Deriving explanations from partial temporal information

The representation and manipulation of natural human understanding of temporal phenomena is a fundamental field of study in Computer Science, which aims both to emulate human thinking, and to use the methods of human intelligence to underpin engineering solutions. In particular, in the domain of Artificial Intelligence, temporal knowledge may be uncertain and incomplete due to the unavailability of complete and absolute temporal information. This paper introduces an inferential framework for deriving logical explanations from partial temporal information. Based on a graphical representation which allows expression of both absolute and relative temporal knowledge in incomplete forms, the system can deliver a verdict to the question if a given set of statements is temporally consistent or not, and provide understandable logical explanation of analysis by simplified contradiction and rule based reasoning

Constitutive metaphor and mental mappings: meaning construction in the language of science and technology

The study of scientific and technical language, where metaphor is central, is enriched by the cognitive linguistics approach. This paper is based on the research project that culminated in the Bilingual Dictionary of Scientific and Technical Metaphors and Metonymies, developed to depict metaphorical terms and mental mappings, thus unfolding cognitive metaphors in science and technology. Although terminological metaphors as a whole exceed widely in number those that constitute part of conceptual metaphors, the importance of the latter radicates in its relevance as a constitutive element of scientific thought and language. Focusing on the analysis of metaphorical terms from earth sciences, agronomy, and mechanical engineering, the study reveals the presence of several conceptual metaphors, typified as ‘humanizing’, ‘organicist’, and ‘objectual’, according to their source domain’s nature. The work presents some very productive conceptual metaphorical patterns found in knowledge representation in engineering, in English and in Spanish, and shows evidence that metaphorical reasoning is a mechanism present at the core of creative scientific development albeit certain socio-cultural variations. This contribution opens a door for further research on the role of metaphor in constructing meaning within all branches of science and technology, as well as on the study of knowledge representation variations in different languages and cultures

Maturity and Future of Artificial Intelligence

Artificial Intelligence (AI) is one of the most important technology in the world today. She has completely matured in the end of 20 century and revolutionized the 21st century. In general, the field of artificial intelligence seeks to advance the science and engineering of intelligence, with the goal of creating machines with human-like characteristics. This includes developing machines with a wide range of human-inspired capabilities, including communication, perception, planning, reasoning, knowledge representation, the ability to move and manipulate objects, and learning. AI approaches problems using tools and techniques from a wide variety of other fields, including probability and statistics, symbolic computation, search and optimization, game theory, information engineering, mathematics, psychology, linguistics, and philosophy. Throughout this paper we will develop all concepts behind IA maturity and how it will impact our future daily live. A case of how will this technology affect our justice and education in the future? Will be provided

Utilization of the MVL system in qualitative reasoning about the physical world

Ankara : Department of Computer Engineering and Information Science and Institute of Engineering and Science, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 60-63An experimental progra.m, QRM, has been implemented using the inference mechanism of the Multivalued Logics (MVL) Theorem Proving System of Matthew Ginsberg. QRM has suitable facilities to reason about dynamical systems in qualitative terms. It uses Kenneth Forbus’s Qualitative Process Theory (QPT) to describe a physical system and constructs the envisionment tree for a given initial situation. In this thesis, we concentrate on knowledge representation issues, and basic qualitative reasoning tasks based on QPT. We offer some insights about what MVL can provide for writing Qualitative Physics programs.Şencan, Mine ÜlküM.S

A neural-symbolic system for temporal reasoning with application to model verification and learning

The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies. An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour. In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role

Learning common sense knowledge from user interaction and principal component analysis

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 107-110).In this thesis, I present a system for reasoning with common sense knowledge in multiple natural languages, as part of the Open Mind Common Sense project. The knowledge that Open Mind collects from volunteer contributors is represented as a semantic network called ConceptNet. Using principal component analysis on the graph structure of ConceptNet yields AnalogySpace, a vector space representation of common sense knowledge. This representation reveals large-scale patterns in the data, while smoothing over noise, and predicts new knowledge that the database should contain. The inferred knowledge, which a user survey shows is often correct, is used as part of a feedback loop that shows contributors what the system is learning and guides them to contribute useful new knowledge.by Robert Speer.M.Eng

Spatial Thinking in the Engineering Curriculum: an Investigation of the Relationship Between Problem Solving and Spatial Skills Among Engineering Students.

Long considered a primary factor of intelligence, spatial ability has been shown to correlate strongly with success in engineering education, yet is rarely included as a learning outcome in engineering programmes. A clearer understanding of how and why spatial ability impacts on performance in science, technology, engineering and mathematics (STEM) subjects would allow educators to determine if spatial skills development merits greater priority in STEM curricula. The aim of this study is to help inform that debate by shedding new light on the role of spatial thinking in STEM learning and allow teaching practice and curriculum design to be informed by evidence based research. A cross cutting theme in STEM education – problem solving – is examined with respect to its relationship with spatial ability. Several research questions were addressed that related to the role and relevance of spatial ability to first year engineering education and, more specifically, the manner in which spatial ability is manifest in the representation and solution of word story problems in mathematics. Working with samples of engineering students in Ireland and the United States, data were collected in the form of responses to spatial ability tests and problem solving exercises in the areas of mathematics and electric circuits. Following a pilot study to select and refine a set of mathematical story problems a mixed methods design was followed in which data were first analysed using quantitative methods to highlight phenomena that were then explored using an interpretive approach. With regard to engineering education in general, it was found that spatial ability cannot be assumed to improve as students progress through an engineering programme and that spatial ability is highly relevant to assessments that require reasoning about concepts, novel scenarios and problems but can remain hidden in overall course grades possibly due to an emphasis on assessing rote learning. With regard to problem solving, spatial ability was found to have a significant relationship with the problem representation step but not with the solution step. Those with high levels of spatial ability were more able to apply linguistic and schematic knowledge to the problem representation phase which led to higher success rates in translating word statements to mathematical form

Design and implementation of an object-oriented expert system shell

Ankara : The Department of Computer Engineering and Information Sciences and the Institute of Engineering and Science of Bilkent Univ. , 1989.Thesis (Master's) -- Bilkent University, 1989.Includes bibliographical references leaves 50-53.Expert systems represent a new opportunity in computing. An expert system is a computing system capable of representing and reasoning about some knowledge-rich domain with a view to solving problems and giving advice. Expert system shells are developed to create expert systems in an easy way. In recent years the object-oriented paradigm has been developed. The objectoriented approach has many advantages such as data abstraction, program modularity, and structural data representation. Therefore, we are developing an expert system shell which stores knowledge and data in object-oriented style. Also, an object-oriented DBMS part of our shell satisfy the needs of several expert systems rec|uiring large base of fcvcts. Such shells can be used to build expert systems by only adding the domain-specific knowledge.Toroslu, İsmail HakkıM.S

Proactive Independent Learning Approach: A case study in computer arithmetic

The rapid growth of knowledge and scientific challenges required lifelong continuous education in computational science and engineering. Computer numerical system representation and computer arithmetic are the basis of numerical computing of scientific models. In this work an adapted student centered and problem based learning strategy is presented. Development of problem solving, effective self directed reasoning and communication skills are promoted. A pilot study was conducted to determine the validity of the proposed alternatives. The study aimed to evaluate the performance of students to solve new problems and effectively describe the problems, the theoretical context and the possible solutions. Preliminary results are presented for a particular population from which the sample is actually drawn.VI Workshop de Innovación en Educación en Informática (WIEI).Red de Universidades con Carreras en Informática (RedUNCI