Using Scone‘s multiple-context mechanism to emulate human-like reasoning

Scone is a knowledge-base system developed specifically to support human-like common-sense reasoning and the understanding of human language. One of the unusual features of Scone is its multiple-context system. Each context represents a distinct world-model, but a context can inherit most of the knowledge of another context, explicitly representing just the differences. We explore how this multiple-context mechanism can be used to emulate some aspects of human mental behavior that are difficult or impossible to emulate in other representational formalisms. These include reasoning about hypothetical or counter-factual situations; understanding how the world model changes over time due to specific actions or spontaneous changes; and reasoning about the knowledge and beliefs of other agents, and how their mental state may affect the actions of those agents. The Scone Knowledge-Base System Scone is a knowledge representation and reasoning system – a knowledge-base system or KB system – that has been developed over the last few years by the author’s research group at Carnegie Mellon University (Fahlman 2006; see also www.cs.cmu.edu/~sef/scone/). Scone, by itself, is not a complete AI or decision-making system, and does not aspire to be; rather, it is a software component – a sort of smart active memory system – that is designed to be used in a wide range of software applications, both in AI and in other areas. Scone deals just with symbolic knowledge. Things like visualization, motor memory, and memory for sound sequences are also important for human-like AI, but we believe that those will have specialized representations of their own, linked in various ways to the symbolic memory. Scone has been used in a number of applications at Carnegie Mellon and with a few selected outside partners; we plan a general open-source release of Scone in the nea