arXiv:2502.18836v1 Announce Type: new Abstract: This benchmark suite provides a comprehensive evaluation framework for assessing both individual LLMs and multi-agent systems in real-world planning scenarios. The suite encompasses eleven designed problems that progress from basic to highly complex, incorporating key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. The benchmark includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of both single-agent and multi-agent planning capabilities. Through standardized evaluation criteria and scalable complexity, this benchmark aims to drive progress in developing more robust and adaptable AI planning systems for real-world applications.
The article “arXiv:2502.18836v1” introduces a benchmark suite that aims to evaluate the performance of both individual LLMs (Language Model Models) and multi-agent systems in real-world planning scenarios. This comprehensive evaluation framework consists of eleven designed problems that range from basic to highly complex, incorporating important aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. The suite allows for scalability along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. It provides detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of single-agent and multi-agent planning capabilities. The ultimate goal of this benchmark is to drive progress in the development of more robust and adaptable AI planning systems for real-world applications, through standardized evaluation criteria and scalable complexity.
Driving Progress in AI Planning Systems: A New Benchmark Suite
Artificial Intelligence (AI) has come a long way, with significant advancements in various domains. However, there is still considerable room for improvement when it comes to real-world planning scenarios. To address this, a new benchmark suite has been developed, providing a comprehensive evaluation framework for assessing both individual LLMs (Language Model Models) and multi-agent systems.
The benchmark suite consists of eleven designed problems that progress from basic to highly complex. These problems incorporate key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem within the suite can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation.
One of the primary objectives of this benchmark suite is to establish standardized evaluation criteria. This will enable researchers and developers to test and compare the capabilities of both single-agent and multi-agent planning systems using a common framework. By defining specific evaluation metrics and providing baseline implementations using contemporary frameworks like LangGraph, this benchmark suite ensures rigorous testing of AI planning capabilities.
Traditionally, AI planning systems have relied on individual LLMs or single-agent approaches. While these can be effective in certain scenarios, they often struggle with complex real-world applications that involve multiple agents and dynamic environments. The benchmark suite aims to address this limitation by encouraging the development of more robust and adaptable AI planning systems.
Standardized Evaluation Criteria
Standardized evaluation criteria are crucial for fair and objective comparisons between different AI planning systems. The benchmark suite includes detailed specifications for each problem, defining the desired outcomes, constraints, and evaluation metrics. By using a common set of criteria, researchers can analyze the performance of their planning systems accurately.
The evaluation metrics consider factors such as the efficiency of planning, the ability to handle inter-agent dependencies, and the adaptability to unexpected disruptions. These metrics provide quantitative measures that can be used to assess and compare the performance of different planning systems.
Baseline Implementations
To facilitate the adoption and usage of the benchmark suite, baseline implementations using contemporary frameworks like LangGraph are provided. These implementations serve as a starting point for researchers and developers, allowing them to focus on improving and optimizing their algorithms rather than spending time on building the infrastructure from scratch.
The baseline implementations are designed to showcase the capabilities of the benchmark suite and demonstrate the potential of AI planning systems in real-world applications. They serve as a reference for developers to understand the expected performance and behavior of their systems.
Scalable Complexity
The benchmark suite’s problems are designed to be scalable along various dimensions, allowing researchers to test their planning systems under different levels of complexity. The number of parallel planning threads can be increased to evaluate the system’s performance under higher workload scenarios. Similarly, the complexity of inter-dependencies and the frequency of unexpected disruptions can be adjusted to assess adaptability and robustness.
This scalability offers a realistic simulation of real-world planning scenarios, where dynamic environments and interactions between agents constantly evolve. By benchmarking planning systems across a range of complexities, researchers can identify strengths and weaknesses in their algorithms and work towards improving them.
Driving Progress in AI Planning Systems
The new benchmark suite aims to drive progress in the development of more robust and adaptable AI planning systems for real-world applications. By providing standardized evaluation criteria, baseline implementations, and scalability, researchers and developers can improve their algorithms effectively.
Through rigorous testing and comparison, promising solutions can emerge, offering better planning capabilities for various industries. The benchmark suite encourages innovation, collaboration, and the exchange of ideas within the AI community, fostering the development of cutting-edge planning systems.
With the new benchmark suite, the possibilities for AI planning systems are expanding, opening doors to more advanced and efficient applications. As researchers continue to push the boundaries of AI, we can look forward to solutions that can address the complexities of real-world planning scenarios with unparalleled precision and adaptability.
The arXiv paper titled “Benchmark Suite for Real-World Planning Scenarios” introduces a comprehensive evaluation framework for assessing both individual LLMs (Logic Language Model) and multi-agent systems in real-world planning scenarios. This benchmark suite is designed to address the challenges faced by AI planning systems in real-world applications, such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions.
The suite consists of eleven carefully designed problems that range from basic to highly complex. These problems are meant to simulate real-world planning scenarios and provide a standardized evaluation platform for AI planning systems. One of the notable features of this benchmark suite is that it allows for scaling along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation.
To facilitate evaluation and comparison, the benchmark suite includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph. This enables researchers and developers to test and evaluate both single-agent and multi-agent planning capabilities using a common framework.
The ultimate goal of this benchmark suite is to drive progress in the development of more robust and adaptable AI planning systems for real-world applications. By providing standardized evaluation criteria and scalable complexity, researchers can assess the performance of their systems objectively and identify areas for improvement.
Looking ahead, this benchmark suite has the potential to significantly advance the field of AI planning by fostering competition and collaboration among researchers. As more researchers use this benchmark suite to evaluate their systems, it will likely lead to the development of more sophisticated planning algorithms and techniques. Additionally, the scalability of the benchmark suite allows for future expansion and inclusion of even more complex planning scenarios, further pushing the boundaries of AI planning capabilities.
Furthermore, this benchmark suite could also serve as a valuable tool for industry practitioners who are developing AI planning systems for real-world applications. By utilizing the evaluation framework and baseline implementations provided in the benchmark suite, practitioners can assess the performance of their systems against established standards and make informed decisions regarding system improvements.
In conclusion, the introduction of this benchmark suite for real-world planning scenarios is a significant contribution to the field of AI planning. It provides a comprehensive evaluation framework that addresses key challenges faced by planning systems in real-world applications. By driving progress in developing more robust and adaptable AI planning systems, this benchmark suite has the potential to greatly impact various industries and domains that rely on efficient planning and decision-making.
Read the original article