Discrete models of continuous behavior of collective adaptive systems
This work addresses a modeling challenge for researchers in collective adaptive systems, but it appears incremental as it builds on existing frameworks like Heraklit without demonstrating broad new applications or breakthroughs.
The paper tackles the problem of representing continuous movement in discrete models of collective adaptive systems, such as artificial ants, by proposing a modeling framework that structures behavior based on causal dependencies rather than temporal relations, using the Heraklit framework as an example.
Artificial ants are "small" units, moving autonomously on a shared, dynamically changing "space", directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of "ants" in discrete models. More generally, we challenge the role of the notion of "time" in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies, and not along temporal relations. We present all arguments by help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that already has proven its worth in many contexts.