An open source rule induction tool for transfer-based SMT

In this paper we describe an open source tool for automatic induction of transfer rules. Transfer rule induction is carried out on pairs of dependency structures and their node alignment to produce all rules consistent with the node alignment. We describe an efficient algorithm for rule induction and give a detailed description of how to use the tool

CREKID: A computer code for transient, gas-phase combustion of kinetics

A new algorithm was developed for fast, automatic integration of chemical kinetic rate equations describing homogeneous, gas-phase combustion at constant pressure. Particular attention is paid to the distinguishing physical and computational characteristics of the induction, heat-release and equilibration regimes. The two-part predictor-corrector algorithm, based on an exponentially-fitted trapezoidal rule, includes filtering of ill-posed initial conditions, automatic selection of Newton-Jacobi or Newton iteration for convergence to achieve maximum computational efficiency while observing a prescribed error tolerance. The new algorithm was found to compare favorably with LSODE on two representative test problems drawn from combustion kinetics

Knowledge aquisition for expert systems: inducing modular rules from examples

Knowledge acquisition for expert systems is notoriously difficult, often demanding an enormous effort on the part of the domain expert, who is essentially expected to spell out everything he knows about the domain. The task is non-trivial and can be time-consuming and tedious. Machine learning research, particularly into automatic rule induction from examples, may provide a way of easing this burden. Arguably, the most popular and successful rule induction algorithm in general use today is Quinlan's ID3. ID3 induces rules in the form of decision trees. However, the research reported in this thesis identifies some major limitations of a decision tree representation. Decision trees can be incomprehensible, but more importantly, there are rules which cannot be represented by trees. Ideally, induced rules should be modular and should capture the essence of causality, avoiding irrelevance and redundancy. The information theoretic approach employed in ID3 is examined in detail and some of its weaknesses identified. A new algorithm is developed which, by avoiding these weaknesses, induces rules which are modular rather than decision trees. This algorithm forms the basis of a new rule induction program, PRISM. Given an ideal training set, PRISM induces a complete and correct set of maximally general rules. The program and its results are described using training sets from two domains, contact lens fitting and a chess endgame. Induction from incomplete training sets is discussed and the performance of PRISM is compared with that of ID3 with particular reference to predictive power. A series of experiments is described, in which PRISM and ID3 were applied to training sets of different sizes and predictive power calculated. The results show that PRISM generally performs better than ID3 in these two domains, inducing fewer, more general rules, which classify a similar number of instances correctly and significantly fewer incorrectly

Automatic Induction of Classification Rules from Examples Using N-Prism

www.dis.port.ac.uk/~bramerma One of the key technologies of data mining is the automatic induction of rules from examples, particularly the induction of classification rules. Most work in this field has concentrated on the generation of such rules in the intermediate form of decision trees. An alternative approach is to generate modular classification rules directly from the examples. This paper seeks to establish a revised form of the rule generation algorithm Prism as a credible candidate for use in the automatic induction of classification rules from examples in practical domains where noise may be present and where predicting the classification for previously unseen instances is the primary focus of attention

Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

Packed rules for automatic transfer-rule induction

We present a method of encoding transfer rules in a highly efficient packed structure using contextualized constraints (Maxwell and Kaplan, 1991), an existing method of encoding adopted from LFG parsing (Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001). The packed representation allows us to encode O(2n) transfer rules in a single packed representation only requiring O(n) storage space. Besides reducing space requirements, the representation also has a high impact on the amount of time taken to load large numbers of transfer rules to memory with very little trade-off in time needed to unpack the rules. We include an experimental evaluation which shows a considerable reduction in space and time requirements for a large set of automatically induced transfer rules by storing the rules in the packed representation