Implementing Bounded Revision via Lexicographic Revision and C-revision
Authors: Meliha Sezgin, Gabriele Kern-Isberner
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that Bounded Revision can be characterized by three simple, yet elegant postulates and corresponds to a special case of a lexicographic revision, which inherits all relevant features of BR. Furthermore, we present methodological implementations of BR including conditional revision with c-revisions, making it directly usable for conditional revision tools. We state the following representation theorem, which proves that ϕα,β displays the right choice to characterize the change mechanism of BR by β w.r.t. α. Theorem 1 (Representation Theorem for BR). Theorem 2. Let be a plausibilistic TPO, αβ be a BR operator by β w.r.t. α and ℓbe a lexicographic revision operator. For αβ and ℓϕα,β with ϕα,β being the core of BR by β w.r.t. α, it holds that ω ( αβ) ω iff ω ( ℓϕα,β) ω . Theorem 3. Let κ be a ranking function and κ α β = κ α,β as defined in (9). Then (BR1) (BR3) hold for the corresponding plausibilistic TPOs κ and κ α,β. Theorem 4. Let κ be a ranking function. For the minimal c-revision κ c Φα,β = κc α,β as defined in (13) and κ α β = κ α,β from (9), it holds that the corresponding plausibilistic TPOs κc α,β and κ α,β are the same, i.e., ω κc α,β ω iff ω κ α,β ω . |
| Researcher Affiliation | Academia | Meliha Sezgin, Gabriele Kern-Isberner TU Dortmund University, Germany meliha.sezgin@tu-dortmund.de, gabriele.kern-isberner@tu-dortmund.de |
| Pseudocode | No | The paper does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a publicly available or open dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would require dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for running experiments. |
| Software Dependencies | No | The paper refers to other works and their tools, such as |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific details like hyperparameter values or training configurations. |