Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
In this paper, we define a continuum of modeling styles, ranging from collections of very simple agents on one end to collections of very complex agents at the other end, and a s...
For many large systems the computational complexity of complete model-based diagnosis is prohibitive. In this paper we investigate the speedup of the diagnosis process by exploiti...
An agent-based approach is used to explain the formation of vortex swarms in biological systems. The dynamics of the multiagent system is described by 3N coupled equations, modeli...
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...