Conjoint computing: integrating numeric, symbolic and neural computing

An ongoing research effort is described whose goal is to develop a single, unified computational paradigm for conjoint computing which integrates concepts from symbolic processing, numeric processing, and neural network technologies. The result will be a novel methodology for synthesizing intelligent systems. By combining these technologies, it is possible to build systems that behave intelligently, i.e. operate in real time, exhibit adaptive, goal-oriented problem-solving skills, tolerate errors, exploit large amounts of knowledge, use symbols and abstractions and learn from the environment. Combining symbolic and neural network technologies and drawing on insights developed from the study of biological systems results in systems which do not exhibit the brittleness of current symbolic processors yet are able to plan, reason, and perform other cognitive processing tasks. The development of the conceptual, architectural, hardware and software framework required for conjoint computing is discussed. The preliminary research has resulted in the multilayered computational model described