Likely and Unlikely Events in International Security Affairs: An Example from the People\u27s Republic of China

This article describes a human judgment shortfall in international security decision making based on statistical probabilities

Aspects of dealing with imperfect data in temporal databases

In reality, some objects or concepts have properties with a time-variant or time-related nature. Modelling these kinds of objects or concepts in a (relational) database schema is possible, but time-variant and time-related attributes have an impact on the consistency of the entire database. Therefore, temporal database models have been proposed to deal with this. Time itself can be at the source of imprecision, vagueness and uncertainty, since existing time measuring devices are inherently imperfect. Accordingly, human beings manage time using temporal indications and temporal notions, which may contain imprecision, vagueness and uncertainty. However, the imperfection in human-used temporal indications is supported by human interpretation, whereas information systems need extraordinary support for this. Several proposals for dealing with such imperfections when modelling temporal aspects exist. Some of these proposals consider the basis of the system to be the conversion of the specificity of temporal notions between used temporal expressions. Other proposals consider the temporal indications in the used temporal expressions to be the source of imperfection. In this chapter, an overview is given, concerning the basic concepts and issues related to the modelling of time as such or in (relational) database models and the imperfections that may arise during or as a result of this modelling. Next to this, a novel and currently researched technique for handling some of these imperfections is presented

Treatment of imprecision in data repositories with the aid of KNOLAP

Traditional data repositories introduced for the needs of business processing, typically focus on the storage and querying of crisp domains of data. As a result, current commercial data repositories have no facilities for either storing or querying imprecise/ approximate data. No significant attempt has been made for a generic and applicationindependent representation of value imprecision mainly as a property of axes of analysis and also as part of dynamic environment, where potential users may wish to define their “own” axes of analysis for querying either precise or imprecise facts. In such cases, measured values and facts are characterised by descriptive values drawn from a number of dimensions, whereas values of a dimension are organised as hierarchical levels. A solution named H-IFS is presented that allows the representation of flexible hierarchies as part of the dimension structures. An extended multidimensional model named IF-Cube is put forward, which allows the representation of imprecision in facts and dimensions and answering of queries based on imprecise hierarchical preferences. Based on the H-IFS and IF-Cube concepts, a post relational OLAP environment is delivered, the implementation of which is DBMS independent and its performance solely dependent on the underlying DBMS engine

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

The development of Spatial Intelligent Agents With Geographic Information Systems

The manipulation of geographic information (GI) has been considered, by most of its users, as a very complex process and has involved the development of specific applications called geographic information systems (GIS). A new subdiscipline of Information Science called Geographic Information Science (GISc) has been proposed by the GIS research community on the grounds that there are specific characteristics not only to GI but also to the processes involved in its manipulation. The thesis draws on several research issues which are part of the agenda in GISc: The complexity of handling spatial/geographic information, spatial reasoning in dynamical systems, integration of several types of application, human-computer interaction and spatio-temporal issues. In this context, this dissertation proposes the application of a new computational paradigm, intelligent agents in GISc. Intelligent agents are “computational systems that inhabit some complex dynamic environment, sense and act autonomously, and by doing so realise a set of goals or tasks for which they are designed “ (Maes, 1995). The aim of this dissertation is to analyse the potential of research in intelligent agents in GISc and to explore the use of simple learning techniques to improve the adaptability of spatial intelligent agents. The thesis involves the following objectives: to analyse the needs of research in GISc in the areas of reasoning about geographic space; to study the potential of intelligent agents in that area of research; to explore the use of simple learning techniques to improve the adaptability of intelligent agents for geographic information; and to explore the implementation environments of GIS software for the integration of intelligent agent systems. The primary contributions of this research are three case studies which use intelligent agents in a spatial or geographic context: a simple non-adaptive interface assistant for the printing and plotting tool of Smallworld GIS; an intelligent assistant that uses memory-based reasoning to identify and locate specific-purpose geographic information; a simulation of a car park where agents are cars that use reinforcement learning techniques to improve their parking performance

Believing Change and Changing Belief

We present a first-order logic of time, chance, and probability that is capable of expressing the four types of higher-order probability sentences relating subjective probability and objective chance at different times. We define a causal notion of objective chance and show how it can be used in conjunction with subjective probability to distinguish between causal and evidential correlation by distinguishing between conditions, events, and actions that 1) influence the agent's belief in chance and 2) the agent believes to influence chance. Furthermore, the semantics of the logic captures some commonsense inferences concerning objective chance and causality. We show that an agent's subjective probability is the expected value of its beliefs concerning objective chance. We also prove that an agent using this representation believes with certainty that the past cannot be causally influenced. To appear in IEEE SMC special issue on Higher-Order Probability. 1 Introduction Temporal probab..