It is well recognized that sequential pattern mining plays an essential role in many scientific and business domains. In this paper, a new extension of sequential pattern, attributes' sequential pattern, is propos...
详细信息
ETL tools are responsible for the extraction of data from sources, their cleansing and loading into a target data warehouse. However, nowadays, the design and development of ETL processes are performed in an in-house ...
详细信息
The use of the Internet raises serious behavioural issues regarding, for example, security and the interaction among agents that may travel across links. Model-building such interactive systems is one of the biggest c...
详细信息
A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique-the random sample consensus (RANSAC) to discard outliers in a...
详细信息
Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequences. In this paper, an immune particle swarm optimization (IPSO) is proposed, which is based on the models o...
详细信息
The optimization of job-shop scheduling is very important because of its theoretical and practical significance. This paper proposes an efficient scheduling method based on artificial immune systems. In the proposed m...
详细信息
To solve the problem that when patterns are long, frequent sequential patterns mining may generate an exponential number of results, which often makes decision-makers perplexed for there is too much useless repeated i...
详细信息
We systematically propose a dual-phase algorithm, DualRank, to mine the optimal profit in retailing market. DualRank algorithm has two major phases which are called mining general profit phase and optimizing profit ph...
详细信息
This paper is concerned with solution of the consistent fundamental matrix estimation in a quadratic measurement error model. First an extended system for determining the estimator is proposed, and an efficient implem...
详细信息
Q-learning is an effective model-free reinforcement learning algorithm. However, Q-learning is centralized and competent only for single agent learning but not multi-agent learning because in later case the size of st...
详细信息
Q-learning is an effective model-free reinforcement learning algorithm. However, Q-learning is centralized and competent only for single agent learning but not multi-agent learning because in later case the size of state-action space is huge and will grow exponentially with the number of agents increasing. In the paper we present a distributed Q-learning algorithm to solving this problem. In our algorithm, the tasks of learning optimal action policy are distributed to each agent in team but not a central agent. In order to reduce the size of action-state space of multi-agent team we introduce a state-action space sharing strategy of agent team, through which one agent in team can use the states already explored by other agents before and need not take time to explore these states again. Additionally, our algorithm has the ability to allocate sub-goals dynamically among agents according to environment changing, which can make agent team coordinate more efficiently. Experiments show the efficiency of our algorithm when it is applied to the benchmark problem of predator-prey pursuit game, also called pursuit game, in which a team of predators coordinate to capture a prey.
暂无评论