The framing problem for visual representation modules in neuro-symbolism for artificial intelligence
Authors: fei Dingzhou 
Published: April 2022

In latest paper “Commonsense visual sensemaking for autonomous driving: on generalized neuro-symbolic online abduction integrating vision and semantics”, which appeared in journal “Artificial intelligence’’(Vol.299,2021), the authors claimed that It incorporates the latest technologies in visual computing and has been developed as a modular framework that can be generally used in hybrid architectures for real-time perception and control. It has been developed as a modular framework that can be generally used in hybrid architectures for real-time perception and control. This advancement represents a useful side of symbolism.Research on the visual computation has received a great deal of attention The demands on human-based cognitive computing are very high. for example, the implementation of autonomous driving functions depends on the effective cooperation of three components: perception, decision making and execution. The perception layer identifies people, objects, and signs on the road by simulating the human eye through multi-dimensional sensors; the decision layer evaluates and makes decisions through pre-processing such as algorithm fusion and feature extraction; the data is fused and output to each control unit in the execution layer; and finally, the hardware mechanism in the execution layer makes feedback actions to realize the full set of autonomous driving operations.In this work, we try to show that this kind of symbolism framework will encounter the so-called “the framing problem” as earlier attempts to formalize the changes of knowledge in event flows. The framing problem is the problem of finding a sufficient set of axioms for a feasible description for environments of the robot or automatic driving. The framing problem is naturally not a problem for connectionism, because the mathematical basis behind it is not formal logic. But among old-school AI, formal logic is still dominant, and implicit in the framing problem is also the flaw in old-school AI's use of symbolic computation as a model of the mind and perception. While end-to-end vision and control based on deep learning has (arguably) been successful for self-driving cars, integrating hybrid vision and semantic solutions at each step, there are clear needs in fulfilling the fundamental legal and ethical responsibilities involving interpretable, human-centered AI and industrialization. However, other paradigms may be worth adopting, for example, the abstract neuro-symbolic approach to online visual perception using answer set programming (ASP) is systematically formalized and fully implemented.In fact, the framing problem describes the problem of expressing facts about the world using first-order logic (FOL). Representing the state of a robot or an automatic driving in traditional FOL requires the use of several axioms that simply imply that things in the environment do not change arbitrarily. For example, we describe a 'block world' in terms of rules about stacking blocks on top of each other. In the FOL system, other axioms need to be used to infer the environment (for example, blocks cannot change position unless they are moved).