We present a method of endowing agents in an agent-based model (ABM) with sophisticated cognitive capabilities and a naturally tunable level of intelligence. Often, ABMs use random behavior or greedy algorithms for maximizing objectives (such as a predator always chasing after the closest prey). However, random behavior is too simplistic in many circumstances and greedy algorithms, as well as classic AI planning techniques, can be brittle in the context of the unpredictable and emergent situations in which agents may find themselves. Our method, called agent-centric Monte Carlo cognition (ACMCC), centers around using a separate agent-based model to represent the agents' cognition. This model is then used by the agents in the primary model to predict the outcomes of their actions, and thus guide their behavior. To that end, we have implemented our method in the NetLogo agent-based modeling platform, using the recently released LevelSpace extension, which we developed to allow NetLogo models to interact with other NetLogo models. As an illustrative example, we extend the Wolf Sheep Predation model (included with NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on agent performance and model dynamics. We find that ACMCC provides a reliable and understandable method of controlling agent intelligence, and has a large impact on agent performance and model dynamics even at low settings.

Robert Wilensky, professor emeritus of computer science at the University of California, Berkeley, and one of the campus's first faculty members in artificial intelligence when the field was just taking off, has died at age 61. He died at the Alta Bates Summit Medical Center in Oakland on Friday, March 15, of a bacterial infection. Wilensky's career at UC Berkeley spanned nearly 30 years, beginning in 1978 when he joined the faculty in computer science. He later was appointed a professor at the School of Information and Management Sciences (now the School of Information, or I School), which he helped form. His many research interests included the role of memory processes in natural language processing, language analysis and production and artificial intelligence in programming languages.

The goal of the Unix Consultant is to provide a natural language help facility that allows new users to learn operating systems conventions in a relatively painless way. UC is not meant to be a substitute for a good operating system command interpreter, but rather, an additional tool at the disposal of the new user, to be used in conjunction with other operating system components.