Inferencing in Database Semantics

As a computational model of natural language communication, Database Semantics1 (DBS) includes a hearer mode and a speaker mode. For the content to be mapped into language expressions, the speaker mode requires an autonomous control. The control is driven by the overall task of maintaining the agent in a state of balance by connecting the interfaces for recognition with those for action. This paper proposes to realize the principle of balance by sequences of inferences which respond to a deviation from the agent’s balance (trigger situation) with a suitable blueprint for action (countermeasure). The control system is evaluated in terms of the agent’s relative success in comparison other agents and the absolute success in terms of survival, including the adaptation to new situations (learning). From a software engineering point of view, the central question of an autonomous control is how to structure the content in the agent’s memory so that the agent’s cognition can precisely select what is relevant and helpful to remedy a current imbalance in real time. Our solution is based on the content-addressable memory of a Word Bank, the data structure of proplets defined as non-recursive feature structures, and the time-linear algorithm of Left-Associative grammar.