Systematic Literature Review: Kemampuan Pembuktian Matematis

Penelitian kajian literatur terkait kemampuan pembuktian matematis belum pernah dilakukan sebelumnya. Tujuan dalam penelitian ini adalah menganalisis studi-studi kualitatif terkait kemampuan pembuktian matematis pada tahun 2015-2022. Metode penelitian yang digunakan adalah Systematic Literature Review (SLR) dengan protokol PRISMA terhadap semua artikel hasil penelitian yang terindeks dalam Google Scholar, Garuda, ERIC, dan Semantic. Strategi pencarian disesuaikan dengan kriteria seleksi dan melibatkan beberapa variabel moderator yaitu tahun publikasi, jenjang pendidikan, indeks jurnal, materi penelitian, dan jenis pembuktian yang diteliti. Data yang diperoleh disajikan secara deskriptif kuantitatif. Hasil dalam penelitian SLR ini memperlihatkan bahwa studi terkait kemampuan pembuktian matematis relatif mengalami peningkatan meski sempat turun pada tahun 2019 dan 2021. Studi mayoritas dilakukan pada jenjang perguruan tinggi dan didominasi materi aljabar. Jenis pembuktian yang sering diteliti adalah pembuktian langsung. Saran untuk peneliti selanjutnya adalah studi lebih lanjut terkait kemampuan pembuktian di jenjang sekolah menengah dengan materi geometri serta jenis pembuktian lainnya

Fifty years of Hoare's Logic

We present a history of Hoare's logic.Comment: 79 pages. To appear in Formal Aspects of Computin

Formally Verified Bug-free Implementations of (Logical) Algorithms

Notwithstanding the advancements of formal methods, which already permit their adoption in a industrial context (consider, for instance, the notorious examples of Airbus, Amazon Web-Services, Facebook, or Intel), there is still no widespread endorsement. Namely, in the Portuguese case, it is seldom the case companies use them consistently, systematically, or both. One possible reason is the still low emphasis placed by academic institutions on formal methods (broadly consider as developments methodologies, verification, and tests), making their use a challenge for the current practitioners. Formal methods build on logics, “the calculus of Computer Science”. Computational Logic is thus an essential field of Computer Science. Courses on this subject are usually either too informal (only providing pseudo-code specifications) or too formal (only presenting rigorous mathematical definitions) when describing algorithms. In either case, there is an emphasis on paper-and-pencil definitions and proofs rather than on computational approaches. It is scarcely the case where these courses provide executable code, even if the pedagogical advantages of using tools is well know. In this dissertation, we present an approach to develop formally verified implementations of classical Computational Logic algorithms. We choose the Why3 platform as it allows one to implement functions with very similar characteristics to the mathematical definitions, as well as it concedes a high degree of automation in the verification process. As proofs of concept, we implement and show correct the conversion algorithms from propositional formulae to conjunctive normal form and from this form to Horn clauses

Finding Periodic Apartments : A Computational Study of Hyperbolic Buildings

This thesis presents a computational study of a fundamental open conjecture in geometric group theory using an intricate combination of Boolean Satisfiability and orderly generation. In particular, we focus on Gromov’s subgroup conjecture (GSC), which states that “each one-ended hyperbolic group contains a subgroup isomorphic to the fundamental group of a closed surface of genus at least 2”. Several classes of groups have been shown to satisfy GSC, but the status of non-right-angled groups with regard to GSC is presently unknown, and may provide counterexamples to the conjecture. With this in mind Kangaslampi and Vdovina constructed 23 such groups utilizing the theory of hyperbolic buildings [International Journal of Algebra and Computation, vol. 20, no. 4, pp. 591–603, 2010], and ran an exhaustive computational analysis of surface subgroups of genus 2 arising from so-called periodic apartments [Experimental Mathematics, vol. 26, no. 1, pp. 54–61, 2017]. While they were able to rule out 5 of the 23 groups as potential counterexamples to GSC, they reported that their computational approach does not scale to genera higher than 2. We extend the work of Kangaslampi and Vdovina by developing two new approaches to analyzing the subgroups arising from periodic apartments in the 23 groups utilizing different combinations of SAT solving and orderly generation. We develop novel SAT encodings and a specialized orderly algorithm for the approaches, and perform an exhaustive analysis (over the 23 groups) of the genus 3 subgroups arising from periodic apartments. With the aid of massively parallel computation we also exhaust the case of genus 4. As a result we rule out 4 additional groups as counterexamples to GSC leaving 14 of the 23 groups for further inspection. In addition to this our approach provides an independent verification of the genus 2 results reported by Kangaslampi and Vdovina

Generalising weighted model counting

Given a formula in propositional or (finite-domain) first-order logic and some non-negative weights, weighted model counting (WMC) is a function problem that asks to compute the sum of the weights of the models of the formula. Originally used as a flexible way of performing probabilistic inference on graphical models, WMC has found many applications across artificial intelligence (AI), machine learning, and other domains. Areas of AI that rely on WMC include explainable AI, neural-symbolic AI, probabilistic programming, and statistical relational AI. WMC also has applications in bioinformatics, data mining, natural language processing, prognostics, and robotics. In this work, we are interested in revisiting the foundations of WMC and considering generalisations of some of the key definitions in the interest of conceptual clarity and practical efficiency. We begin by developing a measure-theoretic perspective on WMC, which suggests a new and more general way of defining the weights of an instance. This new representation can be as succinct as standard WMC but can also expand as needed to represent less-structured probability distributions. We demonstrate the performance benefits of the new format by developing a novel WMC encoding for Bayesian networks. We then show how existing WMC encodings for Bayesian networks can be transformed into this more general format and what conditions ensure that the transformation is correct (i.e., preserves the answer). Combining the strengths of the more flexible representation with the tricks used in existing encodings yields further efficiency improvements in Bayesian network probabilistic inference. Next, we turn our attention to the first-order setting. Here, we argue that the capabilities of practical model counting algorithms are severely limited by their inability to perform arbitrary recursive computations. To enable arbitrary recursion, we relax the restrictions that typically accompany domain recursion and generalise circuits (used to express a solution to a model counting problem) to graphs that are allowed to have cycles. These improvements enable us to find efficient solutions to counting fundamental structures such as injections and bijections that were previously unsolvable by any available algorithm. The second strand of this work is concerned with synthetic data generation. Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm’s superiority over another. However, benchmarks are often limited and fail to reveal differences among the algorithms. First, we show how random instances of probabilistic logic programs (that typically use WMC algorithms for inference) can be generated using constraint programming. We also introduce a new constraint to control the independence structure of the underlying probability distribution and provide a combinatorial argument for the correctness of the constraint model. This model allows us to, for the first time, experimentally investigate inference algorithms on more than just a handful of instances. Second, we introduce a random model for WMC instances with a parameter that influences primal treewidth—the parameter most commonly used to characterise the difficulty of an instance. We show that the easy-hard-easy pattern with respect to clause density is different for algorithms based on dynamic programming and algebraic decision diagrams than for all other solvers. We also demonstrate that all WMC algorithms scale exponentially with respect to primal treewidth, although at differing rates