Ontology evolution in the Model Driven Semantic Web can be looked as a process of model transformations. A model-transformation based conceptual framework for ontology evolution is presented in the paper. Applications...
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ISBN:
(纸本)3885793989
Ontology evolution in the Model Driven Semantic Web can be looked as a process of model transformations. A model-transformation based conceptual framework for ontology evolution is presented in the paper. Applications of model transformations in every phase of ontology evolution process are described. The framework combines technologies of ontology evolution, Ontology Definition Metamodel and model transformations, and it can be looked as a method for ontology evolution in the Model Driven Semantic Web.
The traditional RBAC model already cannot express the complicated secure access control constraint of the workflow. Based on the traditional RBAC model, a new conditioned RBAC model named as CMWRBSAC is proposed on th...
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The configuration problem in manufacture is more complicated than most other fields. Therefore, the design of modeling and reasoning module for product configuration manager in manufacture is very important and comple...
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The workflow model is the abstract expression of the workflow or the business process. Following the WfMC reference model, a PKI-based lightweight workflow model named as PBLW is put forward in this paper. The framewo...
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Mobile ambients is a process calculus for modeling mobile agents in wide-area networks. It has important theoretical and practical values in studying concurrent and mobile computation as well as the security of intera...
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This paper presents two parallel semantics of constraint logic programs: multiset answer constraint semantics and game semantics, which differ entirely from the traditional semantics. When giving the first semantics, ...
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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...
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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...
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CP-networks provide a convenient means for expressing preferences in reasoning, but it is not good at handling preferences with hard constraints. The paper proposes a new approach, which transforms the CP-network with...
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CP-networks provide a convenient means for expressing preferences in reasoning, but it is not good at handling preferences with hard constraints. The paper proposes a new approach, which transforms the CP-network with hard constraints into one constraint hierarchy, therefore one could process preferences and constraints in a single formalism with fruitful constraint solving algorithms. Furthermore, illustrates it with some examples, proves that the transformation preserves the ceteris paribus property and presents some complexity results. Finally compares it with related work and concludes the paper.
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...
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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.
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