PlannerArena: AI Planning Evaluation Suite

• PlannerArena is a suite of benchmarking platforms that offers standardized evaluation and visualization tools for automated planning systems across various AI domains.
• It integrates diverse planning paradigms—from motion planning to LLM-based agents—using rigorous metrics, statistical analyses, and interactive performance plots.
• The platform supports reproducible experimentation with modular design, extensibility features, and dynamic, user-friendly visualization tools.

PlannerArena refers to a suite of benchmarking platforms, evaluation environments, and technical frameworks used for empirical research, development, and comparison of automated planners and planning systems in artificial intelligence. These systems target a range of planning paradigms: from classical plan graph–based AI planners and motion planning benchmarks in robotics, to extensible reinforcement learning environments, multi-modal trip planners, as well as modern LLM-based agent planning and urban systems simulation. PlannerArena platforms support robust performance evaluation, methodological innovation, and reproducible experimentation.

1. Benchmarking Environments and Platforms

PlannerArena, as highlighted in the motion planning domain (Moll et al., 2014), is a central repository and interactive web-based tool that enables researchers to benchmark, compare, and visualize the performance of a broad range of planning algorithms. Integrated with the Open Motion Planning Library (OMPL), it provides:

• Definition and storage of high-level planning problems (state spaces, collision functions, start/goal conditions, and optimization objectives).
• Support for over 29 planning algorithms, both geometric and kinodynamic, encompassing first-solution and optimizing variants.
• Extensible log file and database formats: benchmark outputs are easily parsed, imported into SQL databases, and kept compatible across different planning libraries.
• Dynamic, interactive visualization: tools such as box plots, CDFs, progress plots, and regression plots facilitate nuanced performance analysis and longitudinal studies.
• Downloadable, “camera-ready” figure export for academic dissemination.

These features transform PlannerArena from a static benchmark to a living, evolving laboratory for planning research.

2. Plan Graph–Based Planners and Unified Evaluation Frameworks

An early foundational substrate of PlannerArena is the rigorous empirical comparison of plan graph–based planners, as implemented in the Ipê Planning Environment (IPE) (Marynowski, 2012). Plan graph–based planners represent the state–action space using layered graphs composed of: propositions per time step, applicable actions, and mutex (mutual exclusion) sets denoting pairs of conflicting actions or facts.

Major characteristics include:

• Efficient compact representation of parallelizable actions and exclusion relationships.
• Plan graph layering until the goal layer is mutex-free, enabling immediate detection of unsolvable goals and supporting heuristic extraction (e.g., layer-based relaxed plan heuristics as in FF).
• Modular platform architectures (e.g., IPE) that enforce component separation (PDDL parsing, problem instantiation, representation building, and search modules), supporting apples-to-apples algorithm comparisons.
• Integration of alternative representations: translation from plan graphs to SAT instances (BLACKBOX), Petri nets (PETRIPLAN), enabling cross-paradigm empirical studies.

Empirical results underscore that, when factors such as parser and representation overheads are controlled, differences in planner performance are primarily due to algorithmic advances rather than extraneous implementation details.

3. Performance Metrics, Statistical Analysis, and Visualization

Evaluation platforms in the PlannerArena family employ multidimensional analysis to judge planner efficacy:

• Solution Time: Recorded as both total and per-phase (e.g., parsing, representation, planning/search).
• Plan Quality: Metrics such as action count, step count, or path length are used to indicate optimality or suboptimality.
• Resource Usage: Memory footprint, representation size (number of graph nodes/arcs, mutex counts), and computational breakdowns are documented.
• Solution Distributions: Because many planners (especially those using sampling) are stochastic, distributions of outcomes (not just means) are reported using box plots, CDFs, and confidence intervals. For example, confidence intervals are calculated as

$$\text{CI} = \text{MeanEstimate}(t) \pm 1.96 \cdot \frac{\sigma(t)}{\sqrt{N}}$$

where $\sigma(t)$ is standard deviation and $N$ is number of runs at time $t$ (Moll et al., 2014).

• Progress/Convergence Plots: For anytime or asymptotically optimal planners, time-series data of best-so-far solution cost are visualized, including smoothed means and $95\%$ confidence intervals.

This comprehensive statistical treatment ensures robust, reproducible, and interpretable experimental results.

4. Applications Across Planning Domains

PlannerArena’s methodologies have catalyzed cross-cutting impact in diverse planning domains:

• Motion Planning (Robotics): Integrated with OMPL and MoveIt!, PlannerArena benchmarking supports rigorous evaluation and tuning of sampling-based planners for both simulated and physical robots, bridging the gap between theoretical performance and real-world operation (Moll et al., 2014).
• AI Task Planning: Plan graph, Petri net, and SAT-based planner evaluation frameworks (e.g., IPE) (Marynowski, 2012) clarify algorithmic trade-offs, bottlenecks, and representational effects, contributing to advances in classical planning, temporal planning, and representation engineering.
• Parameter Tuning and Regression Analysis: Parameter sweep support, automated regression benchmarking, and longitudinal tracking enable continuous integration workflows and performance regression studies for planner libraries.
• Education and Research Training: Interactive web-based tools and modular environments accelerate the onboarding of new researchers, standardize empirical methodology, and support didactic exploration of algorithmic variants.

5. Unification, Platform Design, and Future Directions

Unified, modular design is a distinguishing feature of PlannerArena environments:

• Object-Oriented Architectures and API Standardization: Platforms like IPE provide C++ classes for PDDL parsing, instantiation, representation, and search, ensuring that new planners remain compatible and comparable (Marynowski, 2012).
• Extensibility: Log/database schemas add new attributes automatically, and others (e.g., MoveIt!) may produce logs compatible with OMPL/PlannerArena infrastructure (Moll et al., 2014).
• Ease of Experimentation: Sample codebases (e.g., GRAPHPLAN‐1, PETRIPLAN variants) promote rapid prototyping and head-to-head experimentation.
• Educational Utility and Collaborative Development: These properties foster an ecosystem where multiple researchers contribute, benchmark, and compare planning algorithms within the same technical framework.

Future directions include improving underlying solvers (e.g., for Petri net–based planners), supporting non-classical domains (e.g., temporal or resource-constrained planning), composing richer constraint models (e.g., resource/time integration within plan graphs or using constraint programming), and further combining heuristic, SAT, and graph-based planning paradigms.

6. Comparative Benchmarks and Key Findings

Empirical benchmarks across PlannerArena environments have yielded several notable findings:

• Plan graph–based planners (e.g., GRAPHPLAN-1) consistently outperform Petri net–based approaches (PETRIPLAN-1) unless solver inefficiencies are mitigated.
• Alternative representations (PETRIPLAN-2) yield more compact models and occasionally more optimal plans with fewer extraneous actions.
• Component-level decomposition reveals that perceived performance gaps often arise from inefficiencies in submodules (e.g., slow solvers) rather than inherent representational limitations.
• Standardizing the experimental environment allows robust attribution of observed differences directly to core algorithmic decisions.

This body of evidence underscores the importance of benchmarking infrastructures that allow transparent, empirical discrimination among competing planning techniques.

7. Broader Impact and Prospects

PlannerArena not only addresses the need for rigorous, reproducible evaluation in AI planning research but also functions as a catalyst for methodological standardization. Its combination of software frameworks, extensible data formats, visualization suites, and collaborative ecosystems has informed best practices across fields ranging from robotics to classical AI planning and hybrid approaches. The continuing evolution of such platforms is poised to accelerate algorithmic innovation, streamline deployment in real-world settings, and set benchmarks for future advances in automated planning and decision-making systems.