Principles of artificial intelligence
Principles of artificial intelligence
A softbot-based interface to the Internet
Communications of the ACM
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
Query caching and optimization in distributed mediator systems
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
InfoSleuth: agent-based semantic integration of information in open and dynamic environments
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
Bidding Mechanisms for Data Allocation in Multi-Agent Environments
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Selectively Materializing Data in Mediators by Analyzing User Queries
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
PLINI: a probabilistic logic program framework for inconsistent news information
Logic programming, knowledge representation, and nonmonotonic reasoning
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There are now numerous agent applications that track interests of thousands of users in situations where changes occur continuously. [Shim et al., 1994] suggested that such agents can be made efficient by merging commonalities in their activities. However, past algorithms cannot merge more than 10 or 20 concurrent activities. We develop techniques so that a large number of concurrent activities (typically over 1000) can be partitioned into components (groups of activities) of small size (e.g. 10 to 50) so that each component's activities can be merged using previously developed algorithms (e.g. [Shim et al., 1994]). We first formalize the problem and show that finding optimal partitions is NP-hard. We then develop three algorithms - Greedy, A*-based and BAB (branch and bound). A*-based and BAB are both guaranteed to compute optimal solutions. Greedy on the other hand uses heuristics and typically finds suboptimal solutions. We implemented all three algorithms. We experimentally show that the greedy algorithm finds partitions whose costs are at most 14% worse than that found by A*-based and BAB--however, Greedy is able to handle over thousand concurrent requests very fast while the other two methods are much slower and able to handle only 10-20 requests. Hence, Greedy appears to be the best.