Explaining the Behaviour of Hybrid Systems with PDDL+ Planning
Authors: Diego Aineto, Eva Onaindia, Miquel Ramirez, Enrico Scala, Ivan Serina
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach computationally on three domains with different types of dynamics: piece-wise constant, linear and nonlinear. All domains feature non-deterministic (even arbitrary) discrete mode switches. |
| Researcher Affiliation | Academia | 1VRAIN, Universitat Polit ecnica de Val encia 2University of Melbourne 3Universit a degli Studi di Brescia |
| Pseudocode | No | The paper describes the approach and formalisms but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | Thermostat (constant). This domain uses Henzinger s thermostat [Henzinger, 2000] hybrid automaton. Platoon (linear). This domain [Makhlouf and Kowalewski, 2014] is a staple in the linear dynamics competition for formal verification of HS. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing, as it focuses on solving problem instances rather than training a model on a dataset with explicit splits. |
| Hardware Specification | Yes | All experiments run on an Intel Core i5 3.1GHz x 4, time and memory limits set to 300s and 8GB, respectively. |
| Software Dependencies | Yes | We tried different PDDL+ planners [Piotrowski et al., 2016; Penna et al., 2009; Cashmore et al., 2020], but focused on ENHSP [Scala et al., 2020] as it was the one giving us the better performance with support for the dynamics of all our domains. |
| Experiment Setup | Yes | We ran ENHSP using the hmrp max heuristic on top of plain A with a random tie-breaking... We keep δp = δe = 0.01 over all domains when ENHSP run using HSE P, and set δp = 0.1 in Thermostat and Platoon, and δp = 0.4 in Flight when ENHSP run with HSE Pe. |