ボンガード不良設定問題の解導出と等価な最小論理集合を再構成する述語論理アーキテクチャに関する研究

Human intelligence relying on brain information processing has two aspects of implicit memory and explicit memory functions. A possible hypothesis is that human intelligence is a consequence of the fusion of those two aspects, and then a question is addressed as to how the flexibility of making a frame of thinking depending on the context is reconstructed by the fusion. In the assumption that an autonomous classifier provides primitive labels indicating parts in a picture and a generalizer to represent the whole in an abstract way, the problem that remains unsolved is how semantic information can be coordinated to reach a conclusion to connect parts and the whole. Bongard problems question such an issue in the form of logical picture puzzles to request to seek the unique minimum description of pictures to discriminate two groups, throughout abductive reasoning. Bongard Problems (BPs) are a set of 100 visual puzzles introduced by M. M. Bongard in the mid-1960s. BPs have been established as benchmark puzzles for understanding the human context-based learning abilities to solve ill-posed problems. The puzzle requires the logical explanation as to the answer to distinguish two classes of figures from redundant options, which can be obtained by a thinking process to alternatively change the target frame (hierarchical level of analogy) of thinking from a wide range concept networks as D. R. Hofstadter suggested. Some minor research results to solve a limited set of BPs have reported based a single architecture accompanied with probabilistic approaches; however the central problem on BP’s difficulties is the requirement of flexible changes of the target frame; therefore non-hierarchical cluster analyses do not provide the essential solution, and hierarchical probabilistic models need to include unnecessary levels for learning from the beginning to prevent a prompt decision making. Only possible combinations of primitive descriptions like ‘circle in a triangle’ are arisen as a test hypothesis to represent them commonly, and then it is verified whether it matches all pictures totally in each group. The tested hypotheses from two groups are compared, and it will be the solution if there are logically different, such as ‘circle in a triangle’ v.s. ‘triangle in a circle.’ We hypothesized that the logical reasoning process with limited numbers of metadata descriptions realizes the sophisticated and prompt decision-making, and the performance is validated by using BPs. In this study, a semantic web-based hierarchical model to solve BPs was proposed as the minimum and transparent system to mimic the human-logical inference process in solving of BPs by using the Description Logic (DL) with assertions on concepts (TBox) and individuals (ABox). Our computer experiment showed that 65 Bongard problems were solved in the proposed framework. It indicates that the semantic information coordinator works well to solve a type of frame problem by coupling with an autonomous classifier and generalizer. The framework may contribute to the design of the general artificial intelligence in part, especially on coordination against autonomy in semantics. In summary, this thesis helps to pave the practical approach of understanding ill-posed problems and working towards solving them using ontologies. This demonstrates the effectiveness of the semantic network and description logic, and then the common principle is expected to be formulated, and the principle explains why the semantics and logic work well to solve the BPs to avoid an infinite time for the calculation. Our results demonstrated that the proposed model not only provided individual solutions as a BP solver but also proved the correctness of Hofstadter’s idea as the flexible frame with concept networks for BPs in our actual implementation, which no one has ever achieved. This in fact will open the new horizon for theories for designing logical reasoning systems, especially for critical judgments and serious decision-making as expert humans do in a transparent and descriptive way of why they judged so.九州工業大学博士学位論文 学位記番号：生工博甲第374号 学位授与年月日：令和2年3月25日1 Introduction|2 Semantic Web Technology|3 An RDF Based Knowledge Representation Towards Solving BP #39|4 A Semantic Web-based Representation of Human-logical Inference for Solving Bongard Problems|5 Solving BP with Dependent Properties|6 Discussion and Conclusion九州工業大学令和元年