Deriving Behavior from Goal Structure for the Intelligent Control of Physical Systems

Abstract

Given a physical system described by a structural decomposition together with additional constraints, a major task in Artificial Intelligence concerns the automatic identification of the system behavior. We will show in the present paper how concepts and techniques from different AI disciplines help solve this task in the case of the intelligent control of engineering systems. Following generative approaches grounded in Qualitative Physics, we derive behavioral specifications from structural and equational information input by the user in the context of the intelligent control of physical systems. The behavioral specifications stem from a teleological representation based on goal structures which are composed of three primitive concepts, i.e. physical entities, physical roles and actions. An ontological representation of goals extracted from user inputs facilitates both local and distributed reasoning. The causal reasoning process generates inferences of possible behaviors from the ontological representation of intended goals. This process relies on an Event Calculus approach. An application example focussing on the control of an irrigation channel illustrates the behavioral identification process.