We foresee robots that bootstrap knowledge representations and use them for classifying relevant situations and making decisions based on future observations. Particularly for assistive robots, the bootstrapping mechanism might be supervised by humans who should not repeat a training phase several times and should be able to refine the taught representation. We consider robots that bootstrap structured representations to classify some intelligible categories. Such a structure should be incrementally bootstrapped, i.e., without invalidating the identified category models when a new additional category is considered. To tackle this scenario, we presented the Scene Identification and Tagging (SIT) algorithm, which bootstraps structured knowledge representation in a crisp OWL-DL ontology. Over time, SIT bootstraps a graph representing scenes, sub-scenes and similar scenes. Then, SIT can classify new scenes within the bootstrapped graph through logic-based reasoning. However, SIT has issues with sensory data because its crisp implementation is not robust to perception noises. This paper presents a reformulation of SIT within the fuzzy domain, which exploits a fuzzy DL ontology to overcome the robustness issues. By comparing the performances of fuzzy and crisp implementations of SIT, we show that fuzzy SIT is robust, preserves the properties of its crisp formulation, and enhances the bootstrapped representations. On the contrary, the fuzzy implementation of SIT leads to less intelligible knowledge representations than the one bootstrapped in the crisp domain.

The industrial multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices in challenging circumstances need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. The Multi-Agent Reinforcement Learning (MARL) controller trained with the Proximal Policy Optimization (PPO) algorithm can handle these complexities. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics and find that they are key to better performance. We investigated the performance of a fully connected neural network (FCN), LSTM, and Transformer model variants with varying depths and gated residual connections. Our results show that the transformer model of moderate depth with gated residual connections around the multi-head attention, multi-layer perceptron, and the transformer block (STrXL) proposed in this paper is optimal and boosts energy efficiency by an average of 22.1% for these complex spread waves over the existing spring damper (SD) controller. Furthermore, unlike the default SD controller, the transformer controller almost eliminated the mechanical stress from the rotational yaw motion for angled waves. Demo: https://tinyurl.com/yueda3jh

A teacher's knowledge base consists of knowledge of mathematics content, knowledge of student epistemology, and pedagogical knowledge. It has severe implications on the understanding of student's knowledge of content, and the learning context in general. The necessity to formalize the different content knowledge in approximate senses is recognized in the education research literature. A related problem is that of coherent formalizability. Existing responsive or smart AI-based software systems do not concern themselves with meaning, and trained ones are replete with their own issues. In the present research, many issues in modeling teachers' understanding of content are identified, and a two-tier rough set-based model is proposed by the present author for the purpose of developing software that can aid the varied tasks of a teacher. The main advantage of the proposed approach is in its ability to coherently handle vagueness, granularity and multi-modality. An extended example to equational reasoning is used to demonstrate these. The paper is meant for rough set researchers intending to build logical models or develop meaning-aware AI-software to aid teachers, and education research experts.

Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and compares it to various multi-criteria decision-making methods to classify the different climate transition risks an organisation may be exposed to. This approach allows us to use linguistic expressions for risk analysis and to better describe risks and their consequences. The results show that the risks ranked as the most critical for this organisation in their order were price volatility and raw materials availability, the change to less carbon-intensive production or consumption patterns, the increase in carbon taxes and technological change, and the associated development or implementation costs. These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study. These results highlight the importance of investments needed to meet regulatory requirements, which are the main drivers for organisations at the financial level.

This paper analyzes multi-step TD-learning algorithms within the `deadly triad' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping. In particular, we prove that n-step TD-learning algorithms converge to a solution as the sampling horizon n increases sufficiently. The paper is divided into two parts. In the first part, we comprehensively examine the fundamental properties of their model-based deterministic counterparts, including projected value iteration, gradient descent algorithms, and the control theoretic approach, which can be viewed as prototype deterministic algorithms whose analysis plays a pivotal role in understanding and developing their model-free reinforcement learning counterparts. In particular, we prove that these algorithms converge to meaningful solutions when n is sufficiently large. Based on these findings, two n-step TD-learning algorithms are proposed and analyzed, which can be seen as the model-free reinforcement learning counterparts of the gradient and control theoretic algorithms.

This paper presents different neural network-based classifier algorithms for diagnosing and classifying Anemia. The study compares these classifiers with established models such as Feed Forward Neural Network (FFNN), Elman network, and Non-linear Auto-Regressive Exogenous model (NARX). Experimental evaluations were conducted using data from clinical laboratory test results for 230 patients. The proposed neural network features nine inputs (age, gender, RBC, HGB, HCT, MCV, MCH, MCHC, WBCs) and one output. The simulation outcomes for diverse patients demonstrate that the suggested artificial neural network rapidly and accurately detects the presence of the disease. Consequently, the network could be seamlessly integrated into clinical laboratories for automatic generation of Anemia patients' reports Additionally, the suggested method is affordable and can be deployed on hardware at low costs.

Subsequently, soft sets and rough sets frequently inspires the exploration Gorzalczany [16] introduced the notion of of theories related to soft covering-based rough interval-valued fuzzy sets, where the membership degree sets [2, 3, 11, 13, 43], attaining substantial relevance of set elements lies within the interval [0,1]. in specific domains. However, in fuzzy environments, Interval-valued fuzzy sets are adept at handling scenarios rough set theory demonstrates inherent limitations, as where precise probabilities of set membership discussed in [42]. To overcome these challenges, Zhang are elusive, offering instead an interval within which and Zhan[42] integrated fuzzy sets, soft sets, and rough such probabilities are constrained [16, 29].

Nowadays, artificial intelligence is a very fast-developing field in computer research. Tools of artificial intelligence (AI) are commonly based on knowledge models. They may be completely unreadable for humans (eg weights of intersynaptic links in artificial neural networks) or may have a human-friendly form (eg decision trees, rules). Neuro-fuzzy systems are a method of artificial intelligence. They elaborate intelligible models based on fuzzy rules. The rules can be read and interpreted by humans. Thus, neuro-fuzzy systems are an example of explainable artificial intelligence (XAI). In this paper, we present an automatic transformation of rules elaborated by NFS into linguistic sentences in the natural English language.

In Linguistics, a grapheme is a written unit of a writing system corresponding to a phonological sound. In Natural Language Processing tasks, written language is analysed through two different mediums, word analysis, and character analysis. This paper focuses on a third approach, the analysis of graphemes. Graphemes have advantages over word and character analysis by being self-contained representations of phonetic sounds. Due to the nature of splitting a word into graphemes being based on complex, non-binary rules, the application of fuzzy logic would provide a suitable medium upon which to predict the number of graphemes in a word. This paper proposes the application of a Fuzzy Inference System to split words into their graphemes. This Fuzzy Inference System results in a correct prediction of the number of graphemes in a word 50.18% of the time, with 93.51% being within a margin of +- 1 from the correct classification. Given the variety in language, graphemes are tied with pronunciation and therefore can change depending on a regional accent/dialect, the +- 1 accuracy represents the impreciseness of grapheme classification when regional variances are accounted for. To give a baseline of comparison, a second method involving a recursive IPA mapping exercise using a pronunciation dictionary was developed to allow for comparisons to be made.

The integration of knowledge extracted from diverse models, whether described by domain experts or generated by machine learning algorithms, has historically been challenged by the absence of a suitable framework for specifying and integrating structures, learning processes, data transformations, and data models or rules. In this work, we extend algebraic specification methods to address these challenges within such a framework. In our work, we tackle the challenging task of developing a comprehensive framework for defining and analyzing deep learning architectures. We believe that previous efforts have fallen short by failing to establish a clear connection between the constraints a model must adhere to and its actual implementation. Our methodology employs graphical structures that resemble Ehresmann's sketches, interpreted within a universe of fuzzy sets. This approach offers a unified theory that elegantly encompasses both deterministic and non-deterministic neural network designs. Furthermore, we highlight how this theory naturally incorporates fundamental concepts from computer science and automata theory. Our extended algebraic specification framework, grounded in graphical structures akin to Ehresmann's sketches, offers a promising solution for integrating knowledge across disparate models and domains. By bridging the gap between domain-specific expertise and machine-generated insights, we pave the way for more comprehensive, collaborative, and effective approaches to knowledge integration and modeling.