A Fast Goal Recognition Technique Based on Interaction Estimates
Authors: Yolanda E-Martin, Maria D. R-Moreno, David E. Smith
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that this approach is much faster, but still yields high quality results. [...] In Section 5 we present an empirical study, and in Section 6 we discuss future work. [...] We have conducted an experimental evaluation on planning domains used by Ram ırez and Geffner: Blocks Word, Intrusion, Kitchen, and Logistics. Each domain has 15 problems. The hypotheses set and actual goal for each problem were chosen at random with the priors on the goal sets assumed to be uniform. For each problem in each of the domains, we ran the LAMA planner [Richter et al., 2008] to solve the problem for the actual goal. The set of observed actions for each recognition problem was taken to be a subset of this plan solution, ranging from 100% of the actions, down to 10% of the actions. The experiments were conducted on an Intel Xeon CPU E5-1650 processor running at 3.20GHz with 32 GB of RAM. [...] Table 1 and 2 show the results. |
| Researcher Affiliation | Collaboration | Yolanda E-Mart ın1,2 and Mar ıa D. R-Moreno1 and David E. Smith3 1 Departamento de Autom atica. Universidad de Alcal a. Ctra Madrid-Barcelona, Km. 33,6 28871 Alcal a de Henares (Madrid), Spain. {yolanda,mdolores}@aut.uah.es 2 Universities Space Research Association. 615 National Ave, Suite 220, Mountain View, CA 94043 3 Intelligent Systems Division. NASA Ames Research Center. Moffett Field, CA 94035-1000 david.smith@nasa.gov |
| Pseudocode | No | The paper describes the steps for computing goal probabilities in Section 4.4 and rules for pruning in Section 4.2 using numbered lists within the regular text flow, but it does not present them in a formalized pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | We have conducted an experimental evaluation on planning domains used by Ram ırez and Geffner: Blocks Word, Intrusion, Kitchen, and Logistics. Each domain has 15 problems. The hypotheses set and actual goal for each problem were chosen at random with the priors on the goal sets assumed to be uniform. For each problem in each of the domains, we ran the LAMA planner [Richter et al., 2008] to solve the problem for the actual goal. The paper mentions using well-known planning domains but does not provide access information (links, DOIs, specific citations for their problem instances) for the specific problem sets they generated and used for their experiments. |
| Dataset Splits | No | The paper describes evaluating performance based on a 'subset of this plan solution, ranging from 100% of the actions, down to 10% of the actions' for 'observed actions.' It also mentions 'a second test where the set of observed actions for each recognition problem was considered to be the prefix of the plan solution.' However, it does not explicitly define or refer to standard train/validation/test dataset splits or their specific percentages/counts in the typical machine learning context. |
| Hardware Specification | Yes | The experiments were conducted on an Intel Xeon CPU E5-1650 processor running at 3.20GHz with 32 GB of RAM. |
| Software Dependencies | No | We ran the LAMA planner [Richter et al., 2008]... and an optimal planner HSP f [Haslum, 2008]... The paper mentions specific planners used (LAMA, HSPf) but does not provide their version numbers or any other software dependencies with version information. |
| Experiment Setup | Yes | The set of observed actions for each recognition problem was taken to be a subset of this plan solution, ranging from 100% of the actions, down to 10% of the actions. [...] We present two variations of our technique, with and without extension of the plan graph after pruning: GRI: the propagation of cost information through the plan graph considers interaction information. GRIE: same as above, but the pruned cost-plan graph is expanded until quiescence. GR I: the propagation of cost information through the plan graph does not consider interaction information. [...] using a range of time limits from 5 seconds up to 1800 seconds. |