We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, eciently searching not only

systems, epistemological systems hierarchy, experimental frame, data system, generative system, behavior system, state-transition system, structure system, metasystem, methodological distinctions, systems problems

Development of formal infrastructure for perception of

Questions posed to knowledge based systems typically omit information that is essential for answering them. Problem solvers, therefore, take on the responsibility of elaborating them before they can be answered. Automating this process is especially challenging when the problem solver is meant

ABSTRACT:- This paper presents a proposed Framework for an Intelligent Problem Solver (IPS). This framework is based on representing the problem state using a proposed representation, with a representation language to represent the operators or rules that can be used to move from one state

Boolean Satisfiability is probably the most studied of 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

; Goor and Sommerfeld, 1975), and more recent efforts have involved the simulation of thought processes via computer (Newell and Simon, 1972) rather than an examination of human interaction and com-munication patterns of creative problem solvers in groups. Further, these and other efforts have been

of the art approach for constructing such algorithms is the Gröbner basis method for solving systems of polynomial equations. Previously, the Gröbner basis solvers were designed ad hoc for concrete problems and they could not be easily applied to new problems. In this paper we propose an automatic procedure

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a

Several numerical schemes for the solution of hyperbolic conservation laws are based on exploiting the information obtained by considering a sequence of Riemann problems. It is argued that in existing schemes much of this information is degraded, and that only certain features of the exact solution