Artificial intelligence problems are solved on electronic computers by techniques which make heavy use of address calculation and dynamic management of data storage space. Optical computing is normally associated with numerical problems in which the size of the data space is fixed and addressing may be handled in a predictable manner not affected by actual data values. A criterion is presented for determining the amount of dynamic storage management required for an expert system problem and several methods are discussed for eliminating unnecessary address manipulation by careful choice of data representation. Major emphasis is placed on the implementation of the mathematical technique of resolution. Various resolution strategies are analyzed and the impact of these strategies on storage management is assessed with a view to minimizing the complexity of processing. Finally, novel uses of electro-optical/electronic hybrids are considered for problems in which the state space grows drastically or where reversible control strategies are required to implement search methods.