Modular Integration of Connectionist and Symbolic Processing in Knowledge-Based Systems Melanie Hilario * , Christian Pellegrini * & Frédéric Alexandre ** * CUI - University of Geneva, CH-1211 Geneva 4, Switzerland ** INRIA-Lorraine, BP 239 F-54506 Vandoeuvre-les-Nancy Cedex, France Email: hilario@cui.unige.ch, pell@cui.unige.ch, falex@loria.fr Abstract: MIX is an ESPRIT project aimed at developing strategies and tools for integrating symbolic and neural methods in hybrid systems. The project arose from the observation that current hybrid systems are generally small-scale experimental systems which couple one symbolic and one connectionist model, often in an ad hoc fashion. Hence the objective of building a versatile testbed for the design, prototyping and assessment of a variety of hybrid models or architectures, in particular those which combine diverse neural network models with rule/model-based, cased-based, and fuzzy reasoning. A multiagent approach has been chosen to facilitate modular implementation of these hybrid models, which will be tested in the context of real-world applications in the steel and automobile industries. 1. Introduction Current efforts at integrating symbolic and neural processing can be divided into two major approaches. In the unified approach, better known as connectionist symbol processing, neural networks are used as building blocks to create a functional symbolic architecture (e.g., a connectionist expert system). The unified approach has been actively investigated since the renaissance of neural networks in the 80’s, and is illustrated by systems such as DCPS (Touretzky & Hinton, 1985, 1988), TPPS (Smolensky, 1990), CAP2 (Schneider & Oliver, 1991), MACIE (Gallant, 1993), CONSYDERR (Sun, 1991), RUBICON (Samad, 1988, 1992) and NPS3 (Kasabov & Shishkov, 1993). In the hybrid approach, symbolic components and neural nets are com- bined to ally the advantages of both the symbolic and the connectionist paradigms. Integration via hybridization is the main focus of MIX (Modular Integration of Connectionist and Symbolic Processing in Knowledge-Based Systems), an ESPRIT project aimed at investigating strategies and developing tools for the integration of symbolic and neural components in knowledge-based systems. Although a number of connectionist symbol processing systems are hybrid in the sense that they combine different representation schemes (local/distributed hybrids) or different neural network models (e.g., back- propagation/competitive hybrids), these pure connectionist hybrids are in a different class from hybrid symbolic-connectionist systems, which comprise distinct but interacting symbolic and connectionist sub- systems. For the purposes of this paper, we will use the term “hybrid systems” to refer to hybrid symbolic- connectionist (HSC) systems, unless specified otherwise. This paper is organized as follows. The following section discusses the state of the art in HSC integration. Section 3 describes the MIX project, its research objectives as well as ongoing and future work. Section 4 concludes. International Symposium on Integrating Knowledge and Neural Heuristics (pp. 123-132). Pensacola, Florida. May, 1994.