In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers using forward search and omitting duplicate detection, next, the solving process operates in backward direction only within these BFS layers thereby partitioning all BDDs according to the layers the states reside in. We provide experimental results for selected games and compare to a previous approach. This comparison shows that in most cases the new algorithm outperforms the existing one in terms of runtime and used memory so that it can solve games that could not be solved before with a general approach.
This paper presents experimental results showing that discourse structure is a useful element in identifying the focus of negation. We define features extracted from RST-like discourse trees. We experiment with the largest publicly available corpus and an off-the-shelf discourse parser. Results show that discourse structure is especially beneficial when predicting the focus of negations in long sentences.
Zhao, Ran (Carnegie Mellon University) | Deng, Yuntian (Harvard University) | Dredze, Mark (Johns Hopkins University) | Verma, Arun (Bloomberg) | Rosenberg, David (Bloomberg) | Stent, Amanda (Bloomberg)
Technical and fundamental analysis are traditional tools used to analyze individual stocks; however, the finance literature has shown that the price movement of each individual stock correlates heavily with other stocks, especially those within the same sector. In this paper we propose a general-purpose market representation that incorporates fundamental and technical indicators and relationships between individual stocks. We treat the daily stock market as a ‘market image’ where rows (grouped by market sector) represent individual stocks and columns represent indicators. We apply a convolutional neural network over this market image to build market features in a hierarchical way. We use a recurrent neural network, with an attention mechanism over the market feature maps, to model temporal dynamics in the market. We show that our proposed model outperforms strong baselines in both short-term and long-term stock return prediction tasks. We also show another use for our market image: to construct concise and dense market embeddings suitable for downstream prediction tasks.
Methods that calculate dense vector representations for text have proven to be very successful for knowledge representation. We study how to estimate dense representations for multi-modal data (e.g., text, continuous, categorical). We propose Feat2Vec as a novel model that supports supervised learning when explicit labels are available, and self-supervised learning when there are no labels. Feat2Vec calculates embeddings for data with multiple feature types, enforcing that all embeddings exist in a common space. We believe that we are the first to propose a method for learning self-supervised embeddings that leverage the structure of multiple feature types. Our experiments suggest that Feat2Vec outperforms previously published methods, and that it may be useful for avoiding the cold-start problem.
Cluster Hire is defined as a problem of hiring a group of experts to maximize profits with the ability to complete multiple projects simultaneously under a budget. It assumes that we have a set of projects which require skills and experts who possess various skills. The process of hiring a group of experts to complete a set of projects under the given conditions is proven to be the NP-hard problem. Individuals expect financial support (i.e.salary) which can be handled by a specific budget that we get, to work on the projects. Addition to maximizing the total profit, we are interested in hiring productive experts who can work many projects concurrently with effective result.
Video games have proved to be a very defying laboratory to study machine-learning techniques, such as Deep Reinforcement Learning (DRL) algorithms. This paper presents a new approach for a DRL-based agent trained through Deep Q-Network (DQN) technique to perform free kicks in FIFA 18 game. The main motivation for choosing this case study is the fact that, like in many situations of the real life, FIFA represents a stochastic environment. Coping with this task, the main contributions of the present paper consist on: inspired on the OpenAI Gym and on the OpenAI Universe platforms, implementing a new user-friendly interface (in terms of portability and use simplicity) to connect the learning module with the 3D FIFA's game environment; implementing a DRL-based agent for free kicks in FIFA that uses two distinct data representations retrieved from lower cost computational procedures. The results were validated through two evaluative parameters: score of well succeed kicks and training time.
Dutta, Ayan (University of North Florida) | Bhattacharya, Amitabh (University of North Florida) | Kreidl, O. Patrick (University of North Florida) | Ghosh, Anirban (University of North Florida) | Dasgupta, Prithviraj (University of Nebraska at Omaha)
Information collection is an important application of multi-robot systems especially in environments that are difficult to operate for humans. The objective of the robots is to maximize information collection from the environment while remaining in their path-length budgets. In this paper, we propose a novel multi-robot information collection algorithm that uses a continuous region partitioning approach to efficiently divide an unknown environment among the robots based on the discovered obstacles in the area, for better load-balancing. Our algorithm gracefully handles situations when some of the robots cannot communicate with other robots due to limited communication ranges.
In this paper, we aim to predict students' learning performance by combining two-modality sensing variables, namely eye tracking that monitors learners' eye movements and elec-troencephalography (EEG) that measures learners' cerebral activity. Our long-term goal is to use both data to provide appropriate adaptive assistance for students to enhance their learning experience and optimize their performance. An experimental study was conducted in order to collet gaze data and brainwave signals of fifteen students during an interaction with a virtual learning environment. Different classification algorithms were used to discriminate between two groups of learners: students who successfully resolve the problem-solving tasks and students who do not. Experimental results demonstrated that the K-Nearest Neighbor classifier achieved good accuracy when combining both eye movement and EEG features compared to using solely eye movement or EEG.