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Qualitative research for education: An introduction to theories and methods

Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

/pdf/chaff-engineering-an-efficient-sat-solver-2ima1ekzuu.pdf

Chaff: engineering an efficient SAT solver

Sexual Behavior in the Human Male

A general logic program (abbreviated to "program" hereafter) is a set of roles that have both positive and negative subgoals. It is common to view a deductive database as a general logic program consisting of rules (IDB) slttmg above elementary relations (EDB, facts). It is desirable to associate one Herbrand model with a program and think of that model as the "meaning of the program, " or Its "declarative semantics. " Ideally, queries directed to the program would be answered in accordance with this model. Recent research indicates that some programs do not have a "satisfactory" total model; for such programs, the question of an appropriate partial model arises. Unfounded sets and well-founded partial models are introduced and the well-founded semantics of a program are defined to be its well-founded partial model. If the well-founded partial model is m fact a total model. it is called the well-founded model. It n shown that the class of programs possessing a total well-founded model properly includes previously studied classes of "stratified" and "locally stratified" programs, The method in this paper is also compared with other proposals in the literature, including Clark's "program completion, " Fitting's and Kunen's 3-vahred interpretations of it, and the "stable models" of Gelfond and Lifschitz.

/pdf/the-well-founded-semantics-for-general-logic-programs-1i37dmxhij.pdf

The well-founded semantics for general logic programs

The importance of transformations and normal forms in logic programming, and generally in computer science, is well documented. This paper investigates transformations and normal forms in the context of Defeasible Logic, a simple but efficient formalism for nonmonotonic reasoning based on rules and priorities. The transformations described in this paper have two main benefits: on one hand they can be used as a theoretical tool that leads to a deeper understanding of the formalism, and on the other hand they have been used in the development of an efficient implementation of defeasible logic.

Representation results for defeasible logic

Temporal reasoning is an important task in many areas of computer science including planning, scheduling, temporal databases and instruction optimisation for compilers. Given a knowledge-base consisting of temporal relations, the main reasoning problem is to determine whether the knowledge-base is satisfiable, i.e., is there a scenario which is consistent with the information provided. However, many real world problems are over-constrained (i.e. unsatisfiable). To date, there has been little research aimed at solving over-constrained temporal reasoning problems. Recently, we developed standard backtracking algorithms to compute partial scenarios, in the spirit of Freuder and Wallace's notion of partial satisfaction. While these algorithms were capable of obtaining optimal partial solutions, they were viable only for small problem sizes.

In this paper, we apply local search methods to overcome the deficiencies of the standard approach to solving over-constrained temporal reasoning problems. Inspired by our recent success in efficiently handling reasonably large satisfiable temporal reasoning problems using local search, we have developed two new local search algorithms using a random restart strategy and a TABU search. Further, we extend our previous constraint weighting algorithm to handle over-constrained problems. An empirical study of these new algorithms was performed using randomly generated under- and over-constrained temporal reasoning problems. We conclude that 1) local search significantly outperforms standard backtracking approaches on over-constrained temporal reasoning problems; and 2) the random restart strategy and TABU search have a superior performance to constraint weighting for the over-constrained problems. We also conjecture that the poorer performance of constraint weighting is due to distortions of non-zero global minima caused by the weighting process.

/pdf/solving-over-constrained-temporal-reasoning-problems-using-3wpeh05n01.pdf

Solving over-constrained temporal reasoning problems using local search

Defeasible logics provide several linguistic features to support the expression of defeasible knowledge. There is also a wide variety of such logics, expressing different intuitions about defeasible reasoning. However, the logics can only combine in trivial ways. This limits their usefulness in contexts where different intuitions are at play in different aspects of a problem. In particular, in some legal settings, different actors have different burdens of proof, which might be expressed as reasoning in different defeasible logics. In this paper, we introduce annotated defeasible logic as a flexible formalism permitting multiple forms of defeasibility, and establish some properties of the formalism.

Annotated defeasible logic

There is significant interest in the data mining and network management communities to efficiently analyse huge amounts of network traffic, given the amount of network traffic generated even in small networks. Summarization is a primary data mining task for generating a concise yet informative summary of the given data and it is a research challenge to create summary from network traffic data. Existing clustering based summarization techniques lack the ability to create a suitable summary for further data mining tasks such as anomaly detection and require the summary size as an external input. Additionally, for complex and high dimensional network traffic datasets, there is often no single clustering solution that explains the structure of the given data. In this paper, we investigate the use of multiview clustering to create a meaningful summary using original data instances from network traffic data in an efficient manner. We develop a mathematically sound approach to select the summary size using a sampling technique. We compare our proposed approach with regular clustering based summarization incorporating the summary size calculation method and random approach. We validate our proposed approach using the benchmark network traffic dataset and state-of-theart summary evaluation metrics.

An Efficient Technique for Network Traffic Summarization using Multiview Clustering and Statistical Sampling

Network traffic analysis is a process to infer patterns in communication. Reliance on computer network and increasing connectivity of these networks makes it a challenging task for the network managers to understand the nature of the traffic that is carried in their network. However, it is an important data analysis task, given the amount of network traffic generated. Summarization is a key data mining concept, which is considered as a solution for creating concise yet accurate summary of network traffic. In this paper, we propose a new definition of summary for network traffic which outperforms the existing state-of-the-art summarization techniques. Our approach is based on clustering algorithm which reduces the information loss incurred by the existing techniques. By analysing the traffic summarization results using most up to date evaluation metrics, we demonstrate that our approach achieves better summaries than others on benchmark KDD cup 1999 dataset and also on real life network traffic including simulated attacks.

Michael J. Maher

Papers

Representation results for defeasible logic

Solving over-constrained temporal reasoning problems using local search

Annotated defeasible logic

An Efficient Technique for Network Traffic Summarization using Multiview Clustering and Statistical Sampling

A Novel Approach for Network Traffic Summarization