Ebook: Advances in Technological Applications of Logical and Intelligent Systems

Logic began as the science of valid inference and related topics. It gradually underwent profound changes, widening its initial scope and transforming itself into a mathematical discipline. Today it is a basic science, full of significant concepts and involved results (Goedel's theorems, the theory of forcing, forking, the mathematics of Solovay, etc.) but its main value has always been theoretical.

However, in the twentieth century, logic finally found a number of important applications and originated various new areas of research, especially after the development of computing and the progress of the correlated domains of knowledge (artificial intelligence, robotics, automata, logical programming, hyper-computation, etc.). This happened not only in the field of classical logics, but also in the general field of non classical logics. This reveals an interesting trait of the history of logic: despite its theoretical character, it constitutes, at present, an extraordinarily important tool in all domains of knowledge, in the same way as philosophy, mathematics, natural science, the humanities and technology. Moreover, certain new logics were inspired by the needs of specific areas of knowledge, and various new techniques and methods have been created, in part influenced and guided by logical views.

This book contains papers on relevant technological applications of logical methods and some of their extensions, including: annotated logic and expert systems, fuzzy dynamical models, adaptive devices, intelligent automaton vehicles, cellular automata, information systems and temporal logic, paraconsistent robotics, dynamic virtual environments and multiobjective evolutionary search, cable routing problems, and reinforcement of learning. All papers are well summarized in their abstracts.

This collection of papers gives a clear idea of some current applications of logical (and similar) methods to numerous problems, including relevant new concepts and results, in particular those related to paraconsistent logic. It will be of interest to a wide audience: pure logicians, applied logicians, mathematicians, philosophers and engineers.

September, 2008

Newton C.A. da Costa

This paper focuses on the problem of providing a well founded framework to make possible the reverse engineering on algebraic specifications. The paper considers existing and well-accepted formalizations for operations to compose specifications and propose a categorical formalization to operations to break specifications. The concept of categorical limit is adopted, and categorical equalizers are proposed to formalize operations to hide components of specifications. The proposed formalizations complete an algebraic framework that makes possible the manipulation of specifications, composing and decomposing old specifications in order to obtain a better use of existing specification.

Adaptive devices were introduced formally in [7] as a generalization of the adaptive automaton [6]. The semantics were defined by an entirely traditional operational way, based on automaton transitions. This paper starts another path, to describe the semantics of adaptive devices using structural operational semantics [9, 12], λ-calculus [1, 2] and a typing system based on the algorithm W of Damas and Milner [3, 8]. The results achieved showed that adaptive devices are complex to describe, and that a sound and complete typing system should be used to fully analyze them.

This chapter presents a revision of logic temporal, where the potentialities and used in temporal logic in information systems are presented, mainly what refers to its development. The purpose of this work is to show how Temporal logic is applied to an Information System besides introduction an elementary introduction of Modal Logic, Kripke Semantics, the features of Temporal Logic and it use in some areas of Computer Science.

In this paper, we present new results concerning the heuristic optimization of cable routing in electrical panels. The problem is modeled and a heuristic solution, using an insertion algorithm and a modified version of the Dijkstra's algorithm, is proposed, analyzed, and compared with human-made solutions. Tests have shown that good results can be obtained from layouts commonly found in the industry.

The animation problem of avatars or virtual creatures using learning, involves research areas such as Robotics, Artificial Intelligence, Computer Sciences, Virtual Reality, among others. This work presents a Machine Learning approach using Reinforcement Learning and a Knowledge Base for animating avatars. This Knowledge Base (ontology) provides the avatar with semantic definition and necessary awareness of its internal structure (skeleton), its behavior (personality, emotions and moods), its learned skills, and also of the rules that govern its environment. In order to animate and control the behavior of these virtual creatures in 3D Distributed Dynamic Virtual Environments, we use Knowledge-Based Conscious and Affective Personified Emotional Agents as a type of logical agents, within the GeDA-3D Agent Architecture. We focus on the definition of minimum conscience of the avatars. The conscience and cognitive processes of the avatars allow them to solve the animation and behavior problems in a more natural way. An avatar needs to have minimum conscience for computing the autonomous animation. In our approach, the avatar uses the Knowledge Base first as a part of its conscience, and second to implement a set of algorithms that constitute its cognitive knowledge.

This paper presents a general view from the Two-Valued Annotated Paraconsistent Logic – 2vAPL to the Four-Valued Annotated Paraconsistent Logic – 4vAPL. The purpose to expand 2vAPL to 4vAPL is to enable the insertion of opinions from Experts in the knowledge base, so that the problems described approach their real condition, once the opinion of an Expert may be decisive in the evaluation of a system. Furthermore, with the expansion of 2vAPL to 4vAPL, it is possible to analyze the behavioral evolution of the opinions from these Experts within time.

This article deals with the problem of Declarative Modeling of scenarios in 3D. The innovation presented in this work consists in the use of a knowledge base to aid the parsing of the declarative language. The example described in this paper shows that this strategy reduces the complexity of semantic analysis, which is rather time- and resource-consuming. The results of our work confirm that this contribution is useful mainly when the proposed language constructions have high complexity.

A strong motivation for studying cellular automata (CA) is their ability to perform computations. However, the understanding of how these computations are carried out is still extremely vague, which makes the inverse problem of automatically designing CA rules with a predefined computational ability a fledgeling engineering endeavour. Various studies have been undertaken on methods to make CA design possible, one of them being the use of evolutionary computational techniques for searching the space of possible CA rules. A widely studied CA task is the Density Classification Task (DCT). For this and other tasks, it has recently been shown that the use of a heuristic guided by parameters that estimate the dynamic behaviour of CA can improve a standard evolutionary design. Considering the successful application of evolutionary multiobjective optimisation to several kinds of inverse problems, here one such technique known as Non-Dominated Sorting Genetic Algorithm is combined with the parameter-based heuristic, in the design of DCT rules. This is carried out in various alternative ways, yielding evolutionary searches with various numbers of objectives, of distinct qualities. With this exploration, it is shown that the resulting design scheme can effectively improve the search efficacy and obtain rules that solve the DCT with sophisticated strategies.

In this work we presented an Expert System built with Paraconsistent Logic applied in a transmission electrical power system operation support in real time. The computational program forms a Paraconsistent Expert System PES capable to offer a risk analyses, diagnosis and the optimal restorative strategy proposition to the electrical power transmission system after an outage. The logic used for the PES to make decisions is the Annotated Paraconsistent Logic (APL) that belongs to a class of the Non-classic Logical denominated of Paraconsistent Logic-PL. This Paraconsistent Expert System PES uses a type of the Annotated Paraconsistent logic denominated Annotated Paraconsistent logic with annotation of two values APL2v to produce diagnosis suggesting the restorative strategy based in the analysis of occurrence information (electric Switches, Circuit breakers, protections, etc…). The use of APL brings certain advantages in comparison with the classic logic because allow to manipulate contradictory signals, and like this presenting a faster and reliable action for make decision in case of the reception of vague, ambiguous and inconsistent information. The results demonstrate that the Paraconsistent Logic, with their algorithms extracted of APL2v methodology, also opens a wide field for researches and developments and can be used with promising results for implementations of applied Expert Systems in Electric Power Systems re-establishment at different topologies.

In this paper we present a fuzzy approach to the Reed-Frost model for epidemic spreading taking into account uncertainties in the diagnostic of the infection. The heterogeneities in the infected group is based on the clinical signals of the individuals (symptoms, laboratorial exams, medical findings, etc.), which are incorporated to the dynamic of the epidemic. The infectivity level is time-varying and the classification of the individuals is performed through fuzzy relations. Simulations considering a real problem data of the influenza epidemic in the baby daycare are performed and the results are compared with a stochastic Reed-Frost generalization developed by the authors in a previous work.

Loss reduction in electric energy distribution systems can be considered as a hidden source of energy. These systems operate with a radial configuration. The problem of obtaining a topology with minimum losses can be seen as a generalization of minimum spanning tree problem. The paper discusses representations for the problem of loss reduction through reconfiguration of the network. Models with fixed and variable demands are considered. Solution techniques to find good solutions in adequate computational time to the models proposed in the paper are discussed. A reduced model of a distribution network is used to show important aspects of the problem, under fixed and variable demand representations.

Intelligent or Autonomous Vehicles are not visionary technologies that may be present in a far away future. From high tech military unmanned systems and automatic reverse parallel parking to cruise control and Antilock Brake Systems (ABS), these technologies are becoming embedded in people's daily life. The methodologies applied in these applications vary widely across areas, ranging from new hardware development to complex software algorithms. The purpose of this work is to present a survey about the benefits and applications of autonomous ground vehicles and to propose a new learning methodology, which provides the vehicle the capacity of learning maneuvers with a human driver. Four main areas will be discussed: system's topology, instrumentation, high level control, and computational vision. Section 1 will present the most common architecture of autonomous vehicles. The instrumentation used in intelligent vehicles such as differential global positioning system (DGPS), inertial navigation system (INS), radar, ladar and infrared sensor will be described in section 2, as well as the techniques used for simultaneous registration and fusion of multiple sensors. Section 3 presents an overview of some techniques and methods used for visual control in autonomous ground vehicles. Section 4 will describe the most efficient techniques for autonomous driving and parking, collision avoidance and cooperative driving. Finally, section 5 will propose a new algorithm based on Artificial Immune Systems, where a fuzzy system for autonomous maneuvering will be learnt by a data set of actions taken by a human driver. In order to validate the proposed method, the results of its application in an automatic parallel parking maneuver will be showed.

This work presents some improvements regarding to the autonomous mobile robot Emmy based on Paraconsistent Annotated Evidential Logic Eτ. A discussion on navigation system is presented.

This paper presents new methodologies aimed at improving the design of distribution systems from the electrical, the mechanical and the economics viewpoints. The methodologies have been implemented as software solutions fully integrated with AES ELETROPAULO's GIS system.

The paper proposes an analytical methodology and three different Artificial Neural Network alternatives using a multi layer perceptron (MLP) in order to estimate technical losses in distribution systems. For each transformer a load curve is set by aggregated customer load curves, and then losses are estimated by transformer's daily load curve stratified, with the proposed methodologies. Theoretically, it can reach a different load curve for each transformer then consequently distinguished losses either. The losses are estimated for distribution transformer, but the methodology can be used on each segment involved in the distribution system (secondary and primary network, distribution transformers, HV/MV transformers). This is done by using the network's data, the consumer's monthly energy consumption data and the typical load curves by class of consumer and type of enterprise developed.