The problems related to construction production are multi-faceted and complex. This has promoted the search for different methods/approaches for analizing the data which supports the decision-making process in the construction industry. In the article the authors focus their attention on well-known methods and tools, and on some new approaches to solving decision-making problems. The aim of the article is to analyze the methods used to analyse data in a construction company, convey their advantages and disadvantages, and specify the degree of efficiency in the discussed area.
The article presents the use of the Mamdani fuzzy reasoning model to develop a proposal of a system controlling partnering relations in construction projects. The system input variables include: current assessments of particular partnering relation parameters, the weights of these parameters’ impact on time, cost, quality and safety of implementation of construction projects, as well as the importance of these project assessment criteria for its manager. For each of the partnering relation parameters, the project’s manager will receive controlrecommendations. Moreover, the parameter to be improved first will be indicated. The article contains a calculation example of the system’s operations.
The main goal of this article is to characterise and compare some aspects of Hilary Putnam’s referential theory of meaning and Robert B. Brandom’s inferential theory of meaning. I will do it to indicate some similarities and differences in these theories. It will provide an opportunity for a deeper understanding of these theories and for a more adequate evaluation of how they describe and explain the process of meaning acquisition of linguistic expressions. In his theory of meaning Putnam emphasises the importance of reference understood as a relationship which connects linguistic expressions and extra-linguistic (empirical) reality. Brandom acknowledges inference as a main category useful in characterising the meaning of expressions used in premises and a conclusion of inference. But his theory of meaning is criticised for minimalising the role of an empirical component (demonstratives etc.). He tries to defend his standpoint in the anaphoric theory of reference. Putnam like Brandom claimed that we – as cognitive subjects – are not in a situation in which we learn about the extra-linguistic reality in a direct way. It is the reality itself as well as our cognitive apparatus that play a role in a cognitive process.
Reasoning with limited computational resources (such as time or memory) is an important problem, in particular in knowledge-intensive embedded systems. Classical logic is usually considered inappropriate for this purpose as no guarantees regarding deadlines can be made. One of the more interesting approaches to address this problem is built around the concept of active logics. Although a step in the right direction, active logics are just a preliminary attempt towards finding an acceptable solution. Our work is based on the assumption that labelled deductive systems (LDSs) o#27;er appropriate metamathematical methodology to study the problem. As a first step, we have reformulated a pair of active logics systems, namely the memory model and its formalized simplification, the step logic, as LDSs. This paper presents our motivation behind this project, followed by an overview of the investigations on meta-reasoning relevant to this work, and introduces in some reasonable detail the MM system.
The ability of case-based reasoning systems to solve new problems mainly depends on their case adaptation knowledge and adaptation strategies. In order to carry out a successful case adaptation in our case-based reasoning system for a low frequency electromagnetic device design, we make use of semantic networks to organize related domain knowledge, and then construct a rule-based inference system which is based on the network. Furthermore, based on the inference system, a novel adaptation algorithm is proposed to derive a new device case from a real-world induction motor case-base with high dimensionality.
In order to explore creativity in design, a computational model based on Case-Based Reasoning (CBR) (an approach to employing old experiences to solve new problems) and other soft computing techniques from machine learning, is proposed in this paper. The new model is able to address the four challenging issues: generation of a design prototype from incomplete requirements, judgment and improvement of system performance given a sparse initial case base library, extraction of critical features from a given feature space, adaptation of retrieved previous solutions to similar problems for deriving a solution to a given design task. The core principle within this model is that different knowledge from various level cases can be explicitly explored and integrated into a practical design process. In order to demonstrate the practical significance of our presented computational model, a case-based design system for EM devices, which is capable of deriving a new design prototype from a real-world device case base with high dimensionality, has been developed.
This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use of methods which process the information based on the experience and observations of a technologist responsible for the inspection of ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (CaseBased Reasoning) methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation phase, but also provides the system with an opportunity to "learn" by collecting new cases solved earlier by this system. The authors present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for similarities between the cases observed and cases stored in the knowledge base.
This work presents the project of the application of Case-based reasoning (CBR) methodology to an advisory system. This system should give an assistance by selection of proper alloying additives in order to obtain a material with predetermined mechanical properties. The considered material is silumin EN AC-46000 (hypoeutectic Al-Si alloy) that is modified by the addition of Cr, Mo, V and W elements in the range from 0% to 0.5% in the modified alloy. The projected system should indicate to the user the content of particular additives so that the obtained material is in the chosen range of parameters: tensile strength Rm, yield strength Rp0.2, elongation A and hardness HB. The CBR methodology solves new problems basing on the solutions of similar problems resolved in the past. The advantage of the CBR application is that the advisory system increases knowledge base as the subsequent use of the system. The presented design of the advisory system also considers issues related to the ergonomics of its operation.
The main scope of the article is the development of a computer system, which should give advices at problem of cooper alloys manufacturing. This problem relates with choosing of an appropriate type of bronze (e.g. the BA 1044 bronze) with possible modification (e.g. calcium carbide modifications: Ca + C or CaC2) and possible heat treatment operations (quenching, tempering) in order to obtain desired mechanical properties of manufactured material described by tensile strength - Rm, yield strength - Rp0.2 and elongation - A5. By construction of the computer system being the goal of presented here work Case-based Reasoning is proposed to be used. Case-based Reasoning is the methodology within Artificial Intelligence techniques, which enables solving new problems basing on experiences that are solutions obtained in the past. Case-based Reasoning also enables incremental learning, because every new experience is retained each time in order to be available for future processes of problem solving. Proposed by the developed system solution can be used by a technologist as a rough solution for cooper alloys manufacturing problem, which requires further tests in order to confirm it correctness.
There are different meanings and functions of what is called a “general principle of law.” This article seeks to address their importance as the basis for the systemic integration of the international legal order. When international law is considered as a legal system, its normative unity and completeness seems essential. This article argues that general principles of law are a necessary, although less visible, element of international legal practice and reasoning, which secure the systemic integration and long-lasting underpinnings of international law. In this sense they may be seen as the gentle guardians of international law as a legal system.