Despite significant achievements and current interest in machine learning and artificial intelligence, the quest for a theory of intelligence, allowing general and efficient problem solving, has done little progress. This work tries to contribute in this direction by proposing a novel framework of intelligence based on three principles. First, the generative and mirroring nature of learned representations of inputs. Second, a grounded, intrinsically motivated and iterative process for learning, problem solving and imagination. Third, an ad hoc tuning of the reasoning mechanism over causal compositional representations using inhibition rules. Together, those principles create a systems approach offering interpretability, continuous learning, common sense and more. This framework is being developed from the following perspectives: as a general problem solving method, as a human oriented tool and finally, as model of information processing in the brain.
翻译:尽管在机器学习和人工智能方面取得了重大成就和当前的兴趣,但寻求情报理论、允许一般性和高效率地解决问题,进展甚微,这项工作试图通过提出基于三项原则的新的情报框架,为这一方向作出贡献。第一,根据经验对投入的表述具有遗传和反射性质。第二,学习、解决问题和想象的有根有根、有内在动机和迭接过程。第三,利用抑制规则对因果构成表述的推理机制进行特别调整。这些原则共同形成了一种系统方法,提供了可解释性、持续学习、常识和更多内容。这一框架是从以下角度发展起来的:作为一般性解决问题的方法,作为面向人类的工具,最后,作为大脑信息处理的模型。