35 research outputs found
Symmetric Weighted First-Order Model Counting
The FO Model Counting problem (FOMC) is the following: given a sentence
in FO and a number , compute the number of models of over a
domain of size ; the Weighted variant (WFOMC) generalizes the problem by
associating a weight to each tuple and defining the weight of a model to be the
product of weights of its tuples. In this paper we study the complexity of the
symmetric WFOMC, where all tuples of a given relation have the same weight. Our
motivation comes from an important application, inference in Knowledge Bases
with soft constraints, like Markov Logic Networks, but the problem is also of
independent theoretical interest. We study both the data complexity, and the
combined complexity of FOMC and WFOMC. For the data complexity we prove the
existence of an FO formula for which FOMC is #P-complete, and the
existence of a Conjunctive Query for which WFOMC is #P-complete. We also
prove that all -acyclic queries have polynomial time data complexity.
For the combined complexity, we prove that, for every fragment FO, , the combined complexity of FOMC (or WFOMC) is #P-complete.Comment: To appear at PODS'1
Students´ language in computer-assisted tutoring of mathematical proofs
Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand.
This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora
Q(sqrt(-3))-Integral Points on a Mordell Curve
We use an extension of quadratic Chabauty to number fields,recently developed by the author with Balakrishnan, Besser and M ̈uller,combined with a sieving technique, to determine the integral points overQ(√−3) on the Mordell curve y2 = x3 − 4
The 1974 Bilingual Education Amendments: Revolution, Reaction or Reform
Purpose: The study examined in detail the legislative history
of the 1974 Bilingual Education Act, Section 105 of the Education
Amendments of 1974, Public Law 93-380. The study examined
the roles of Representatives, Senators, lobbyists,
judicial decisions, minority groups and Administration officials
in developing the 1974 Bilingual Education Act
Logic and lattices for a statistics advisor
The work partially reported here concerned the development ot a prototype Expert System for
giving advice about Statistics experiments, called ASA, and an inference engine to support
ASA, called ABASE.This involved discovering what knowledge was necessary for performing the task at a satis¬
factory level of competence, working out how to represent this knowledge in a computer, and
how to process the representations efficiently.Two areas of Statistical knowledge are described in detail: the classification of measure¬
ments and statistical variables, and the structure of elementary statistical experiments. A
knowledge representation system based on lattices is proposed, and it is shown that such
representations are learnable by computer programs, and lend themselves to particularly
efficient implementation.ABASE was influenced by MBASE, the inference engine of MECHO [Bundy et al 79a]. Both
are theorem provers working on typed function-free Horn clauses, with controlled creation of
new entities. Their type systems and proof procedures are radically different, though, and
ABASE is "conversational" while MBASE is not
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum