How to search for millions of queens

Journal ArticleThe n-queens problem is a classical combinatorial problem in artificial intelligence (AI) area. Since its simplicity and regular structure, this problem has widely been chosen as a testbed to develop and benchmark new AI search problem-solving strategies in the AI community. Due to its inherent complexity, so far even very efficient AI search algorithms can only find a solution for n-queens problem with n up to about 100. In this manuscript we present a new probabilistic local search algorithm which is based on a gradient-based heuristic. This efficient algorithm is capable of finding a solution for over 1,000,000 queens in several CPU hours on a 25Mhz Motorola 68030 computer

Ringo: Interactive Graph Analytics on Big-Memory Machines

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.Comment: 6 pages, 2 figure

The Many Faces of Introspection

Introspection or the ability to observe one's own behavior is one of the most powerful capabilities of human intelligence; it is the basis for understanding and improvement of one's behavior and of human progress. Similarly, introspective computer systems, introduced in this thesis, examine, reason about, and change their own behavior in powerful new ways. Because the complexity of computers is rapidly increasing, yet is restricted by limited human resources, the most attractive quality of introspective computers is their ability to manage this growing complexity themselves. Self-managing computer systems would greatly expand the rational power and complexity of computer systems that can be successfully built. The main difficulty in constructing introspective computer systems is enabling the system to obtain a description of its complete behavior in a dynamic and unobtrusive way. This thesis proposes the partition of the system into two threads of control. The first thread performs the..