Abstract not found

Specifying exact query concepts has become increasingly challenging to end-users. This is because many query concepts #e.g., those for looking up a multimedia object# can be hard to articulate, and articulation can be subjective. In this study,we propose a query-concept learner that learns query criteria through an intelligent sampling process. Our concept learner aims to ful#ll two primary design objectives: 1# it has to be expressive in order to model most practical query concepts, and 2# it must learn a concept quickly and with a small number of labeled data since online users tend to be too impatient to provide much feedback. To ful#ll the #rst goal, we model query concepts in k-CNF, which can express almost all practical query concepts. To ful#ll the second design goal, we propose our maximizing expected generalization algorithm #MEGA#, which converges to target concepts quickly by its two complementary steps: sample selection and concept re#nement. We also propose a divide-and-conquer method that divides the concept-learning task into G subtasks to achieve speedup. We notice that a task must be divided carefully, or search accuracy may su#er. Wethus employ a genetic-based mining algorithm to discover good feature groupings. Through analysis and mining results, we observe that organizing image features in a multi-resolution manner, and minimizing intragroup feature correlation, can speed up query-concept learning substantially while maintaining high search accuracy. Through examples, analysis, experiments, and an prototype implementation, we show that MEGA converges to query concepts signi#cantly faster than traditional methods. Keywords: query concept, relevance feedback, active learning, data mining. 1

This paper describes optimizations for checking proofs represented in the Edinburgh Logical Framework (LF). The optimizations allow large proofs to be checked eciently which cannot feasibly be checked using the standard algorithm for LF. The crucial optimization is a form of result caching. To formalize this optimization, a path calculus for LF is developed and shown equivalent to a standard calculus.

this paper every formula is equivalent to a formula in negation normal form

The paper ivestigates nite model building for rst order logic. We consider two main categories of methods: Mace-type and Falcontype methods. The paper has two goals: rst, presenting several improvements and strategies for the basic Mace-type and Falcon-type algorithms, second, comparing the eciency of dierent methods. The improvements to the Mace-type algorithms are focused on decreasing the size of the propositional subtasks. A new cell selection heuristics is introduced for the Falcon-type algorithms. The methods are implemented in the Gandalf theorem prover. We present both the eect of the introduced improvements and the comparison of method categories for several problem classes, based on their syntactical characteristics. Finally, several suggestions for further investigations are given.

Abstract. We present a new answer set solver nomore++. Distinguishing features include its treatment of heads and bodies equitably as computational objects and a new hybrid lookahead. nomore++ is close to being competitive with stateof-the-art answer set solvers, as demonstrated by selected experimental results. 1

We investigate the applicability of classical resolution-based theorem proving methods for the Semantic Web. We consider several well-known search strategies, propose a general schema for applying resolution provers and propose a new search strategy "chain resolution" tailored for large ontologies. Chain resolution is an extension of the standard resolution algorithm. The main idea of the extension is to treat binary clauses of the general form A(x)#B(x) with a special chain resolution mechanism, which is di#erent from standard resolution used otherwise.

Existing meta-programming languages operate on encodings of programs as data. This paper presents a new meta-programming language, based on an untyped lambda calculus, in which structurally reflective programming is supported directly, without any encoding. The language features call-by-value and call-by-name lambda abstractions, as well as novel reflective features enabling the intensional manipulation of arbitrary program terms. The language is scope safe, in the sense that variables can neither be captured nor escape their scopes. The expressiveness of the language is demonstrated by showing how to implement quotation and evaluation operations, as proposed by Wand. The language’s utility for meta-programming is further demonstrated through additional representative examples. A prototype implementation is described and evaluated.

In this paper, we describe an approach to theorem proving in Computational Tree Logic (CTL) which utilises classical first-order resolution techniques. Since there already exist a lot of well-developed first-order logic theorem provers, reusing those techniques provides great benefit for solving other similar problems. We do not attempt to prove CTL theorems directly within the temporal logic syntax. We first translate arbitrary CTL formulae into a normal form for CTL and then implement the CTL calculus using resolution in first-order logic. After that, we utilise an efficient first-order logic theorem prover, for example, VAMPIRE or SPASS to carry out proof. Further, this approach has the potential to be extended to solve problems in other logics. 1

Abstract. A classical higher-order logic PFsub of partial functions is defined. The logic extends a version of Farmer’s logic PF by enriching the type system of the logic with subset types and dependent types. Validity in PFsub is then reduced to validity in PF by a translation. 1