Cognitive structure is a student's subjective organization of an objective knowledge system, reflected in the psychological construction of concepts and their relations. However, cognitive structure assessment remains a long-standing challenge in student modeling and psychometrics, persisting as a foundational yet largely unassessable concept in educational practice. This paper introduces a novel framework, Cognitive Structure Generation (CSG), in which we first pretrain a Cognitive Structure Diffusion Probabilistic Model (CSDPM) to generate students' cognitive structures from educational priors, and then further optimize its generative process as a policy with hierarchical reward signals via reinforcement learning to align with genuine cognitive development levels during students' learning processes. Experimental results on four popular real-world education datasets show that cognitive structures generated by CSG offer more comprehensive and effective representations for student modeling, substantially improving performance on KT and CD tasks while enhancing interpretability.

In the context of visual navigation in unknown scenes, both "exploration" and "exploitation" are equally crucial. Robots must first establish environmental cognition through exploration and then utilize the cognitive information to accomplish target searches. However, most existing methods for image-goal navigation prioritize target search over the generation of exploratory behavior. To address this, we propose the Navigation with Uncertainty-driven Exploration (NUE) pipeline, which uses an implicit and compact scene representation, NeRF, as a cognitive structure. We estimate the uncertainty of NeRF and augment the exploratory ability by the uncertainty to in turn facilitate the construction of implicit representation. Simultaneously, we extract memory information from NeRF to enhance the robot's reasoning ability for determining the location of the target. Ultimately, we seamlessly combine the two generated abilities to produce navigational actions. Our pipeline is end-to-end, with the environmental cognitive structure being constructed online. Extensive experimental results on image-goal navigation demonstrate the capability of our pipeline to enhance exploratory behaviors, while also enabling a natural transition from the exploration to exploitation phase. This enables our model to outperform existing memory-based cognitive navigation structures in terms of navigation performance.

For the past few weeks, I've been engaged in an email exchange with my favourite anarcho-syndicalist Noam Chomsky. I reached out to him initially to ask whether recent developments in ANNs (artificial neural networks) had caused him to reconsider his famous linguistic theory Universal Grammar. Our conversation touched on the possible limitations of Deep Learning, how well ANNs really model biological brains and also meandered into more philosophical territory. I'm not going to quote Professor Chomsky directly in this article as our discussion was informal but I will attempt to summarise the key take-aways. Noam Chomsky is first and foremost a professor of linguistics (considered by many to be "the father of modern linguistics") but he is probably better known outside of academic circles as an activist, philosopher and historian.

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.

For more information or to get higher pictures resolution, contact the author (see contact information at the bottom of this article.) This is a different approach to solve the AI problem. It is a cognitive math based on pyramids built with self-programming logic gates through learning. A Boolean polynomial associated with a given truth table can be implemented with electronic logic gates. These circuits have pyramidal structures. Then I built pyramids accomplishing the generic form for any of these problems. Although I can choose the balance between pure logic and pure memory in which they operate, in general, always I prefer to use the maximum cognitive power mathematically possible. The result is an algorithmic that makes you feel as teacher in front of another human infinitely intelligent who learns looking for the logic that might exist in the patterns (input, output) fed in training.

Furthermore, the main conceptual foundations of AI--namely, the knowledge representation hypothesis of Brian Smith (1982) and the physical symbol system hypothesis of Allen Newell (1980)--are not discussed at all. These hypotheses have been considered fundamental cornerstones of AI research, but they are now being questioned as posing strong limitations on AI (Dahlbäck 1989; Dreyfus 1972; Winograd and Flores 1986). Given this perspective, the author concludes that AI's essential methodology is a continuous attempt to overcome the formal constraints of computer science and philosophy without sacrificing rigor. Although I liked the author's perspective, and I wholly agree with his main conclusion, both are just stated in the preface, and no further reference to them is given. Let's get a feeling of what this first volume is really about.

After more than sixty years, Shannon's research [1-3] continues to raise fundamental questions, such as the one formulated by Luce [4,5], which is still unanswered: "Why is information theory not very applicable to psychological problems, despite apparent similarities of concepts?" On this topic, Pinker [6], one of the foremost defenders of the computational theory of mind [6], has argued that thought is simply a type of computation, and that the gap between human cognition and computational models may be illusory. In this context, in his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides further theoretical interpretation by differentiating the two assumed systems of the cognitive functioning of the human mind. He calls them intuition (system 1) determined to be an associative (automatic, fast and perceptual) machine, and reasoning (system 2) required to be voluntary and to operate logical- deductively. In this paper, we propose an ansatz inspired by Ausubel's learning theory for investigating, from the constructivist perspective [9-12], information processing in the working memory of cognizers. Specifically, a thought experiment is performed utilizing the mind of a dual-natured creature known as Maxwell's demon: a tiny "man-machine" solely equipped with the characteristics of system 1, which prevents it from reasoning. The calculation presented here shows that [...]. This result indicates that when the system 2 is shut down, both an intelligent being, as well as a binary machine, incur the same energy cost per unit of information processed, which mathematically proves the computational attribute of the system 1, as Kahneman [7,8] theorized. This finding links information theory to human psychological features and opens a new path toward the conception of a multi-bit reasoning machine.

Each of the The second volume promises to inherent to the task of specifying objections is then analyzed from a draw on a characterization of AI's the deterministic or nondeterministic formal standpoint because the relevant essential methodology as continuous machine, and complexity of electric elements of formal theory are attempts to overcome the formal or logical circuits), physical limits of introduced in subsequent chapters. I hope to see my (that is, finite, discrete concepts can Lovelace's objection. Despite the criticisms dissipate after reading the never form a perfect model of a continuous introductory character of the chapter, second volume. Let's get a feeling of what this first and possible-world semantics. With volume is really about.