b {
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return 1
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}
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return 0
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}
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func fmtID(v any) string {
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if s, ok := v.(string); ok {
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return s
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}
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return ""
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}
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// subjectKey reduces text to its three most distinctive tokens so the same
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// unexplained situation yields the same question subject.
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func subjectKey(text string) string {
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toks := tokenise(text)
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var keep []string
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for _, t := range toks {
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if len(t) >= 4 && len(keep) < 3 {
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keep = append(keep, t)
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}
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}
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return strings.Join(keep, "+")
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}
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func tokenise(text string) []string { return tokeniseWS(text) }
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