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This paper presents a volunteer-computing-based grid environment or called a desktop grid environment for Connect6 applications. The Connect6 application described in this paper is to let professional Connect6 players to develop or solve openings, based on two programs, NCTU6 and Verifier. NCTU6 is to make Connect6 moves, written by the team led by Wu [19][21]. NCTU6 Verifier (abbr. Verifier), modified from NCTU6, is to verify whether one player wins in a given game position, or to generate the defensive moves if not winning in the position. Since both NCTU6 and Verifier consume huge amount of computation resources and requires ondemand responses, we design a desktop grid environment that provides players with on-demand computing through dynamic resource provisioning. The underlying desktop grid achieves high throughput computing by harvesting the idle CPU times on desktop computers connected to the Internet.

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A nonogram puzzle is played on a rectangular grid of pixels with clues given in the form of row and column constraints. The aim of solving a nonogram puzzle, an NP-complete problem, is to paint all the pixels of the grid in black and white while satisfying these constraints. This paper proposes an efficient approach to solving nonogram puzzles. We propose a fast dynamic programming (DP) method for line solving, whose time complexity in the worst case is only, where the grid size is and is the average number of integers in one constraint, always smaller than . In contrast, the time complexity for the best line-solving method in the past is . We also propose some fully probing (FP) methods to solve more pixels before running backtracking. Our FP methods can solve more pixels than the method proposed by Batenburg and Kosters (before backtracking), while having a time complexity that is smaller than theirs by a factor of . Most importantly, these FP methods provide useful guidance in choosing the next promising pixel to guess during backtracking. The proposed methods are incorporated into a fast nonogram solver, named LalaFrogKK. The program outperformed all the programs collected in webpbn.com, and also won both nonogram tournaments that were held at the 2011 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011, Taiwan). We expect that the proposed FP methods can also be applied to solving other puzzles efficiently.

In a distributed system, detecting whether a given logical predicate is true on the global states is fundamental for testing and debugging the program. Detecting predicates by examining all global states is intractable due to the combinatorial nature of the problem. This work designs an efficient online algorithm that identifies the consistent and useless states each time a new state is reported. This paper formulates the optimality of detecting algorithms in terms of pseudo states, which are employed to represent unknown states to the monitor process. Based on this technique, memory space of the debugger can be minimized by removing the useless states without affecting the debugging results. While minimizing memory space, the proposed algorithm requires only O( p 2 M) time in total, where p is the number of processes, and M is the number of reported states.