However, systems that rely primarily on reactive techniques tend to lack deep understanding and behavioral flexibility. Essentially, knowledge-based inference and planning are replaced by rote behavior, conditioned by miscellaneous features of the current discourse state and observations in the world. Furthermore, deliberate reasoning and plan synthesis seem necessary for an agent that can acquire effective goal-directed plans and behaviors autonomously. Although random trial and error (as in reinforcement learning), supervised learning, and learning by imitation are other learning options, their potential is limited. Random trial and error is apt to be impractical in the enormously large state space of linguistic and commonsense behavior; supervised learning (of appropriate choices based on contextual features) seems at best able to induce rote plan recognition and discourse state transitions (reactive behaviors of types (iii) and (iv) above); and imitation is possible only when relevant, readily observable exemplary behaviors can be presented to the learner—and by itself, it can lead only to rote, rather than reasoned, behavior.