Eugene Fink Libri

Focusing on enhancing AI problem-solving efficiency, this research introduces a system that automates the process of changing problem representations and selecting suitable search algorithms. It highlights the critical role of representation in determining problem difficulty and efficiency. Unlike previous approaches that relied on user input for algorithm selection, this system integrates multiple algorithms for both problem-solving and representation improvement. The study aims to formalize representation concepts and validate the hypothesis that an effective system comprises three key components: libraries of algorithms and a control module for selection.

The study delves into restricted-orientation convexity, a geometric concept where intersections with specific lines remain connected, expanding on traditional convexity. It examines properties of this generalized convexity in multidimensional Euclidean spaces and presents restricted-orientation analogs of lines, hyperplanes, and other geometric entities. Additionally, the book introduces strong restricted-orientation convexity, highlighting its similarities to standard convexity and exploring the shared properties between them, offering a comprehensive understanding of these geometric structures.

The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor­ tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im­ provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible for the choice of algorithms appropriate for a given problem. We present a system that integrates multiple description-changing and problem-solving algorithms. The purpose of the reported work is to formalize the concept of representation and to confirm the following An effective representation-changing system can be built from three • a library of problem-solving algorithms; • a library of algorithms that improve problem descriptions; • a control module that selects algorithms for each given problem.

The authors explore the properties of this generalized convexity in multidimensional Euclidean space, and describ restricted-orientation analogs of lines, hyperplanes, flats, halfspaces, and identify major properties of standard convex sets that also hold for restricted-orientation convexity.