Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations
Authors: Joseph Kim, Christian Muise, Ankit Shah, Shubham Agarwal, Julie Shah
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the effectiveness of Bayes LTL for inferring contrastive explanations from sets of traces generated from International Planning Competition (IPC) planning domains... (Section 5.1). Table 2 shows the inference results on the tested domains and on problem instances of varying complexity. (Section 6). |
| Researcher Affiliation | Collaboration | Joseph Kim1 , Christian Muise2 , Ankit Shah1 , Shubham Agarwal2 and Julie Shah1 1MIT Computer Science and Artificial Intelligence Laboratory 2MIT-IBM Watson AI Lab {joseph_kim, ajshah, julie_a_shah}@csail.mit.edu, {christian.muise, shubham.agarwal}@ibm.com |
| Pseudocode | No | The paper describes algorithmic steps in prose (e.g., 'MH sampling requires a user-defined proposal function F(ϕ |ϕ) that samples a new candidate ϕ given the current ϕ.'). It does not present structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper provides a link to a baseline's code: 'https://github.com/gergia/samples2LTL (commit: 69f692a).' There is no statement or link for the authors' own code for Bayes LTL. |
| Open Datasets | Yes | We evaluated the effectiveness of Bayes LTL for inferring contrastive explanations from sets of traces generated from International Planning Competition (IPC) planning domains [Long and Fox, 2003]. |
| Dataset Splits | Yes | We collected twenty traces for each set. (Section 5.1). A total of 24 instances (i.e. traces) of LFEs were separated into positive and negative sets by a subject matter expert. The detail of the input was as follows: |πA|=16, |πB|=8, |V |=15, and the average length of traces involved 11 time steps. (Section 6). |
| Hardware Specification | Yes | All experiments were conducted on Debian machines with Intel Xeon E3-1200 CPUs at 1.8 GHz using up to 4 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Debian machines' but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | α = β = 0.01, to put equal importance of positive and negative sets, λ = 0.7 to penalize ϕ for every additional conjunct, and ϵ = 0.2 to apply ϵ-greedy search in the the proposal function. We ran the MH sampler with num MH = 2, 000 iterations with the first 300 used as a burn-in period. |