Principles of artificial intelligence
Principles of artificial intelligence
Query caching and optimization in distributed mediator systems
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Integrating information via matchmaking
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Programming and Deploying Java Mobile Agents Aglets
Programming and Deploying Java Mobile Agents Aglets
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Impact: A Platform for Collaborating Agents
IEEE Intelligent Systems
Querying Heterogeneous Information Sources Using Source Descriptions
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The RETSINA MAS Infrastructure
Autonomous Agents and Multi-Agent Systems
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The Internet contains a vast array of sources that provide identical or similar services. When an agent needs to solve a problem, it may split the problem into "subproblems" and find an agent to solve each of the subproblems. Later, it may combine the results of these subproblems to solve the original problem. In this case, the agent is faced with the task of determining to which agents to assign the subproblems. We call this the agent selection problem (ASP for short). Solving ASP is complex because it must take into account several different parameters. For instance, different agents might take different amounts of time to process a request. Different agents might provide varying "qualities" of answers. Network latencies associated with different agents might vary. In this paper, we first formalize the agent selection problem and show that it is NP-hard. We then propose a generic cost function that is general enough to take into account the costs of (i) network and server loads, (ii) source computations, and (iii) internal mediator costs. We then develop exact and heuristic based algorithms to solve the agent selection problem.