This article describes a connectionist architecture, CAPS, for implementing production systems programmed in the OPS5 language. CAPS illustrates the potential benefits and obstacles in using artificial neural networks (i.e., connectionist networks) in expert systems. The main objective of CAPS is to enhance the performance of existing rule-based OPS5 programs (e.g., expert systems) by implementing them on connectionist networks. CAPS supports a subset of the OPS5 language which includes variables, negation, conjunction, and disjunction of conditions. It uses a translation program to transform an OPS5 program into a limitedly interconnected, fully trained neural network. The translation program eliminates the learning/training phase in creating a network.
CAPS resolves the variable binding problems normally associated with connectionist architectures by using local representations. Local representations also simplify the overall architecture and reduce the number of interconnections within the network, thus increasing the feasibility of hardware implementations. A hardware implementations of a CAPS network can potentially provide an 80- to 200- fold increase in parallelism over serial implementations, allowing significant increases in production system performance. Although CAPS advances the state of connectionist production systems, it still has some problems and limitations, particularly in handling simultaneously matched variables and performing conflict resolution.
The CAPS architecture has been tested by transforming small OPS5 programs into connectionist networks and simulating them on a connectionist simulator. In addition, various cases of the classic Monkey and Bananas program have been successfully simulated. CAPS demonstrates that connectionist architectures can perform rule-based symbolic reasoning and support dynamic variable bindings.
