Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.Comment: AAAI 201

A Two-Stage GIS-Based Suitability Model for Siting Biomass-to-Biofuel Plants and its Application in West Virginia, USA

Woody biomass has been considered of low value because the cost of removal generally exceeded market price. New, valued-added markets to offset removal costs are necessary for utilization to be effective. In recent years the use of biomass as feedstock for biofuel production in the United States has been on the rise. A variety of liquid fuels can be produced from woody biomass; ethanol is one of the most promising. This study presents a two-stage approach to selecting woody biomass-based biofuel plants using Geographical Information System (GIS) spatial analysis and the multi-criteria analysis ranking algorithm of compromise programming. Site suitability was evaluated to minimize direct cost for investors and potential negative environmental impacts. The first step was to create a site suitability index using a linear fuzzy logic prediction model. The model involved 15 variables in three factor groups: (1) general physical conditions, (2) costs, and (3) environmental factors. The weights of the cost factors were determined using pairwise comparisons in the Analytical Hierarchy Process (AHP). The value of site suitability was reclassified into three categories (non-suitable, low-suitable, and high-suitable) using different classification methods. With a feasible plant location defined as an industrial site within the most suitable area, the second stage of the analysis used compromise programming to compare the potential sites. The criteria used to rank the potential sites included fuzzy distance to woody biomass, highways, railways, commercial airports, communities, and available parcel size. The AHP was used to compute the relative importance of each criterion. The top ten suitable sites were determined, and sensitivity analyses were conducted to derive the most preferred sites. The approach was successful in taking a large amount of non-commensurate spatial data and integrating a site-based ranking algorithm to find the top locations for biomass plants. It also has great potential and applicability to other suitability and site selection studies

Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious and time-consuming visual analysis of sleep recordings. This process involves sleep clinicians visually inspecting electromyogram (EMG) signals to identify abnormal movements. The distribution of characteristics that represent movement can be diverse and varied, ranging from brief moments of tensing to violent outbursts. This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model. Several features are extracted from 10 second mini-epochs, where each mini-epoch has been classified as 'leg-movement' or 'no leg-movement' based on annotations of movement from sleep clinicians. The distributions of the features from each category can be estimated accurately using Gaussian mixture models with the Dirichlet process as a prior. The available dataset includes 36 participants that have all been diagnosed with RBD. The performance of this framework was evaluated by a 10-fold cross validation scheme (participant independent). The study was compared to a random forest model and outperformed it with a mean accuracy, sensitivity, and specificity of 94\%, 48\%, and 95\%, respectively. These results demonstrate the ability of this framework to automate the detection of limb movement for the potential application of assisting clinical diagnosis and decision-making

An Analysis of Appalachian Hardwood Products in the Chinese Market

To improve information on log and lumber trade between the Appalachian region of the United States and China, 50 Chinese firms identified as potential and current hardwood products buyers were surveyed using a stratified random sampling method from January to May 2008. A series of questions were posed on the importance of certain attributes of the US products and how to make the trading process more efficient. Sampled information included business activities, location(s), annual sales, product distribution of firms importing hardwood products, customer satisfaction, potential barriers to trade, principal sources of supply, product types, grades, and species of hardwood products imported from the United States. Results indicated that the Appalachian region of the United States is an important hardwood source and will continue to play an important role in the Chinese market. Red and white oaks were the most frequently imported species, followed by hard (sugar) maple, black cherry, soft maple, and ash. The Appalachian hardwood logs entered the markets largely in east and north central China, whereas the hardwood lumber importers were mainly distributed in east, south, and north central China. Some ongoing issues such as the impact of Russia's log tariff, the Lacey Act, and others on China's wood supply were also raised. The results should be helpful for Appalachian hardwood producers to further explore opportunities to promote their products in the Chinese markets

Economic Feasibility of a Woody Biomass- Based Ethanol Plant in Central Appalachia

A mixed-integer programming model is developed to assess the economic feasibility of siting a woody biomass-based ethanol facility in the central Appalachian hardwood region. The model maximizes the net present value (NPV) of a facility over its economic life. Model inputs include biomass availability, biomass handling system type, plant investment and capacity, transportation logistics, feedstock and product pricing, project financing, and taxes. Four alternative woody biomass handling systems, which include all processes from stand to plant, are considered. Eleven possible plant locations were identified based on site selection requirements. Results showed that the optimal plant location was in Buckhannon, West Virginia. The NPV of the plant with a demand of 2,000 dry tons of woody biomass per day varied from $68.11 million to$84.51 million among the systems over a 20-year plant life. Internal rate of return (IRR) of the facility averaged 18.67% for the base case scenario. Average ethanol production costs were approximately $2.02 to$2.08 per gallon. Production costs were most impacted by biomass availability, mill residue purchase price, plant investment and capacity, ethanol yield, and financing. Findings suggest that a woody biomass-based ethanol facility in central Appalachia could be economically feasible under certain operational scenarios