RALSTP Consultant

RALSTP Consultant

Based on "Recursive Agents and Landmarks Strategic-Tactical Planning (RALSTP)" by Dorian Buksz, King's College London, 2024.

Core Concepts (from the thesis)

1. Agents Identification

Definition: Agents are objects with dynamic types that are active during goal state search.

How to identify:

Real PDDL Example (RTAM Domain):

(:types  
   ambulance police_car tow_truck fire_brigade - vehicle
   acc_victim vehicle car - subject
   ...
)

2. Passive Objects

Objects that are NOT agents — things being acted upon but don't act themselves.

3. Agent Dependencies

Definition: Relationships between agents based on what preconditions they satisfy for other agents.

Types:

4. Entanglement

Definition: When agents fight for shared resources (time, space, locations, etc.)

Measurement:

Real PDDL Example (RTAM - Road Traffic Accident):

(:durative-action confirm_accident
   :parameters (?V - police_car ?P - subject ?A - accident_location)
   :condition (and (at start (at ?V ?A)) (at start (at ?P ?A)) ...)
   :effect (and (at end (certified ?P)) ...)
)
(:durative-action untrap
   :parameters (?V - fire_brigade ?P - acc_victim ?A - accident_location)
   :condition (and (at start (certified ?P)) (at start (available ?V)) ...)
)

5. Landmarks

Definition: Facts that must be true in any valid plan (from goals back to initial state).

Types:

Real PDDL Example (RTAM - sequential dependencies):

Goal: (delivered victim1) ∧ (delivered car1)
Required sequence of fact landmarks:
1. (certified victim1)     ← police must confirm
2. (untrapped victim1)     ← fire must free them
3. (aided victim1)         ← ambulance must treat
4. (loaded victim1 ambulance) ← ambulance must load
5. (at victim1 hospital)   ← deliver to hospital
6. (delivered victim1)     ← FINAL
Action landmarks:
confirm_accident → untrap → first_aid → load_victim → unload_victim → deliver_victim

6. Strategic vs Tactical

7. Difficulty Metrics

From the thesis, difficulty increases with:

Buksz Complexity Score ≈ Agent Count × Entanglement Factor

Implementation Note (Natural Language vs PDDL)

This skill operates in two modes:

1. Conceptual Mode (Default): Uses the LLM to apply RALSTP methodology to natural language problems (e.g., "Plan a marketing launch"). No PDDL files are required. The agent identifies Agents/Landmarks conceptually. 2. Formal Mode (Optional): If you provide PDDL domain/problem files, the included scripts/analyze.py can be run to mathematically extract agents and landmarks.

*The instructions below apply to both modes, but "Real PDDL Examples" are provided for technical context.*

Usage

For any complex problem, just describe it and I'll apply RALSTP:

RALSTP analyze: I need to migrate 1000 VMs from datacentre A to B with minimal downtime

Output Format

## RALSTP Analysis
Agents Identified
[list agents and their types]
Passive Objects  
[list objects being acted upon]
Dependency Graph
[which agents depend on which]
Difficulty Assessment
Agent Count: X
Entanglement: Low/Medium/High
Estimated Complexity: [score]
Strategic Phase
[high-level plan ignoring details]
Tactical Phase
[detailed execution]
Decomposition Suggestion
Split by: [agent type / landmark / location]
Parallelize: [what can run concurrently]
Risks: [potential conflicts/entanglements]

When to Use

USE for:

SKIP for:

Reference

PhD Thesis: "Recursive Agents and Landmarks Strategic-Tactical Planning (RALSTP)" — Dorian Buksz, King's College London, 2024.

Example: RTAM Domain (IPC-2014)

Domain: Road Traffic Accident Management

Source: https://github.com/potassco/pddl-instances/tree/master/ipc-2014/domains/road-traffic-accident-management-temporal-satisficing

Full Analysis

Agents (4):

Passive Objects:

Dependencies (Critical Path):

police_car → fire_brigade → ambulance → hospital
     ↓            ↓           ↓
  certify      untrap       deliver

Landmarks Chain (must execute in order): 1. confirm_accident (police at scene) 2. untrap (fire frees victim) 3. first_aid (ambulance treats) 4. load_victim → unload_victim → deliver_victim 5. load_car → unload_car → deliver_vehicle

Entanglement:

Difficulty: High — 4 agents, tight dependencies, shared locations

For any complex problem, just describe it and I'll apply RALSTP:

RALSTP analyze: I need to migrate 1000 VMs from datacentre A to B with minimal downtime