Information, knowledge and the context of interaction

Representing knowledge as information content alone is insufficient in providing us with an understanding of the world around us. A combination of context as well as reasoning of the information content is fundamental to representing knowledge, within information –based systems. The field of knowledge representation and knowledge management has thus far been concerned with providing structures and theories that can lead to some form of qualified intelligent reasoning, and contextualised information. By drawing upon previous research and applying and extending concepts of Semiotics and Symbiosis from the interaction design school of thought, this paper presents a conceptual framework for establishing the interplay between knowledge and users of knowledge via information systems constructs. Subsequently, by drawing upon notions of interfaces to knowledge, a conceptual framework which describes the relationship between the semiotic, symbiotic and interface to knowledge presented, along with a discussion of contemporary issues common to the field of knowledge management is developed

2Planning for Contingencies: A Decision-based Approach

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to the problems of contingent planning, and provides a solid base for extensions such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file

Some reflections on knowledge representation in the semantic web

The knowledge representation technology Description Logics (DLs) has become an important component of developments around the Semantic Web. It is suggested here that in order to be really useful, the knowledge represented in DLs should in some fundamental way reflect the way the human mind organises and structures the same knowledge. There is a short historical review of some relevant background work in cognitive psychology, including WordNet. This is followed by a brief introduction to the importance of automatic classification in DLs before considering some issues around ontologies

Geometric reasoning

Cognitive robot systems are ones in which sensing and representation occur, from which task plans and tactics are determined. Such a robot system accomplishes a task after being told what to do, but determines for itself how to do it. Cognition is required when the work environment is uncontrolled, when contingencies are prevalent, or when task complexity is large; it is useful in any robotic mission. A number of distinguishing features can be associated with cognitive robotics, and one emphasized here is the role of artificial intelligence in knowledge representation and in planning. While space telerobotics may elude some of the problems driving cognitive robotics, it shares many of the same demands, and it can be assumed that capabilities developed for cognitive robotics can be employed advantageously for telerobotics in general. The top level problem is task planning, and it is appropriate to introduce a hierarchical view of control. Presented with certain mission objectives, the system must generate plans (typically) at the strategic, tactical, and reflexive levels. The structure by which knowledge is used to construct and update these plans endows the system with its cognitive attributes, and with the ability to deal with contingencies, changes, unknowns, and so on. Issues of representation and reasoning which are absolutely fundamental to robot manipulation, decisions based upon geometry, are discussed here, not AI task planning per se

SCIENTIFIC TEXT IN THE GLOBAL SCIENTIFIC DISCOURSE OF THE 21ST CENTURY

The review of the monograph by V. E. Chernyavskaya Scientific Discourse: Representation of Results as Communicative and Linguistic Problem (Moscow, 2017) states that the most important task of this book lies in attracting attention of research community to communication problems in science in the 21st century. The review’s authors examine a range of issues that are of current importance to modern stylistics of scientific language. These include science in epistemic and socio-cultural contexts, commercialization of science, imbalance between fundamental and applied researches, forms of knowledge spread, structure of modern scientific text etc. The reviewers emphasize a high topicality, social and scientific importance of the monograph as its author analyzes a combination of factors influencing perception and evaluation of knowledge in the modern information society as well as proposes a reference text structure contributing to successful promotion of scientific results. According to the reviewers’ opinion, the monograph meets the need to conceive the global scientific discourse and pay attention to the issues of representation, perception and evaluation of scientific knowledge. The review also considers some debatable issues of modern scientific communication, namely selection of a language for publication, difficulties in promoting knowledge in the humanities, contradictions between a perception subject and institutional factors that impede productive cognitive activity etc. The review underlines the unanimity of Professor V. E. Chernyavskaya’s views and the views of Perm School of Functional Stylistics on the structure of scientific text and its role in the development of science

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Design approaches in technology enhanced learning

Design is a critical to the successful development of any interactive learning environment (ILE). Moreover, in technology enhanced learning (TEL), the design process requires input from many diverse areas of expertise. As such, anyone undertaking tool development is required to directly address the design challenge from multiple perspectives. We provide a motivation and rationale for design approaches for learning technologies that draws upon Simon's seminal proposition of Design Science (Simon, 1969). We then review the application of Design Experiments (Brown, 1992) and Design Patterns (Alexander et al., 1977) and argue that a patterns approach has the potential to address many of the critical challenges faced by learning technologists

Kant: constitutivism as capacities-first philosophy

Over the last two decades, Kant’s name has become closely associated with the “constitutivist” program within metaethics. But is Kant best read as pursuing a constitutivist approach to meta- normative questions? And if so, in what sense? In this essay, I’ll argue that we can best answer these questions by considering them in the context of a broader issue – namely, how Kant understands the proper methodology for philosophy in general. The result of this investigation will be that, while Kant can indeed be read as a sort of constitutivist, his constitutivism is ultimately just one instance of a much more general approach to philosophy – which treats as fundamental our basic, self-conscious rational capacities. Thus, to truly understand why and how Kant is a constitutivist, we need to consider this question within the context of his more fundamental commitment to “capacities-first philosophy”