An Effective Combination of PLC and Microcontrollers for Centralized Traffic Control and Monitoring System

In this paper, a smart and centralized traffic light control and monitoring system is proposed to control the modern transportation systems and make the city safer, using programmable logic controllers (PLCs) and programmable electronic microcontrollers. A camera is used to monitor the mishaps during the traffic flow of vehicles. The system has four modes, i.e., auto-control mode (ACM), manual control mode (MCM), central control mode (CCM), and remote control mode (RCM). In the auto-control mode (ACM), the traffic light signals are controlled automatically through programmable electronic microcontrollers at specific times, while the manual control mode (MCM) controls the traffic light signals manually (on–off switches) according to the traffic congestion. The central control mode (CCM) is considered to be a centralized mode, where the programmable logic controller (PLC) is used by a computer workstation. In this mode, the traffic light signals are controlled by a ladder logic program of the PLC. The third mode, RCM, is linked with the second mode, CCM; in this mode, the traffic light signals are remotely controlled through the software by transferring programmable logic controller (PLC) functions to the software interface. As a result, this transportation system can also be controlled remotely. The designed system delivers suitable, flexible, and reliable control for traffic signaling and transportation.

A relational tsetlin machine with applications to natural language understanding

Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

An Asymptotic Analysis of Probabilistic Logic Programming, with Implications for Expressing Projective Families of Distributions

Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.

FUZZY LOGIC PROGRAM FOR BUS INDOOR ENVIRONMENTAL ASSESSMENT

This paper proposes a fuzzy logic algorithm that evaluates the indoor environmental conditions on an urban bus specifically in Metro Manila. This algorithm identifies the value of three indexes: IAQI, TCI, and CO2. The Indoor Air Quality Index (IAQI) quantifies the level of indoor air quality of the bus. CO2, CO, NO2, O3, TVOC, and PM10 are the input parameters for the fuzzy logic system that will determine IAQI. Thermal Comfort Index (TCI) quantifies the indoor thermal condition in four levels. The indoor temperature and humidity are the input parameters for the fuzzy logic system that will determine TCI. The fuzzy logic program in this study is designed mainly for the bus ventilation control system. The created FLS program was able to give good results. The observations from the program were the following: as the indoor air pollutants increased, the IAQI decreased; as the level of thermal parameters increased, the TCI decreased; and as the CO2 level and temperature increased, the number of passengers also increased.

On Correctness and Completeness of an n Queens Program

Thom Frühwirth presented a short, elegant, and efficient Prolog program for the n queens problem. However, the program may be seen as rather tricky and one may not be convinced about its correctness. This paper explains the program in a declarative way and provides proofs of its correctness and completeness. The specification and the proofs are declarative, that is they abstract from any operational semantics. The specification is approximate, it is unnecessary to describe the program’s semantics exactly. Despite the program works on non-ground terms, this work employs the standard semantics, based on logical consequence and Herbrand interpretations. Another purpose of the paper is to present an example of precise declarative reasoning about the semantics of a logic program.

Solution Enumeration by Optimality in Answer Set Programming

Given a combinatorial search problem, it may be highly useful to enumerate its (all) solutions besides just finding one solution, or showing that none exists. The same can be stated about optimal solutions if an objective function is provided. This work goes beyond the bare enumeration of optimal solutions and addresses the computational task of solution enumeration by optimality (SEO). This task is studied in the context of answer set programming (ASP) where (optimal) solutions of a problem are captured with the answer sets of a logic program encoding the problem. Existing answer set solvers already support the enumeration of all (optimal) answer sets. However, in this work, we generalize the enumeration of optimal answer sets beyond strictly optimal ones, giving rise to the idea of answer set enumeration in the order of optimality (ASEO). This approach is applicable up to the best k answer sets or in an unlimited setting, which amounts to a process of sorting answer sets based on the objective function. As the main contribution of this work, we present the first general algorithms for the aforementioned tasks of answer set enumeration. Moreover, we illustrate the potential use cases of ASEO. First, we study how efficiently access to the next-best solutions can be achieved in a number of optimization problems that have been formalized and solved in ASP. Second, we show that ASEO provides us with an effective sampling technique for Bayesian networks.

Fuzzy Logic Implementation for Accurate Electric Car Battery SoC Measurement

Changes in temperature can affect the accuracy of the estimated SoC value based on voltage. In this study, fuzzy logic was implemented to correct the SoC estimation error caused by the influence of temperature. The system acquired data through sensors and then processed it using the Arduino microcontroller. Parameters in the form of voltage, temperature, and current were processed by Arduino with a fuzzy logic program which was uploaded into it and produced the output of the estimated SoC value. From the observations, it was found that the estimated SoC value from this method had better accuracy with a smaller error than the SoC estimation based on voltage alone. Using the RMSE method, the errors calculated in this method in the process of charging and discharging without running were 2.26 and 7.74, while the SoC estimation error based on voltage alone reached 4.88 and 12.8. In the discharging process with a running car, the SoC estimation results using fuzzy logic also showed accurate results. There was only 1% of SoC value increasing pattern during the discharging process, which the value trend should continue to decrease and should not be an increase. In addition, compared to the previous method applied to the same research object, namely the chemical equilibrium constant method, this method also showed more accurate results.

Defining the Semantics of Abstract Argumentation Frameworks through Logic Programs and Partial Stable Models (Extended Abstract)

Extensions of Dung’s Argumentation Framework (AF) include the class of Recursive Bipolar AFs (Rec-BAFs), i.e. AFs with recursive attacks and supports. We show that a Rec-BAF \Delta can be translated into a logic program P_\Delta so that the extensions of \Delta under different semantics coincide with subsets of the partial stable models of P_\Delta.