Key word "coordination and learning tasks"

Context: Many AI and machine-learning techniques are primarily focused on past-to-future extrapolations of statistical regularities in large amounts of data. We introduce a method that builds on an in-action sampling of probes from possible futures with preference for those that prove promising for maximizing the perceivable space of possibilities. This foresight-oriented (rather than hindsight-oriented) method is particularly promising for handling non-linear or abruptly emerging developments. Problem: What von Foerster called the Ethical Imperative seems less strictly derived from physical principles than other well-known concepts in his work. Regarding investigations in recent AI research, however, it appears that the Ethical Imperative corresponds almost literally to the so-called principle of Future State Maximization, a principle that lately has been applied successfully to a range of coordination and learning tasks. Method: We discuss the principle of Future State Maximization, as previewed by von Foerster, against a background of a general need for tackling uncertain futures by way of modeling, and introduce three computational investigations on different coordination tasks based on Future State Maximization. Results: We show that the principle of Future State Maximization corresponds to von Foerster’s Ethical Imperative and to constructivist principles, and that it lends itself to opening up interesting new horizons for AI research. Implications: The article suggests an interpretation of how von Foerster’s Ethical Imperative can be understood as a foresight- rather than hindsight-oriented method against a background of computer-based modeling and AI research. Furthermore, it shows that computer-based methods conform well with the epistemology of constructivism.

Key words: Agent-based modeling, coordination and learning tasks, Ethical Imperative, Future State Maximization, Heinz von Foerster