Constraint satisfaction problems are important in AI. Various distributed and parallel computing strategies have been proposed to solve these problems. In this paper, these strategies are classified as distributed-agent-based, parallel-agent-based, and function-agent-based distributed problem-solving strategies. These different strategies are presented and discussed. Parallel-agentbased strateg...

The paper presents what a Distributed system is. It also overviews the issues to be taken into consideration for developing the strategies for load balancing. Strategies for various types of Distributed systems are discussed. Each strategy considers the most economical transfer policy, selection policy, location policy, information policy. The paper concludes with comparison of various strategi...

The paper presents what a Distributed system is. It also overviews the issues to be taken into consideration for developing the strategies for load balancing. Strategies for various types of Distributed systems are discussed. Each strategy considers the most economical transfer policy, selection policy, location policy, information policy. The paper concludes with comparison of various strategi...

Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train as training complexity is proportional to inference, which is frequently non-linear in example sequence length. In this paper we investigate distributed training strategies ...

Distributed Artificial Intelligence is concerned with problem solving in which groups solve tasks. In this paper we describe strategies of cooperation that groups require to solve shared tasks effectively. We discuss such strategies in the context of a specific group problem solving application: collision avoidance in air traffic control. Experimental findings with four distinct air-traffic con...