feat(scripts): add GOFAI temporal reasoning engine
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scripts/temporal_reasoner.py
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307
scripts/temporal_reasoner.py
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#!/usr/bin/env python3
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"""temporal_reasoner.py - GOFAI temporal reasoning engine for the Timmy Foundation fleet.
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A symbolic temporal constraint network (TCN) for scheduling and ordering events.
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Models Allen's interval algebra relations (before, after, meets, overlaps, etc.)
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and propagates temporal constraints via path-consistency to detect conflicts.
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No ML, no embeddings - just constraint propagation over a temporal graph.
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Core concepts:
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TimePoint: A named instant on a symbolic timeline.
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Interval: A pair of time-points (start, end) with start < end.
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Constraint: A relation between two time-points or intervals
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(e.g. A.before(B), A.meets(B)).
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Usage (Python API):
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from temporal_reasoner import TemporalNetwork, Interval
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tn = TemporalNetwork()
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deploy = tn.add_interval('deploy', duration=(10, 30))
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test = tn.add_interval('test', duration=(5, 15))
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tn.add_constraint(deploy, 'before', test)
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consistent = tn.propagate()
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CLI:
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python temporal_reasoner.py --demo
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"""
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from __future__ import annotations
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import argparse
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import sys
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Dict, List, Optional, Set, Tuple
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INF = float('inf')
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# ---------------------------------------------------------------------------
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# Data model
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True)
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class TimePoint:
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"""A named instant on the timeline."""
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name: str
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id: int = field(default=0)
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def __str__(self) -> str:
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return self.name
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@dataclass
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class Interval:
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"""A named interval bounded by two time-points."""
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name: str
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start: int # index into the distance matrix
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end: int # index into the distance matrix
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def __str__(self) -> str:
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return self.name
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class Relation(Enum):
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"""Allen's interval algebra relations (simplified subset)."""
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BEFORE = 'before'
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AFTER = 'after'
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MEETS = 'meets'
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MET_BY = 'met_by'
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OVERLAPS = 'overlaps'
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DURING = 'during'
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EQUALS = 'equals'
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# ---------------------------------------------------------------------------
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# Simple Temporal Network (STN) via distance matrix
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# ---------------------------------------------------------------------------
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class TemporalNetwork:
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"""Simple Temporal Network with Floyd-Warshall propagation.
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Internally maintains a distance matrix D where D[i][j] is the
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maximum allowed distance from time-point i to time-point j.
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Negative cycles indicate inconsistency.
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"""
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def __init__(self) -> None:
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self._n = 0
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self._names: List[str] = []
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self._dist: List[List[float]] = []
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self._intervals: Dict[str, Interval] = {}
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self._origin_idx: int = -1
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self._add_point('origin')
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self._origin_idx = 0
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# ------------------------------------------------------------------
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# Point management
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# ------------------------------------------------------------------
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def _add_point(self, name: str) -> int:
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"""Add a time-point and return its index."""
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idx = self._n
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self._n += 1
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self._names.append(name)
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# Extend distance matrix
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for row in self._dist:
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row.append(INF)
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self._dist.append([INF] * self._n)
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self._dist[idx][idx] = 0.0
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return idx
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# ------------------------------------------------------------------
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# Interval management
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# ------------------------------------------------------------------
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def add_interval(
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self,
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name: str,
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duration: Optional[Tuple[float, float]] = None,
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) -> Interval:
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"""Add a named interval with optional duration bounds [lo, hi].
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Returns the Interval object with start/end indices.
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"""
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s = self._add_point(f"{name}.start")
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e = self._add_point(f"{name}.end")
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# start < end (at least 1 time unit)
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self._dist[s][e] = min(self._dist[s][e], duration[1] if duration else INF)
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self._dist[e][s] = min(self._dist[e][s], -(duration[0] if duration else 1))
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interval = Interval(name=name, start=s, end=e)
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self._intervals[name] = interval
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return interval
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# ------------------------------------------------------------------
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# Constraint management
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# ------------------------------------------------------------------
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def add_distance_constraint(
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self, i: int, j: int, lo: float, hi: float
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) -> None:
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"""Add constraint: lo <= t_j - t_i <= hi."""
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self._dist[i][j] = min(self._dist[i][j], hi)
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self._dist[j][i] = min(self._dist[j][i], -lo)
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def add_constraint(
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self, a: Interval, relation: str, b: Interval, gap: Tuple[float, float] = (0, INF)
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) -> None:
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"""Add an Allen-style relation between two intervals.
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Supported relations: before, after, meets, met_by, equals.
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"""
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rel = relation.lower()
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if rel == 'before':
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# a.end + gap <= b.start
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self.add_distance_constraint(a.end, b.start, gap[0], gap[1])
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elif rel == 'after':
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self.add_distance_constraint(b.end, a.start, gap[0], gap[1])
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elif rel == 'meets':
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# a.end == b.start
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self.add_distance_constraint(a.end, b.start, 0, 0)
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elif rel == 'met_by':
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self.add_distance_constraint(b.end, a.start, 0, 0)
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elif rel == 'equals':
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self.add_distance_constraint(a.start, b.start, 0, 0)
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self.add_distance_constraint(a.end, b.end, 0, 0)
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else:
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raise ValueError(f"Unsupported relation: {relation}")
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# ------------------------------------------------------------------
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# Propagation (Floyd-Warshall)
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# ------------------------------------------------------------------
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def propagate(self) -> bool:
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"""Run Floyd-Warshall to propagate all constraints.
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Returns True if the network is consistent (no negative cycles).
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"""
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n = self._n
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d = self._dist
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for k in range(n):
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for i in range(n):
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for j in range(n):
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if d[i][k] + d[k][j] < d[i][j]:
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d[i][j] = d[i][k] + d[k][j]
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# Check for negative cycles
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for i in range(n):
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if d[i][i] < 0:
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return False
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return True
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def is_consistent(self) -> bool:
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"""Check consistency without mutating (copies matrix first)."""
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import copy
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saved = copy.deepcopy(self._dist)
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result = self.propagate()
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self._dist = saved
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return result
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# ------------------------------------------------------------------
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# Query
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# ------------------------------------------------------------------
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def earliest(self, point_idx: int) -> float:
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"""Earliest possible time for a point (relative to origin)."""
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return -self._dist[point_idx][self._origin_idx]
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def latest(self, point_idx: int) -> float:
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"""Latest possible time for a point (relative to origin)."""
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return self._dist[self._origin_idx][point_idx]
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def interval_bounds(self, interval: Interval) -> Dict[str, Tuple[float, float]]:
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"""Return earliest/latest start and end for an interval."""
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return {
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'start': (self.earliest(interval.start), self.latest(interval.start)),
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'end': (self.earliest(interval.end), self.latest(interval.end)),
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}
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# ------------------------------------------------------------------
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# Display
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# ------------------------------------------------------------------
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def dump(self) -> None:
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"""Print the current distance matrix and interval bounds."""
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print(f"Temporal Network — {self._n} time-points, {len(self._intervals)} intervals")
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print()
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for name, interval in self._intervals.items():
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bounds = self.interval_bounds(interval)
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s_lo, s_hi = bounds['start']
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e_lo, e_hi = bounds['end']
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print(f" {name}:")
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print(f" start: [{s_lo:.1f}, {s_hi:.1f}]")
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print(f" end: [{e_lo:.1f}, {e_hi:.1f}]")
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# ---------------------------------------------------------------------------
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# Demo: Timmy fleet deployment pipeline
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# ---------------------------------------------------------------------------
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def run_demo() -> None:
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"""Run a demo temporal reasoning scenario for the Timmy fleet."""
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print("=" * 60)
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print("Temporal Reasoner Demo - Fleet Deployment Pipeline")
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print("=" * 60)
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print()
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tn = TemporalNetwork()
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# Define pipeline stages with duration bounds [min, max]
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build = tn.add_interval('build', duration=(5, 15))
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test = tn.add_interval('test', duration=(10, 30))
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review = tn.add_interval('review', duration=(2, 10))
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deploy = tn.add_interval('deploy', duration=(1, 5))
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monitor = tn.add_interval('monitor', duration=(20, 60))
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# Temporal constraints
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tn.add_constraint(build, 'meets', test) # test starts when build ends
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tn.add_constraint(test, 'before', review, gap=(0, 5)) # review within 5 of test
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tn.add_constraint(review, 'meets', deploy) # deploy immediately after review
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tn.add_constraint(deploy, 'before', monitor, gap=(0, 2)) # monitor within 2 of deploy
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# Global deadline: everything done within 120 time units
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tn.add_distance_constraint(tn._origin_idx, monitor.end, 0, 120)
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# Build must start within first 10 units
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tn.add_distance_constraint(tn._origin_idx, build.start, 0, 10)
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print("Constraints added. Propagating...")
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consistent = tn.propagate()
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print(f"Network consistent: {consistent}")
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print()
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if consistent:
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tn.dump()
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print()
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# Now add a conflicting constraint to show inconsistency detection
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print("--- Adding conflicting constraint: monitor.before(build) ---")
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tn.add_constraint(monitor, 'before', build)
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consistent2 = tn.propagate()
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print(f"Network consistent after conflict: {consistent2}")
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def main() -> None:
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parser = argparse.ArgumentParser(
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description="GOFAI temporal reasoning engine"
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)
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parser.add_argument(
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"--demo",
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action="store_true",
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help="Run the fleet deployment pipeline demo",
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)
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args = parser.parse_args()
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if args.demo or not any(vars(args).values()):
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run_demo()
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else:
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parser.print_help()
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if __name__ == "__main__":
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main()
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