Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
LTLf/LDLf Non-Markovian Rewards
Authors: Ronen Brafman, Giuseppe De Giacomo, Fabio Patrizi
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees. The aim of this paper is to bring to bear developments in the theory of temporal logic over finite traces to the problem of specifying and solving MDPs with non-Markovian rewards. With these tools, which were unavailable to earlier work, we are able to provide a cleaner, more elegant approach that builds on well understood semantics, much more expressive languages, and enjoys good algorithmic properties. |
| Researcher Affiliation | Academia | Ronen I. Brafman Ben-Gurion University, Beer-Sheva, Israel EMAIL Giuseppe De Giacomo, Fabio Patrizi Sapienza Universit a di Roma, Italy EMAIL |
| Pseudocode | Yes | 1: algorithm LDLf 2NFA 2: input LDLf formula ϕ 3: output NFA Aϕ = (2P, Q, q0, δ, F) 4: q0 {ϕ} 5: F { } 6: if ( (ϕ, ϵ) = true) then 7: F F {q0} 8: Q {q0, }, δ 9: while (Q or δ change) do 10: for (q Q) do 11: if (q (ψ q) (ψ, Θ) then 12: Q Q {q } 13: δ δ {(q, Θ, q )} 14: if ( (ψ q ) (ψ, ϵ) = true) then 15: F F {q } (Figure 1: LDLf 2NFA algorithm) |
| Open Source Code | No | The paper does not contain any statement or link indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | This is a theoretical paper that does not conduct empirical experiments or use datasets. |
| Dataset Splits | No | This is a theoretical paper that does not conduct empirical experiments or use datasets with train/validation/test splits. |
| Hardware Specification | No | This is a theoretical paper that does not describe empirical experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not describe empirical experiments or specific software implementations with version numbers. |
| Experiment Setup | No | This is a theoretical paper that does not describe empirical experiments or their setup details, such as hyperparameters or training configurations. |