Rule-based Research Articles

Semantic-based neural network repair for AI models in dynamic environments

This paper addresses a critical challenge in the field of artificial intelligence (AI) and deep learning (DL) - the debugging and repair of AI models within dynamic environments. The rapid development of frameworks like TensorFlow or PyTorch can introduce version incompatibilities, potentially breaking existing code. To tackle this issue, it can be seen that a semantic-based neural network repair method that leverages an existing semantics called ExAIS is proposed, defined in the Prolog logic programming language. This method identifies architectural and parameter errors in AI models and generates debug messages to facilitate the repair process. Key contributions of this paper include introducing a novel semantic-based AI model repair method capable of suggesting multiple changes to rectify inefficiencies in AI models. We evaluate the effectiveness of our approach in a real-world context by collecting a set of real bugs from AI developers. The background section provides an overview of AI framework testing, machine learning, neural architecture search (NAS), and deep learning. The methodology section details the repair method based on ExAIS semantics, covering error identification and repair suggestions as the two main steps. The evaluation section showcases the repair effectiveness on automatically generated and manually written models, emphasizing the applicability and efficiency of the approach.

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

Digital support of spatial planning processes in the regions

Subject. This article deals with the design and modeling of a data-centered tool for supporting spatial planning processes. Objectives. The article aims to develop a tool designed to solve the problem of automated formation of volumetric surfaces, which is necessary for assessing the innovative development of the region. Methods. For the study, we used the methods and approaches of the fuzzy logic theory, and logical programming methods. Results. An integrated approach to solving regional problems, taking into account the empirical knowledge of experts, helped develop a tool for assessing the innovative potential of the region, its approbation through using a Russian region as a case study is illustrated with graphs. Relevance. The formed triad of sustainable development makes it possible to reflect the behavior of the regional innovative development index as a whole on the basis of social, economic and environmental indicators of sustainability.

Dissociable roles of the dorsolateral and ventromedial prefrontal cortex in human categorization.

Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age = 69.6 years; 5 men, 5 women) to examine how prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared to healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants' data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision making.Significance Statement Category learning creates flexible memory representations that easily generalize to novel situations. Although it is generally established that the prefrontal cortex is central to categorization, it is unclear how different prefrontal subregions contribute to learning. Separate literatures have implicated both the dorsolateral prefrontal cortex (dlPFC) and the ventromedial prefrontal cortex (vmPFC) in categorization, but there has been little effort to bridge these literatures. The current study is the first to examine categorization in patients with lesions centered in the dlPFC and vmPFC. We found that, as a whole, the PFC orients attention to relevant stimulus information. The dlPFC is important for rule-based learning, whereas the vmPFC is important for focusing attention on highly diagnostic features and ignoring less predictive features.

Computationally Hard Problems for Logic Programs under Answer Set Semantics

Showing that a problem is hard for a model of computation is one of the most challenging tasks in theoretical computer science, logic and mathematics. For example, it remains beyond reach to find an explicit problem that cannot be computed by polynomial size propositional formulas (PF). As a model of computation, logic programs (LP) under answer set semantics are as expressive as PF, and also \(\mathtt{NP}\) -complete for satisfiability checking. In this paper, we show that the PAR problem is hard for LP, i.e., deciding whether a binary string contains an odd number of \(1\) ’s requires exponential size logic programs. The proof idea is first to transform logic programs into equivalent boolean circuits, and then apply a probabilistic method known as random restriction to obtain an exponential lower bound. Based on the main result, we generalize a sufficient condition for identifying hard problems for LP, and give a separation map for a logic program family from a computational point of view, whose members are all equally expressive and share the same reasoning complexity.

A temperature fluctuation suppression control method of fuel cell vehicles to reduce hydrogen consumption

During the driving of the fuel cell vehicles, temperature fluctuations occur in the Proton Exchange Membrane Fuel Cell (PEMFC), whose output efficiency decreases and hydrogen consumption increases. Rule-based (RB) and proportional-integral-derivative (PID) controllers can roughly suppress the temperature fluctuation, but they cannot perform multi-objective optimization, which cannot reduce hydrogen consumption at the same time. To solve this problem, in the first step of this paper, the hydrogen consumption factor and the temperature fluctuation factor in the operating temperature control are introduced to design the DDQN controller. In the second step of this paper, the DDQN controller is trained offline and verified under simulation conditions. The simulation results show that compared with the RB controller and PID controller, the average temperature fluctuation of the DDQN controller is reduced by 27.27 % and 28.50 %, and the hydrogen consumption is saved by 1.78 % and 2.58 %, respectively. The research in this paper is innovative in that it reduces the hydrogen consumption of PEMFC and improves the driving range of fuel cell vehicles from a specific point of view.

Integrating UAV Photogrammetry and Terrestrial Laser Scanning for the 3D surveying of the Fortress of Bashtova

Through the synergistic application of Aerial Photogrammetry using UAVs and Terrestrial Laser Scanning (TLS), this paper investigates how this combination can be used for conducting a 3D survey of the Fortress of Bashtova thereby demonstrating the effectiveness of such integrated methods in acquiring an all-encompassing image of this historical building. As the efforts towards preservation become intense, there arises the urgency of precise and detailed 3D documentation that will facilitate appropriate conservation processes and further studies. Therefore, combining TLS and UAV photogrammetry offers a powerful tool that can provide accurate architectural data for the documentation of heritage areas. Moreover, the TLS component acquires ground point-cloud data with laser scanners giving a complementary alternative for aerial perspective. The merging of these datasets ensures broad inclusion since it allows the production of accurate, detailed three-dimensional models of the Fortress of Bashtova. Thanks to the research on the case study of the Fortress of Bashtova in the article, it can be stated that the integration of data from aerial photogrammetry and TLS is seamless with the help of modern software while respecting the basic photogrammetric-geodetic rules and demonstrates the possibility of creating a complex 3D model, usable for further analyses for architects and conservation professionals, as well as for restorers and civil engineers. To estimate the accuracy of the point clouds derived from TLS and UAV, we compared the distances between the point clouds using CloudCompare software. We obtained a mean RMS of 2.199073 mm and std. dev was 7.356 mm. Research has shown that the difference between point clouds from TLS and UAV is within 1.7 centimeters.

What can we do in Philosophy using Frege’s and Kripke’s logics?

In this article I issue a challenge to philosophers engaged in constructing logical languages. Formal languages that have had a great influence on various areas of philosophy have ineffable statements that arise from metaphysical assumptions, thus limiting what we can do with them. I deal with two cases. The case of Frege known as “The paradox of the concept horse”, and that of Kripke that is not as famous as the Fregean problem, which I call “The necessary bearing of the name.” I briefly develop each case and show that together they give us sufficient evidence of the limits of what we can do with logic when the formal systems employ unexamined metaphysical assumptions.

From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.

From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.

Intelligent Traffic Signal Management with PLC

This study introduces a Smart Traffic Light Control System (STLCS) that uses advanced technologies to improve traffic regulation. The key components include a camera-based traffic density detection module and a specialized vehicle identification system using Radio Frequency (RF) technology. The system’s core is an Arduino Uno board along with several converters and a Programmable Logic Controller (PLC). Field tests show that STLCS effectively analyses traffic density and prioritizes emergency vehicles, proving its potential for global traffic management improvements

Development of a MIMO fuzzy inference system—PI controller for a closed‐circuit grinding ball mill circuit

AbstractThis article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (WF) and speed classifier rotor (VR), and two outputs, a sieve fraction 45 μm (P45) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.

Development of a MIMO fuzzy inference system—PI controller for a closed‐circuit grinding ball mill circuit

AbstractThis article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (WF) and speed classifier rotor (VR), and two outputs, a sieve fraction 45 μm (P45) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.

Axiomatization of Non-Recursive Aggregates in First-Order Answer Set Programming

This paper contributes to the development of theoretical foundations of answer set programming. Groundbreaking work on the SM operator by Ferraris, Lee, and Lifschitz proposed a definition/semantics for logic (answer set) programs based on a syntactic transformation similar to parallel circumscription. That definition radically differed from its predecessors by using classical (second-order) logic and avoiding reference to either grounding or fixpoints. Yet, the work lacked the formalization of crucial and commonly used answer set programming language constructs called aggregates. In this paper, we present a characterization of logic programs with aggregates based on a many-sorted generalization of the SM operator. This characterization introduces new function symbols for aggregate operations and aggregate elements, whose meaning can be fixed by adding appropriate axioms to the result of the SM transformation. We prove that our characterization coincides with the ASP-Core-2 semantics for logic programs and, if we allow non-positive recursion through aggregates, it coincides with the semantics of the answer set solver CLINGO.

Axiomatization of Non-Recursive Aggregates in First-Order Answer Set Programming

This paper contributes to the development of theoretical foundations of answer set programming. Groundbreaking work on the SM operator by Ferraris, Lee, and Lifschitz proposed a definition/semantics for logic (answer set) programs based on a syntactic transformation similar to parallel circumscription. That definition radically differed from its predecessors by using classical (second-order) logic and avoiding reference to either grounding or fixpoints. Yet, the work lacked the formalization of crucial and commonly used answer set programming language constructs called aggregates. In this paper, we present a characterization of logic programs with aggregates based on a many-sorted generalization of the SM operator. This characterization introduces new function symbols for aggregate operations and aggregate elements, whose meaning can be fixed by adding appropriate axioms to the result of the SM transformation. We prove that our characterization coincides with the ASP-Core-2 semantics for logic programs and, if we allow non-positive recursion through aggregates, it coincides with the semantics of the answer set solver CLINGO.

STUDY ON AUTOMATION PROGRAMMING TO MONITOR AND CONTROL A SCREW ELECTRO-COMPRESSOR

The article describes the programming architecture of a programmable logic controller (PLC) and an operator panel that is part of an automation cabinet of a screw electro-compressor. Behind the operation of a screw electro-compressor, there is a programming logic presented in the article that is implemented according to the requirements of each project. The article presents all the states of a screw compressor that are implemented in the PLC by a programmer in the form of software sequences. The article depicts the logic diagrams used to facilitate the software implementation process. Following the software implementation in the PLC of the logic diagrams, the article has a second software that contains a series of screens for the operator panel. The operator panel is a touch-screen type and communicates with the PLC through a defined communication protocol displaying the parameter values in real time on the programmed screens. The software developed for the operator panel by a programmer has the role of facilitating a human operator to monitor parameters and transmit commands to the screw electro-compressor. The article ends by presenting the conclusions that argue the efficiency and safety in operation through the presented software concept of a screw electro-compressor.

A digital twin design and implementation approach for industrial application leveraging programmable logic controllers

A digital twin design and implementation approach for industrial application leveraging programmable logic controllers

Design analysis and development of a gantry robot for multi-layer 3-D concrete printing with simulation and experimental validation

The present study investigates the design analysis and development of a framed gantry robot (FGR) for 3-D concrete printing. Finite element simulations are performed for design validation and parameter estimation to conduct detailed analysis of the concrete printer. The kinematic and dynamic models of FGR are used for workspace analysis, motion control study, and motor sizing for printer development. The motion trajectory of the printer nozzle tip is evaluated using MATLAB simulations. An integrated programmable logic controller (PLC) with an in-built human-machine interface (HMI) through control algorithms has been implemented for interactive, user-friendly, and precise motion control. A separate control system is developed to monitor the pumping power and feed rate of mortar mix during printing. The simulated control scheme is validated experimentally using trajectory tracking for various input printing paths through HMI, with a maximum tracking error of 1 mm. Successful concrete printing trials have been conducted using various mix-design, following extensive evaluation of different printer and material parameters at the laboratory scale. The proposed design concept, mechanisms, interactive control interface, and tracking accuracy have been validated through simulation and experimental results, for ensuring quality concrete prints by the developed printer. Material testing parameters have been evaluated experimentally for modeling various mix-designs. To further evaluate printing performance, simulations of layer deformation in printed structures are conducted using finite element analysis, incorporating non-linear material models and various contact layer interactions. The simulated results of printed layers of different shapes of structures show a close resemblance with the experimental concrete printing trials.

Challenges for Non-Classical Reasoning in Contemporary AI Applications

AbstractIn knowledge representation and reasoning, a key area in artificial intelligence research, non-classical logics play a prominent double role: firstly, non-classical logic languages allow for a precise and transparent encoding of domain specific knowledge. Secondly, as the logical languages are equipped with custom-tailored rules of logical inference, they make available a principled approach to derive new knowledge from previous information. In practice, the first aspect addresses data storage and retrieval, the second aspect the utilization of available information. This article briefly surveys contemporary challenges of NCL research in AI.

Rule learning by modularity

AbstractIn this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with well-established methods in inductive logic programming (ILP) and rule induction to provide efficient and scalable algorithms for the classification of vast data sets. By construction, these classifications are based on the synthesis of simple rules, thus providing direct explanations of the obtained classifications. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherung which is an insurance company offering diverse services in Germany.