Artificial Intelligence is that branch of Computer Science that designs and builds programs to automate those behaviors that, when seen in humans, are called "intelligent". This area of research has been important in computing at least since the design of the computer itself. Research and courses in Artificial Intelligence have been a part of the UNM curriculum for more than twenty-five years. The book Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Luger 2005) reflects our general approach. Select Research Projects to find more detailed information on the UNM AI research. This work lies largely within what are sometimes called the symbol based, learning, and stochastic components of AI.

The primary research in Artificial Intelligence at UNM is in the labs of Professors Lane and Luger. Current research focuses on symbol/stochastic hybrid systems, Bayesian modeling, time-series analysis, reinforcement learning, control and decision-making, relational learning, and developing first-order systems for diagnostics and prognostics. Our current application areas include security, bioinformatics, neuroimaging (with focus on understanding the neural substrates of learning), robotic navigation, streaming media, user modeling, and the real time diagnosis of complex mechanical environments.

We refer the reader to Professors Forrest, Caudell, and Ackley for alternative approaches to research in intelligent computation and to Professors Veroff and Kapur who specialize in automated reasoning and theorem proving.