Weighted Action-coupled Semantic Network (wASN) for robot intelligence

Abstract

Semantic knowledge especially for robots is often used as a form of knowledge representation which includes ontological knowledge to support Description Logics and rule language. However, it is difficult to design rules which provide some goal-associated Context, Object and Space (COS)-action complexes to be used to infer alternative attempts for success of a goal, when current attempt is not satisfactory. To cope with such a difficulty, we propose a novel weighted Action-coupled Semantic Network (wASN) which is used to recursively update and infer goal-associated COS-action complexes. To be specific, as in the semantic networks, wASN is a graph consisting of nodes and edges, respectively, to represent concepts-such as perception, object, space and context, and to represent semantic relation between the concepts. And, those concepts are designed to be all coupled with their associated actions. On the other hand, as in neural networks, our proposed wASN is designed to have weighted values of the connections among semantic nodes. And those weighted values are summed at each node, and are used to adopt a node by “winner-takes-all” activation strategy. It is shown that wASN is useful to recursively get COS-action complexes for a specific task to find objects which are partially occluded or unobservable.