The MADLA planner: Multi-agent planning by combination of distributed and local heuristic search

We experimentally compare the newly proposed multi-heuristic scheme and the two used heuristics separately. The results show that the proposed solution outperforms classical (single-heuristic) distributed search with either one of the heuristics used separately. In the detailed experimental analysis, we show limits of the planner and of the used heuristics based on particular properties of the benchmark domains. In a comprehensive set of multi-agent planning domains and problems, we show that the MADLA Planner outperforms all contemporary state-of-the-art privacy-preserving multi-agent planners using a compatible planning model.