Srinivas Padmanabhuni

Towards an Enterprise Business Process Architecture Standard

An effective process architecture helps provide a high-level blueprint of the complexity underlying an enterprise, which is used by executive committees during key decision and change processes. As existing service standards focus on co-ordination, they fall short in describing the motivational structure depicted in such models. In order to progress towards standardization in this area of complexity, we discuss the ldquopracticalityrdquo of a process architecture, and present a set of 22 questions that can be used in the functional evaluation and construction of a process architecture. We use these questions to evaluate the current state-of-the-art"" in business process architecture. We then apply the knowledge gathered during our evaluation and other work to develop the proposal for a general mapping framework that is capable of answering the set of queries we have proposed.

Inductive constraint logic programming: An overview

This paper provides a brief introduction and overview of the emerging area of Inductive Constraint Logic Programming (ICLP). It discusses some of the existing work in the area and presents some of the research issues and open questions that need to be addressed.

A framework for learning constraints: Preliminary report

Constraints represent a powerful way of specifying knowledge in any problem solving domain. Typically the appropriate constraints for a given problem need to be fully specified. In general it is difficult to describe the appropriate constraints in every problem setting. Hence automatic constraint acquisition is an important problem.

Curried least general generalization: A framework for higher order concept learning

Continued progress with research in inductive logic programming relies on further extensions of their underlying logics. The standard tactics for extending expressivity include a generalization to higher order logics, which immediately forces attention to the computational complexity of higher order reasoning.

Learning how to program

Automated software engineering has long been a goal of artificial intelligence. There has been slow but steady progress towards understanding the processes underlying program synthesis and modification. One significant observation that came out of Richards and Waters (1986) Software Apprentice Project was that programmers repeatedly use certain program constructs or cliches to solve a variety of programming tasks. Richards developed Plan Description Language (PDL) to capture the association between various code fragments and the functional goals that they serve. Another development has been the understanding of the relationship between physical devices and programs. This understanding asserts that causal theories developed for reasoning with physical systems can be applied to reason with programs. This observation led to the development of debugging and program understanding tools. Here, we outline a framework for functional representation of programs which also allows us to automatically learn new programming constructs and to refine existing programs so as to achieve a more functionally complete program.