curiositech/muscettola-nicola-1993-1

SKILL.md: HSTS — Integrated Planning & Scheduling

license: Apache-2.0

name: hsts-integrated-planning-scheduling
version: 1.0.0
description: >
  Wisdom from Muscettola's HSTS framework for building intelligent systems
  that unify planning (what to do) and scheduling (when/how to do it).
  Activates when designing constraint-based agents, solving resource allocation
  problems, building robust execution systems, or reasoning about commitment
  under uncertainty.
activation_triggers:
  - planning AND scheduling in same problem
  - resource contention / allocation conflicts
  - temporal reasoning / constraint networks
  - robust execution / plan flexibility
  - combinatorial search with bottlenecks
  - state variable modeling for complex domains
  - "brittle plan" / replanning overhead complaints
  - multi-subsystem coordination problems

---

When to Use This Skill

Load this skill when the problem involves any of these signatures:

| Situation | Signal Phrases | |-----------|----------------| | Designing an agent that must both decide what to do and when to do it | "planning and scheduling", "task allocation over time" | | Plans that break whenever reality deviates slightly | "brittle", "replanning constantly", "nominal plan fails" | | Search that explores too many options before finding feasibility | "combinatorial explosion", "too many combinations" | | Resource conflicts across concurrent activities | "contention", "bottleneck", "resource oversubscription" | | Complex domain with multiple interacting subsystems | "subsystem coordination", "state interdependencies" | | Need to balance flexibility at execution time vs. commitment at plan time | "flexible execution", "degrees of freedom", "commitment" | | Encoding expert domain knowledge as constraints | "domain rules", "compatibility", "causal knowledge" |

This skill is NOT primarily about: classical STRIPS planning, pure optimization (LP/ILP), real-time scheduling theory, or project management methodologies — though it illuminates all of them.

---

Core Mental Models

1. The Planning/Scheduling Unification

The separation of "what to do" (planning) from "when and with what" (scheduling) is an artifact of representation, not a property of the world. When you model a domain as state variables evolving over continuous time, both decisions happen simultaneously and inform each other. A causal justification for a goal is a temporal constraint. A resource allocation is a state transition.

Practical implication: If your architecture has a planner feeding into a scheduler, ask whether the separation is creating coordination debt. The seam may be where bugs live.

2. Schedules as Behavioral Envelopes

The goal of scheduling is not to find the optimal sequence — it is to find the most permissive constraint network consistent with all goals. A good schedule is a set of legal behaviors. The executor chooses a path within that set at runtime, absorbing disturbances without replanning.

Practical implication: Preserve temporal flexibility as long as possible. Don't bind a time when an ordering relation suffices. Don't fix a resource assignment when a constraint set suffices.

3. Commitment Level as a First-Class Decision

At every problem-solving step, you choose how much to commit. The spectrum runs from:

• Value commitment: bind variable to exact value (most constraining)
• Constraint posting: add ordering or precedence relation (medium)
• Argument left unbound: defer choice entirely (least constraining)

Lower commitment during search produces better solutions with less computation. Commit only when forced or when the information gain justifies it.

Practical implication: Design your search to ask "what is the minimum commitment needed to make progress?" before making any decision.

4. Bottleneck-Centered Probabilistic Focus

Before resolving any disjunction in a constraint network, run stochastic simulation over the current partial solution to estimate where conflicts are most likely to occur. Focus search effort on the resource-time pair with the highest estimated contention.

Practical implication: Measure before committing. The hardest constraint to satisfy should be addressed first — and "hardest" is an empirical estimate, not an assumption.

5. Modularity and Additive Scalability

Complex domains decompose into modules (subsystems, state variables). When heuristics respect module boundaries and model local interactions explicitly, computational cost scales as sum of parts, not exponential product. This is architecture-dependent — the framework must make module structure and interaction topology visible.

Practical implication: Define module boundaries before building heuristics. Interactions that cross boundaries are the scalability risk; make them explicit and budget for them.

---

Decision Frameworks

Framework A: "Should I separate planning and scheduling?"

IF the domain has resources with time-varying availability
AND causal goals interact with resource constraints
  → DO NOT separate planning and scheduling
  → Model as unified state variable system
  → LOAD: references/planning-scheduling-unification.md

IF the problem is purely causal (no resource contention)
  → Classical planning may suffice; no unification needed

IF the problem is purely temporal (fixed task structure)
  → Classical scheduling may suffice; no unification needed

Framework B: "How much should I commit right now?"

IF a decision is reversible and more information is coming
  → Post constraint, do not bind value
  → Preserve flexibility for executor

IF a decision resolves a high-contention bottleneck
  → Commit now to unblock downstream decisions
  → Use probabilistic estimates to confirm it IS the bottleneck first

IF you are tempted to fix exact times early in search
  → STOP — ask if an ordering relation achieves the same progress
  → LOAD: references/schedules-as-behavioral-envelopes.md

Framework C: "Where should search focus next?"

IF constraint network is partially resolved with active disjunctions
  → Run stochastic simulation (sample without resolving disjunctions)
  → Estimate conflict probability per resource-time pair
  → Focus next decision on highest-contention resource

IF search is thrashing (many backtracks, no progress)
  → Likely attacking non-bottleneck variables first
  → Re-estimate contention distribution
  → LOAD: references/bottleneck-centered-probabilistic-search.md

IF domain has strong ordering among constraints
  → Address most constrained first (fail-first principle)

Framework D: "How do I model this domain?"

IF domain has objects with internal state that changes over time
  → Model each object as a state variable with legal transition graph

IF domain has shared resources (consumable or reusable)
  → Model resource levels as state variables
  → LOAD: references/resource-modeling-spectrum.md

IF domain knowledge exists as expert rules ("X and Y can't overlap")
  → Encode as compatibility constraints, not as search pruning hacks
  → LOAD: references/compatibility-constraints-as-causal-knowledge.md

IF problem is too large to solve flat
  → Identify natural abstraction hierarchy
  → Solve at coarse level first, refine within committed structure
  → LOAD: references/hierarchical-abstraction-in-problem-solving.md

Framework E: "Why is my constraint system failing?"

IF solutions exist but search can't find them
  → Check: are you over-committing early? (commitment level issue)
  → Check: are you ordering search on wrong variables? (bottleneck issue)

IF constraint propagation is slow
  → Check: are constraints encoded at right abstraction level?
  → Check: are module boundaries respected?

IF system produces plans that fail at execution
  → Check: is schedule a nominal trajectory instead of behavioral envelope?
  → LOAD: references/failure-modes-in-constraint-based-systems.md

---

Reference Documents

| File | Load When You Need To... | |------|--------------------------| | references/planning-scheduling-unification.md | Understand WHY the planning/scheduling boundary is artificial; design unified architectures; explain the HSTS core thesis | | references/schedules-as-behavioral-envelopes.md | Design flexible execution systems; argue against nominal trajectory planning; understand temporal flexibility preservation | | references/state-variable-modeling-for-agents.md | Model a complex domain from scratch; decompose problem into state variables; understand legal transition graphs | | references/bottleneck-centered-probabilistic-search.md | Focus search effort; implement Conflict Partition Scheduling; use stochastic simulation to guide decisions | | references/compatibility-constraints-as-causal-knowledge.md | Encode expert domain knowledge; understand how constraints carry causal semantics; distinguish hard vs. soft constraints | | references/hierarchical-abstraction-in-problem-solving.md | Manage problem complexity through staged commitment; design coarse-to-fine solving strategies | | references/failure-modes-in-constraint-based-systems.md | Diagnose why a planning/scheduling system is failing; understand classical failure patterns HSTS was designed to overcome | | references/resource-modeling-spectrum.md | Choose the right resource representation (binary, numeric, state-based); model consumable vs. reusable resources | | references/token-networks-as-executable-knowledge-representation.md | Understand HSTS's core data structure; implement partial commitment as first-class architecture; understand token semantics |

Loading heuristic: Start with planning-scheduling-unification.md for conceptual grounding. Load state-variable-modeling-for-agents.md when touching domain design. Load bottleneck-centered-probabilistic-search.md when touching search strategy. Load failure-modes-in-constraint-based-systems.md when debugging.

---

Anti-Patterns

These are the mistakes HSTS was explicitly designed to prevent:

❌ The Nominal Trajectory Trap

Building a scheduler that outputs a single specific sequence of actions with exact times. Any runtime deviation requires full replanning. Fix: target behavioral envelopes; output constraint networks, not schedules.

❌ Premature Value Commitment

Binding variables to specific values (exact start times, specific resource assignments) before constraints force it. This artificially shrinks the solution space. Fix: post the weakest constraint that makes progress.

❌ Planning-Then-Scheduling Pipeline

Handing a complete symbolic plan to a scheduler as a fixed input. Resource constraints discovered by the scheduler cannot flow back to revise causal decisions. Fix: unify representations so resource and causal reasoning interleave.

❌ Flat Search Without Bottleneck Analysis

Treating all disjunctions as equally important and resolving them in arbitrary order. This causes thrashing on easy variables while hard constraints fester. Fix: estimate contention distribution before committing; address bottlenecks first.

❌ Monolithic Domain Models

Encoding an entire complex domain as one undifferentiated constraint satisfaction problem. Interactions are invisible; heuristics can't be modular; scaling is exponential. Fix: decompose into state variable modules; make interaction topology explicit.

❌ Hardcoded Pruning as Domain Knowledge

Encoding expert knowledge as ad hoc search pruning rules rather than as formal compatibility constraints. Knowledge becomes invisible, unauditable, and non-reusable. Fix: compatibility constraints are first-class objects with causal semantics.

❌ Treating Feasibility and Optimality as the Same Problem

Spending search effort optimizing solutions before confirming that the constraint network is satisfiable. Fix: first find the most permissive feasible envelope; optimize within it.

---

Shibboleths

How to tell if someone has genuinely internalized HSTS versus skimmed a summary:

They get it if they say...

• "A schedule is a set of behaviors, not a sequence of actions" — and mean it architecturally
• "We should post the ordering constraint, not bind the time" — naturally, during design discussion
• "Where is the bottleneck? Have we measured, or are we guessing?" — before starting search
• "The planning/scheduling separation is a representation choice, not a domain property"
• "Flexibility at execution time is a design goal, not an afterthought"
• "Module interactions are where the complexity lives — we need to make them explicit"

They're faking it if they say...

• "We'll plan first, then schedule" — without questioning whether the boundary is costing them
• "We need the optimal schedule" — without asking whether a feasible flexible envelope would be better
• "We'll fix the time early and adjust later if needed" — treating replanning as free
• "The domain is too complex to model formally" — without trying state variable decomposition
• "We pruned those branches because of domain knowledge" — but can't state the knowledge as a constraint
• "HSTS is about spacecraft scheduling" — reducing a general framework to its demonstration domain

The deepest shibboleth: Ask them to explain the difference between a nominal plan and a behavioral envelope and watch whether they give an architectural answer (the envelope is a constraint network; any path through it is legal execution) or a vague one ("the envelope has some slack in it"). The architectural answer shows they've internalized the representation-level insight, not just the intuition.

---

Last updated: based on Muscettola, N. "HSTS: Integrating Planning and Scheduling" (1994)