Towards a Unified View of Social Laws with Instantaneous Actions
Authors: Alexander Tuisov, Evgeny Mishlyakov, Alexander Shleyfman , Erez Karpas
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that the added expressivity of the new compilation does not hurt its performance, and it achieves comparable performance to the previous state-of-the-art compilations. ... 4 Experimental Evaluation The compilation presented above is more general than the earlier approaches [Karpas et al., 2017; Nir et al., 2023], but this increased generality might lead to more challenging planning problems. To evaluate this trade-off, we conducted an empirical study, comparing the solving times for problems generated by our compilation (new) and the previous one (old). |
| Researcher Affiliation | Academia | 1Technion Israel Institute of Technology 2Bar-Ilan University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the compilation in detail using formal definitions of actions, preconditions, and effects, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code available at https://github.com/ Technion Cognitive Robotics Lab/up social laws ijcai2025 |
| Open Datasets | Yes | We performed the comparison on a set of benchmarks adapted from the first Competition of Distributed and Multiagent Planners [Stolba et al., 2015] and from the Numeric track of IPC 2023 [Taitler et al., 2024] described below: GRID: each agent starts at a randomly assigned point and navigates toward a designated finish point to exit the map. ... ZENOTRAVEL: aircraft transport individuals between cities... BLOCKSWORLD: robotic arms act as individual agents... DRIVERLOG: Drivers travel on foot or by truck... MARKET TRADER: In this trading scenario, agents (camels) travel between markets... EXPEDITION: In this domain, agents (sleds) navigate between waypoints... |
| Dataset Splits | No | The paper mentions using problem instances from established benchmarks (Competition of Distributed and Multiagent Planners, IPC 2023 Numeric track) and describes the domains (GRID, ZENOTRAVEL, BLOCKSWORLD, DRIVERLOG, MARKET, EXPEDITION). However, it does not specify any particular training, validation, or test splits for these datasets, only indicating the number of problems solved (coverage) for each. |
| Hardware Specification | Yes | The planners had a time limit of 30 minutes on an Intel i7-6700k CPU, and a memory limit of 8GB for Fast Downward and 16GB for ENHSP. |
| Software Dependencies | Yes | Both compilations were implemented1 on top of the Unified Planning Framework [upf, 2025], and all of our benchmarks were encoded in this framework. Classical problems were grounded using the Fast-Downward grounder [Helmert, 2006]; the planner used for both compilations was the LAMA-first planner since it is the baseline/winner of the last IPC 2023 Agile Track [Taitler et al., 2024]. Numeric problems were solved by ENHSP-20 [Scala et al., 2020]. |
| Experiment Setup | Yes | The planners had a time limit of 30 minutes on an Intel i7-6700k CPU, and a memory limit of 8GB for Fast Downward and 16GB for ENHSP. |