The resurgence of effort within computational semantics has led to increased interest in various types of relation extraction and semantic parsing. While various manually annotated resources exist for enabling this work, these materials have been developed with different standards and goals in mind. In an effort to develop better general understanding across these resources, we provide a summar...

Would it be possible to explain the emergence of new computational ideas using the computation itself? Would it be feasible to describe the discovery process of new algorithmic solutions using only mathematics? This study is the first effort to analyze the nature of such inquiry from the viewpoint of effort to find a new algorithmic solution to a given problem. We define program reachability as...

In sequential principal component analyzers based on deflation of the input vector, deviations from orthogonality of the previous eigenvector estimates may entail a severe loss of orthogonality in the next stages. A combination of the learning method with subsequent Gram-Schmidt orthonormalization solves this problem, but increases the computational effort. For the “robust recursive least squar...

Simulating stochastic structures with inelastic material behavior is often done Monte Carlo simulations. The method robust but needs a huge computational effort. We propose new approach for the dynamic simulation of viscoelastic effort in magnitude one deterministic simulation. It based on separation viscous evolution equation time-dependent and time-independent terms. present calculation effec...

Diversity of computational rules would appear to be beneficial. After all being able to apply a combination of different rules to a particular problem has at least as much power as applying a single rule. However, it takes more time and effort to find multiple rules that work well together than it does to find one rule. We investigated a particular instantiation of this trade-off by using a sim...

y(t0) = y0 Here f(t, y) is a given function, t0 is a given initial time and y0 is a given initial value for y. The unknown in the problem is the function y(t). Two obvious considerations in deciding whether or not a given algorithm is of any practical value are (a) the amount of computational effort required to execute the algorithm and (b) the accuracy that this computational effort yields. Fo...

This paper describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer running Windows acting as a host and a network of transputers acting as processing nodes. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available worksta...

This paper describes an ongoing research effort to develop a single, unified computational paradigm for conjoint computing, which integrates concepts from symbolic processing, numeric processing and neural network technologies. The result of this research effort will be a new methodology for synthesizing intelligent systems. Neither the symbolic nor neural computational paradigms is sufficientl...

The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scann...