Planning Over Multi-Agent Epistemic States: A Classical Planning Approach
Authors: Christian Muise, Vaishak Belle, Paolo Felli, Sheila McIlraith, Tim Miller, Adrian Pearce, Liz Sonenberg
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the scheme above to convert a RP-MEP planning problem into a classical planning problem, which can be subsequently solved by any planner capable of handling negative preconditions and conditional effects. ... As a preliminary investigation, we varied some of the discussed parameters and report on the results in Table 1 (the first Corridor problem corresponds to the one presented by Kominis and Geffner). |
| Researcher Affiliation | Academia | Department of Computing and Information Systems, University of Melbourne Department of Computer Science, University of Toronto {christian.muise,paolo.felli,tmiller,adrianrp,l.sonenberg}@unimelb.edu.au, {vaishak,sheila}@cs.toronto.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We have verified the model of the pre-existing Thief problem, and all of the existing queries considered in the previous literature posed to demonstrate the need for nested reasoning (e.g., those found in (L owe, Pacuit, and Witzel 2011)) are trivially solved in a fraction of a second. As a more challenging test-bed, we modelled a setting that combines the Corridor problem (Kominis and Geffner 2014) and the classic Gossip problem (Entringer and Slater 1979). |
| Dataset Splits | No | The paper concerns classical planning problem solving and does not discuss dataset splits for training, validation, or testing in the context of machine learning model development. Thus, information about such splits is not applicable or provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the Fast Downward planner (Helmert 2006)' but does not specify a precise version number for this or any other software dependency. |
| Experiment Setup | No | The paper mentions varying 'some of the discussed parameters' but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |