Planning engines

Except for Tamer and Pyperplan and anytime plan quality, you can configure all planning engines listed here for runtime, plan quality and anytime plan quality regarding your instance set.

LPG

Support Features:

  • classical and numeric state variables.

  • durative actions.

  • Supports the Oneshot Planner in both quality and runtime modes.

Fast-Downward

Support Features:

  • Classical planning support with full information, non-numeric, deterministic instantaneous actions.

  • Supports Oneshot and Anytime Planner engine in both quality and runtime modes.

Advantages:

  • Integration within the unified_planning library facilitated by the AIPlan4EU project, making it accessible for broader use.

  • Recognized as one of the most successful systems for classical planning.

Disadvantages:

  • Specificity to classical planning scenarios may limit its applicability to other types of planning problems.

EnhSP

Support Features:

  • Boolean and numeric state variables, actions, processes, and events (PDDL+ language).

  • Supports Oneshot Planner in both quality and runtime modes.

Advantages:

  • Handles disjunctive preconditions and conditional effects without expensive compilations.

Disadvantages:

  • Optimality only assured for specific PDDL+ fragments (simple numeric planning problems)​

SymK

Support Features:

  • State-of-the-art classical optimal and top-k planner based on symbolic search extending Fast Downward. It can find a single optimal plan or a set of k different best plans with the lowest cost for a given planning task.

  • Supported in the Oneshot and Anytime Planners in both quality and runtime modes.

Tamer

Tamer is a temporal planner that supports temporal action-based problems.

Support Features:

  • Supported in the Oneshot Planner in the runtime mode.

Pyperplan

Support Features:

  • Classical planning based on different search heuristics.

  • Action-based problems with hierarchical typing.

  • Supports the STRIPS PDDL fragment without action costs.

  • Supported in the Oneshot Planner in the runtime mode.

Advantages:

  • Lightweight and written in Python.

Disadvantages:

  • Does not support action costs in the STRIPS PDDL fragment, which could limit its usefulness in certain planning scenarios.

  • The default planning algorithm is a blind breadth-first search, which does not scale well, although other heuristic search algorithms are available.

  • Limited to specific PDDL fragments, which may not cater to more complex or varied planning needs.