Positional Games and QBF: The Corrective Encoding

Positional games are a mathematical class of two-player games comprising Tic-tac-toe and its generalizations. We propose a novel encoding of these games into Quantified Boolean Formulas (QBF) such that a game instance admits a winning strategy for first player if and only if the corresponding formula is true. Our approach improves over previous QBF encodings of games in multiple ways. First, it is generic and lets us encode other positional games, such as Hex. Second, structural properties of positional games together with a careful treatment of illegal moves let us generate more compact instances that can be solved faster by state-of-the-art QBF solvers. We establish the latter fact through extensive experiments. Finally, the compactness of our new encoding makes it feasible to translate realistic game problems. We identify a few such problems of historical significance and put them forward to the QBF community as milestones of increasing difficulty.Comment: Accepted for publication in the 23rd International Conference on Theory and Applications of Satisfiability Testing (SAT2020

Structured parallel programming for Monte Carlo Tree Search

The thesis is part of a bigger project, the HEPGAME (High Energy Physics Game). The main objective for HEPGAME is the utilization of AI solutions, particularly by using MCTS for simplification of HEP calculations. One of the issues is solving mathematical expressions of interest with millions of terms. These calculations can be solved with the FORM program, which is software for symbolic manipulation. Since these calculations are computationally intensive and take a large amount of time, the FORM program was parallelized to solve them in a reasonable amount of time.Therefore, any new algorithm based on MCTS, should also be parallelized. This requirement was behind the problem statement of the thesis: “How do we design a structured pattern-based parallel programming approach for efficient parallelism of MCTS for both multi-core and manycore shared-memory machines?”.To answer this question, the thesis approached the MCTS parallelization problem in three levels: (1) implementation level, (2) data structure level, and (3) algorithm level.In the implementation level, we proposed task-level parallelization over thread-level parallelization. Task-level parallelization provides us with efficient parallelism for MCTS to utilize cores on both multi-core and manycore machines.In the data structure level, we presented a lock-free data structure that guarantees the correctness. A lock-free data structure (1) removes the synchronization overhead when a parallel program needs many tasks to feed its cores and (2) improves both performance and scalability.In the algorithm level, we first explained how to use pipeline pattern for parallelization of MCTS to overcome search overhead. Then, through a step by step approach, we were able to propose and detail the structured parallel programming approach for Monte Carlo Tree Search.Algorithms and the Foundations of Software technolog

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics

Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

We are addressing the course timetabling problem in this work. In a university, students can select their favorite courses each semester. Thus, the general requirement is to allow them to attend lectures without clashing with other lectures. A feasible solution is a solution where this and other conditions are satisfied. Constructing reasonable solutions for course timetabling problem is a hard task. Most of the existing methods failed to generate reasonable solutions for all cases. This is since the problem is heavily constrained and an effective method is required to explore and exploit the search space. We utilize Monte Carlo Tree Search (MCTS) in finding feasible solutions for the first time. In MCTS, we build a tree incrementally in an asymmetric manner by sampling the decision space. It is traversed in the best-first manner. We propose several enhancements to MCTS like simulation and tree pruning based on a heuristic. The performance of MCTS is compared with the methods based on graph coloring heuristics and Tabu search. We test the solution methodologies on the three most studied publicly available datasets. Overall, MCTS performs better than the method based on graph coloring heuristic; however, it is inferior compared to the Tabu based method. Experimental results are discussed

Does complexity deter customer‐focus?

Economic models suggest that firms use a simple cost‐benefit calculation to evaluate customer requests for new product features, but an extensive organizational literature shows the decision to implement innovation is more nuanced. We address this theoretical tension by studying how firms respond to customer requests for incremental product innovations, and how these responses change when the requested innovation is complex. Using large sample empirical analyses combined with detailed qualitative data drawn from interviews, we find considerable variance in the relationship between customer demands, complexity, and investments in incremental innovations. The qualitative study revealed the importance of organization structures, competitive pressures, and incentives for resource allocation processes. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89508/1/947_ftp.pd

Production scheduling in a foundry machine shop

Bibliography: pages 89-92.The scheduling of production in job shops is generally accomplished in four stages; aggregate planning, machine loading, sequencing and detailed scheduling. In industrial job shops, the number of jobs and machines makes detailed scheduling a particularly complicated and unwieldy task. When faced with this situation, a typical response of managements is to simply ignore the problem and apply some remedial action by adapting existing company operation procedures. The first objective of this dissertation is to indicate the dangers and inefficiencies which result when the problem of detailed scheduling is ignored. This is done in terms of a case study analysis in which the problems which currently exist in the machine shop at Atlantis Aluminium, a jobbing foundry, are illustrated. The second objective is to develop a systematic approach for the solution of detailed scheduling in job shops. Major steps in this approach are: i) a classification of shop scheduling problems ii) a survey of relevant scheduling literature in order to determine existing detailed scheduling techniques iii) the design of the scheduling system This approach is illustrated by applying it to the machine shop at Atlantis Aluminium

Effective use of storyboarding as a co-design method to enhance power assisted exercise equipment for people with stroke

Power assisted exercise equipment designed to assist multi-directional movements represent an exercise solution for people with stroke. Users identified digitization of the equipment through a new Graphical User Interface (GUI) to display feedback on exercise performance as a development priority. The Medical Device Technology (MDT) framework was adopted to structure the four-stage digitization programme and ensure meaningful user involvement. This paper reports on stage two of the digitization programme, the aim of which was to create a prototype GUI. Storyboarding followed by participatory data analysis was selected as a co-design method to engage professional (n ¼ 6) and expert (n ¼ 8) end users to create artefacts and express preferences relevant to the design of the GUI. Four overarching themes emerged from thematic analysis of the data; (a) aesthetic format, (b) functional features, (c) exercise programme, (d) motivation and reward. The data was crystallized with external sources to generate a design criterion matrix which directed the first iteration of the prototype GUI. Storyboarding with participatory analysis was an effective method for engaging participants in the design of the GUI and associated user experience. This paper represents a novel application of storyboarding to the MDT framework in user centred digital design