Interactions between perception and rule-construction in human and machine concept learning

Weitnauer E. Interactions between perception and rule-construction in human and machine concept learning. Bielefeld: Universität Bielefeld; 2016.Concepts are central to human cognition and one important type of concepts can be represented naturally with symbolic rules. The learning of such rule-based concepts from examples relies both on a process of perception, which extracts information from the presented examples, and a process of concept construction, which leads to a rule that matches the given examples and can be applied to categorize new ones. This thesis introduces PATHS, a novel cognitive process model that learns structured, rule-based concepts and takes the active and explorative nature of perception into account. In contrast to existing models, the PATHS model tightly integrates perception and rule construction. The model is applied to a challenging problem domain, the physical Bongard problems, and its performance under different learning conditions is analyzed and compared to that of human solvers

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard Problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Albeit new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Despite new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark.Comment: 22 pages, NeurIPS 202

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

Physical Bongard Problems

Part 4: Learning and Data MiningInternational audienceIn this paper, we introduce Physical Bongard Problems (PBPs) as a novel and potentially rich approach to study the impact the constraints of a physical world have on mechanisms of concept learning and scene categorization. Each PBP consists of a set of 2D physical scenes which are positive or negative examples of a concept that must be identified. We discuss the properties that make PBPs challenging, analyze computational and representational requirements for a computational solver, and describe a first implementation of such a system. It can solve a subset of non-trivial PBPs using a version space approach for achieving its scene categorizations. The key element is a physics engine that is used both for the construction of information-rich physical features and for the prediction of how a given situation might evolve

Perception and simulation during concept learning

Weitnauer E, Goldstone RL, Ritter H. Perception and simulation during concept learning. Psychological Review . 2023.A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel concepts. Bongard problems (BPs), which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. BPs are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new type of BP called physical Bongard problems (PBPs) is introduced, which requires solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The perceiving and testing hypotheses on structures (PATHS) computational model, which can solve many PBPs, is presented and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP. Spatially or temporally juxtaposing similar (relative to dissimilar) scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category. The core theoretical commitments of PATHS, which we believe to also exemplify open-ended human category learning, are (a) the continual perception of new scene descriptions over the course of category learning; (b) the context-dependent nature of that perceptual process, in which the perceived scenes establish the context for the perception of subsequent scenes; (c) hypothesis construction by combining descriptions into explicit rules; and (d) bidirectional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved)