Abstract
Artificial Intelligence is a brain child of Alan Turing and his universal programmable computer. During the 1960s and 1970s, AI researchers used computers for exploring intuitions about intelligence and for writing programs displaying intelligent behavior. A significant change occurred however in the 1980s, as many AI researchers moved from the early AI paradigm of writing programs for ill-defined problems to writing solvers for well-defined mathematical models like Constraint Satisfaction Problems, Strips Planning, SAT, Bayesian Networks, Partially Observable Markov Decision Processes and General Game Playing. Solvers are programs that take a compact description of a particular model instance and automatically compute its solution. Unlike the early AI programs, solvers are general as they must deal with any instance that fits the model. Many ideas have been advanced to address this crisp computational challenge from which a number of lessons can be drawn. In this paper, I revisit the problem of generality in AI, look at the way in which this ‘Models and Solvers’ agenda addresses the problem, and discuss the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition.