The Best-partitions Problem: How to Build Meaningful Aggregations ?

The design and the debugging of large distributed AI systems require abstraction tools to build tractable macroscopic descriptions. Data aggregation can provide such abstractions by partitioning the systems dimensions into aggregated pieces of information. This process leads to information losses, so the partitions should be chosen with the greatest caution, but in an acceptable computational time. While the number of possible partitions grows exponentially with the size of the system, we propose an algorithm that exploits exogenous constraints regarding the system semantics to find best partitions in a linear or polynomial time. We detail two constrained sets of partitions that are respectively applied to temporal and spatial aggregation of an agentbased model of international relations. The algorithm succeeds in providing meaningful high-level abstractions for the system analysis