MuRaLi: An architecture for multiple reasoning

Abstract

We propose that to realise a trade-off between the generality of problems that can be solved and efficiency of response, intelligent systems require representations of different ‘types’ of knowledge, and such heterogeneous knowledge can most effectively be represented through multiple models with heterogeneous representation formats. Three dimensions; generality, precision, and scope of models are suggested here to formulate a frame-work for the structuring of heterogeneous models in intelligent systems. We embed such models within an architecture for intelligent systems that supports adaptive responses to unfamiliar situations by switching between pre-specified models and learning of these for future use.