Belief Integration and Source Reliability Assessment
Authors: Paolo Liberatore
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This article applies this common approach to the reliability of the information sources instead of single atoms as in answer set programming (Bonatti, Calimeri, Leone, & Ricca, 2010) or formulae like in autoepistemic logic (Gabbay & Schlechta, 2016). ... Here is a roadmap of the next sections: general definitions (reliability ordering, maxcons): Section 2; the framework is applied to the simple case in which sources are assessed as either reliable or not reliable, and each provides a single formula: Section 3; the sources are assessed as either reliable, partly reliable and unreliable: Section 5; ... The most common definition is for a single-class sequence (P) or, equivalently, a set of formulae P: Definition 2 A maxcon of a set of formulae P is a consistent subset of P such that no other consistent subset of P strictly contains it. ... Theorem 1 There exist three formulae A, B and C such that the partition (AB|C) is stable while (A|BC) is not. Proof. Three consistent formulae A, B and C are chosen so that their plain maxcons are {A, B} and {B, C}. Lemma 5 proves that this is possible because these sets are not contained in each other; for example, A = (x z) y, B = x z and C = (x z) y. |
| Researcher Affiliation | Academia | Paolo Liberatore EMAIL DIAG Sapienza University of Rome Via Ariosto 25, 00185, Rome, Italy |
| Pseudocode | Yes | Algorithm 1 (maxcon and induced partition of two classes) Input: a set of sources S = {S1, . . . , Sn} Output1: a weakly stable partition (R|S\R) Output2: a maxcon M inducing (R|S\R) and induced by (R|S\R) ... Algorithm 2 (Weakly and strongly stable partitions of two classes) Input: a set of sources S = {S1, . . . , Sn} Output1: a weakly stable partition Output2: a strongly stable partition |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide links to code repositories or supplementary materials for code. |
| Open Datasets | No | The paper focuses on theoretical models and examples (e.g., Example 1, Figure 1, Figure 2) to illustrate concepts. It does not use or refer to any publicly available datasets for experimental evaluation. For instance, Wikipedia is mentioned as a scenario for belief merging, not as a dataset for analysis: 'Between November 2005 and September 2006 Wikipedia had an article about Porchesia...' |
| Dataset Splits | No | The paper does not use any datasets for experimental evaluation, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical concepts, algorithms, lemmas, and proofs. It does not mention any experimental runs, performance evaluations, or specific hardware used for computations. |
| Software Dependencies | No | The paper describes theoretical models and algorithms (e.g., Algorithm 1, Algorithm 2) but does not provide any specific software dependencies or version numbers for implementation. |
| Experiment Setup | No | The paper describes a theoretical framework for belief integration and source reliability assessment, including formal definitions, lemmas, and theorems. It does not detail any experimental setups, hyperparameters, or system-level training settings as no empirical experiments are conducted. |