In this article, we expose some of the issues raised by the critics of the neoclassical approach to rational agent modeling and we propose a formal approach for the design of artificial rational agents that includes some of the functions of emotions found in the human system. We suggest that emotions and rationality are closely linked in the human mind (and in the body, for that matter) and, therefore, need to be included in architectures for designing rational artificial agents, whether these agents are to interact with humans, to model humans' behaviors and actions, or both. We describe an Affective Knowledge Representation (AKR) scheme to represent emotion schemata, which we developed to guide the design of a variety of socially intelligent artificial agents. Our approach focuses on the notion of "social expertise" of socially intelligent agents in terms of their external behavior and internal motivational goal-based abilities. AKR, which uses probabilistic frames, is derived from combining multiple emotion theories into a hierarchical model of affective phenomena useful for artificial agent design. AKR includes a taxonomy of affect, mood, emotion, and personality, and a framework for emotional state dynamics using probabilistic Markov Models.