Analyzing Intentional Behavior in Autonomous Agents under Uncertainty
Authors: Filip Cano Córdoba, Samuel Judson, Timos Antonopoulos, Katrine Bjørner, Nicholas Shoemaker, Scott J. Shapiro, Ruzica Piskac, Bettina Könighofer
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a case study, we show how our method can distinguish between intentional and accidental traffic collisions. ... In this section, we showcase our method on a traffic-related scenario related to Examples 1-2, and that is illustrated in Figure 2. ... All experiments were executed on an Intel Core i5 CPU with 16GB of RAM running Ubuntu 20.04. We use TEMPEST [Pranger et al., 2021] as our model checking engine. 6.1 Model of Environment 6.2 Analysis of a Trace 6.3 Comparative Analysis of Several Agents |
| Researcher Affiliation | Academia | 1Graz University of Technology 2Yale University 3 New York University {filip.cano, bettina.koenighofer}@iaik.tugraz.at, {samuel.judson, timos.antonopoulos, nick.shoemaker, scott.shapiro, ruzica.piskac}@yale.edu, kbjorner@nyu.edu |
| Pseudocode | No | The paper describes the methodology in prose and includes a high-level flowchart (Figure 1), but it does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Find code and experimental details in the accompanying repository https://github.com/filipcano/intentional-autonomous-agents. |
| Open Datasets | No | The paper describes modeling a custom environment and scenario (Section 6.1) rather than using a pre-existing, publicly available dataset with concrete access information. No dataset is mentioned for public access. |
| Dataset Splits | No | The paper analyzes a specific scenario and generates counterfactual scenarios for analysis, but it does not describe a traditional machine learning experimental setup with training, validation, and test dataset splits with specified percentages or sample counts. |
| Hardware Specification | Yes | All experiments were executed on an Intel Core i5 CPU with 16GB of RAM running Ubuntu 20.04. |
| Software Dependencies | No | The paper mentions using "TEMPEST [Pranger et al., 2021] as our model checking engine" and "Ubuntu 20.04", but it does not provide specific version numbers for software libraries, frameworks, or solvers beyond the operating system. |
| Experiment Setup | Yes | As thresholds to evaluate evidence of intention, we use δL ρ = 0.25, δU ρ = 0.75 and δσ = 0.5. ... We change the following variables: Slipperiness range... Slipperiness factor... Hesitancy factor... Visibility... The variables and the ranges considered for generating counterfactuals are summarized in Table 1. |