Automated Reasoning

@techreport{RISC7141,
author = {Besik Dundua and Temur Kutsia},
title = {{Higher-Order Pattern Unification Modulo Similarity Relations}},
language = {english},
abstract = {The combination of higher-order theories and fuzzy logic can be useful in decision-making tasks that involve reasoning across abstract functions and predicates, where exact matches are often rare or unnecessary. Developing efficient reasoning and computational techniques for such a combined formalism presents a significant challenge. In this paper, we adopt a more straightforward approach aiming at integrating two well-established and computationally well-behaving components: higher-order patterns on one side and fuzzy equivalences expressed through similarity relations based on minimum T-norm on the other. We propose a unification algorithm for higher-order patterns modulo these similarity relations and prove its termination, soundness, and completeness. This unification problem, like its crisp counterpart, is unitary. The algorithm computes the most general unifier with the highest degree of approximation when the given terms are unifiable.},
number = {25-03},
year = {2025},
month = {February},
keywords = {Unification, higher-order patterns, fuzzy similarity relations},
length = {20},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC7136,
author = {Wolfgang Schreiner and William Steingartner},
title = {{Semantics-Based Rapid Prototyping of a Subset of SQL}},
language = {english},
abstract = {This report documents the application of our semantics-based language generator SLANG to developing a rapid prototype of a non-trivial domain-specific language, a substantial subset of the Structured Query Language SQL that we have named SubSQL. After developing a mathematical/logical formulation of the language’s abstract syntax, formal type system, and denotational semantics, we have translated this formulation into a SLANG specification from which the SLANG software generates Java code that implements a parser, a printer, a type-checker, and an executor of the language. This implementation is based on several manually created Java classes that implement the mathematical domains and operations used in the formalization, a simple persistent database, and a high-level application programming interface that allows to execute complete SubSQL scripts from file or individual SubSQL commands within Java programs. The results represent a blueprint for the semantics-based development of other domain-specific languages of similar complexity.},
number = {25-02},
year = {2025},
month = {February},
keywords = {formal semantics of programming languages, domain specific languages, rapid prototyping, interpreters},
sponsor = {Aktion Österreich–Slowakei project 2024-05-15-001, KEGA project 030TUKE-4/2023},
length = {179},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC7056,
author = {G. Ehling and T. Kutsia},
title = {{Solving Quantitative Equations}},
language = {english},
abstract = {Quantitative equational reasoning provides a framework that extends equality to an abstract notion of proximity by endowing equations with an element of a quantale. In this paper, we discuss the unification problem for a special class of shallow subterm-collapse-free quantitative equational theories. We outline rule-based algorithms for solving such equational unification problems over generic as well as idempotent Lawvereian quantales and study their properties.},
number = {24-03},
year = {2024},
month = {April},
keywords = {quantitative equational reasoning, Lawvereian quantales, equational unification},
length = {23},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC7084,
author = {W. Windsteiger},
title = {{Gray-Box Proving in Theorema}},
language = {english},
abstract = {Many theorems in mathematics have the form of an implication, an equivalence, or an equality, and in the standard prover in the Theorema system such formulas are handled by rewriting. Definitions ofnew function- or predicate symbols are yet another example of formulas that require rewriting in their treatment inthe Theorema system. Both theorems and definitions in practice often carry conditions under which they are valid. Rewriting is, thus, only valid in cases where all side-conditions are met. On the other hand, many of these side-conditions are trivial and when presenting a proof we do not want to distract the reader with lengthy derivations that justify the side-conditions. The goal of this paper is to present the design and implementation of a mechanism that efficiently checks side-conditionsin rewriting while preserving the readability and the explanatory power of a mathematical proof, which has always been of centralinterest in the development of the Theorema system.},
number = {24-07},
year = {2024},
month = {July},
keywords = {Theorema, Automated Theorem Proving, Conditional Rewriting, Mathematica},
length = {8},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}

@techreport{RISC6684,
author = {Bruno Buchberger},
title = {{Is ChatGPT Smarter Than Master’s Applicants?}},
language = {English},
abstract = {During the selection procedure for a particular informatics fellowship program sponsored by Upper Austrian companies, I ask the applicants a couple of simple technical questions about programming, etc., in a Zoom meeting. I put the same questions to the dialogue system ChatGPT, [ChatGPT]. The result surprised me: Nearly all answers of ChatGPT were totally correct and nicely explained. Also, in the dialogues to clarify some critical points in the answers, the explanations by ChatGPT were amazingly clear and goal-oriented.In comparison: I tried out the same questions in the personal Zoom interviews with approximately 30 applicants from five countries. Only the top three candidates (with a GPA of 1.0, i.e., the highest possible GPA in their bachelor’s study) performed approximately equally well in the interview. All the others performed (far) worse than ChatGPT. And, of course, all answers from ChatGPT came within 1 to 10 seconds, whereas most of the human applicants' answers needed lengthy and arduous dialogues.I am particularly impressed by the ability of ChatGPT to extract meaningful and well-structured programs from problem specifications in natural language. In this experiment, I also added some questions that ask for proofs for simple statements in natural language, which I do not ask in the student's interviews. The performance of ChatGPT was quite impressive as far as formalization and propositional logic are concerned. In examples where predicate logic reasoning is necessary, the ChatGPT answers are not (yet?) perfect. I am pleased to see that ChatGPT tries to present the proofs in a “natural style” This is something that I had as one of my main goals when I initiated the Theorema project in 1995. I think we already achieved this in the early stage of Theorema, and we performed this slightly better and more systematically than ChatGPT does.I also tried to develop a natural language input facility for Theorema in 2017, i.e., a tool to formalize natural language statements in predicate logic. However, I could not continue this research for a couple of reasons. Now I see that ChatGPT achieved this goal. Thus, I think that the following combination of methods could result in a significant leap forward:- the “natural style” proving methods that we developed within Theorema (for the automated generation of programs from specifications, the automated verification of programs in the frame of knowledge, and the automated proof of theorems in theories), in particular, my “Lazy Thinking Method” for algorithm synthesis from specifications- and the natural language formalization techniques of ChatGPT.I propose this as a research project topic and invite colleagues and students to contact me and join me in this effort: Buchberger.bruno@gmail.com.},
number = {23-04},
year = {2023},
month = {January},
keywords = {ChatGPT, automated programming, program synthesis, automated proving, formalization of natural language, master's screening},
length = {30},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}
}