A Musical Suite Composed by an Electronic Brain: Reexamining the Illiac Suite and the Legacy of Lejaren A. Hiller Jr.

In 1956, Lejaren A. Hiller, Jr., and Leonard Isaacson debuted the Illiac Suite, the first score composed with a computer. Its reception anticipated Hiller’s embattled career as an experimental composer. Though the Suite is an influential work of modern electronic music, Hiller’s accomplishment in computational experimentation is above all an impressive feat of postwar conceptual performance art. A reexamination of theoretical and methodological processes resulting in the Illiac Suite reveals a conceptual and performative emphasis reflecting larger trends in the experimental visual arts of the 1950s and 1960s, illuminating his eventual collaborations with John Cage and establishing his legacy in digital art practices.

Computational Estimate Visualisation and Evaluation of Agent Classified Rules Learning System

Student modelling and agent classified rules learning as applied in the development of the intelligent Pre-assessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results it is gathered that the system has performed according to its design specification.

Computational Experimentation

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

Computational Experimentation

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management.

Knowledge Management Research through Computational Experimentation

Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management. This article combines and builds upon recent results to describe a research approach that bridges the chasm between laboratory and field methods in KM: computational experimentation. As implied by the name, computational experiments are conducted via computer simulation. But such experiments can go beyond most simulations (e.g., incorporating experimental controls, benefiting from external model validation). And they can offer simultaneously benefits of laboratory methods (e.g., internal validity, lack of confounding) and fieldwork (e.g., external validity, generalizability). Further, computational experiments can be conducted at a fraction of the cost and time associated with either laboratory experiments or field studies. And they provide a window to view the kinds of meta-knowledge that are important for understanding knowledge management. Thus, computational experimentation offers potential to mitigate many limitations of both laboratory and field methods and to enhance KM research. We discuss computational modeling and simulation as a complementary method to bridge the chasm between laboratory and field methods—not as a replacement for either of these methods.

From Data to Knowledge to Discoveries: Artificial Intelligence and Scientific Workflows

Scientific computing has entered a new era of scale and sharing with the arrival of cyberinfrastructure facilities for computational experimentation. A key emerging concept is scientific workflows, which provide a declarative representation of complex scientific applications that can be automatically managed and executed in distributed shared resources. In the coming decades, computational experimentation will push the boundaries of current cyberinfrastructure in terms of inter-disciplinary scope and integrative models of scientific phenomena under study. This paper argues that knowledge-rich workflow environments will provide necessary capabilities for that vision by assisting scientists to validate and vet complex analysis processes and by automating important aspects of scientific exploration and discovery.