Artificial Intelligence - Special volume on qualitative reasoning about physical systems
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In order to deal with unexpected or illegal behavior in multi-agent systems, underlying causal models connecting the target system's behavior and each agent's behavior are indispensable. In this paper, we present a method for generating causal networks, which consist of arithmetic and differential relations for explicitly defined parameters and implicitly existing parameters embedded in the target system. The task consists of three components: 1) A macro-behavior rule generator, which prepares implicit parameters and generates the rules about system's behavior at macro-level. 2) A causal network constructor. 3) An explanation generator. In the course of this process, spatial extents are represented and reasoned with qualitative regions. We took, as an example for this method, the foraging behavior of ant colonies, which are typical mobile multi-agent systems with a local communication method by means of the chemical pheromone.