Probabilistic Algorithmic Knowledge

The framework of algorithmic knowledge assumes that agents use deterministic knowledge algorithms to compute the facts they explicitly know. We extend the framework to allow for randomized knowledge algorithms. We then characterize the information provided by a randomized knowledge algorithm when its answers have some probability of being incorrect. We formalize this information in terms of evidence; a randomized knowledge algorithm returning ``Yes'' to a query about a fact \phi provides evidence for \phi being true. Finally, we discuss the extent to which this evidence can be used as a basis for decisions.Comment: 26 pages. A preliminary version appeared in Proc. 9th Conference on Theoretical Aspects of Rationality and Knowledge (TARK'03

Modeling Adversaries in a Logic for Security Protocol Analysis

Logics for security protocol analysis require the formalization of an adversary model that specifies the capabilities of adversaries. A common model is the Dolev-Yao model, which considers only adversaries that can compose and replay messages, and decipher them with known keys. The Dolev-Yao model is a useful abstraction, but it suffers from some drawbacks: it cannot handle the adversary knowing protocol-specific information, and it cannot handle probabilistic notions, such as the adversary attempting to guess the keys. We show how we can analyze security protocols under different adversary models by using a logic with a notion of algorithmic knowledge. Roughly speaking, adversaries are assumed to use algorithms to compute their knowledge; adversary capabilities are captured by suitable restrictions on the algorithms used. We show how we can model the standard Dolev-Yao adversary in this setting, and how we can capture more general capabilities including protocol-specific knowledge and guesses.Comment: 23 pages. A preliminary version appeared in the proceedings of FaSec'0

Экологические аспекты представления знаний средствами алгебры алгоритмики

Работа представляет экологические аспекты порождения алгоритмических знаний предметных областей. Средства алгоритмической формализации и синтеза знаний предметных областей представляют собой триаду – абстракции, биологию и экологию программирования. В качестве абстрактного механизма используется алгебраический аппарат теории клонов. Биологическая компонента отвечает за распространение полученных алгоритмических знаний на другие задачи данной предметной области и близких к ней областей. Экологический компонент предназначен для формирования инструментальных средств поддержки методов абстрактной и биологической составляющих теории клонов. В рамках экологической компоненты предлагаются различные интерпретации алгоритмических операций, средства исследования параллелизма (в частности структурного на примере клеточных автоматов Минского), механизмы вывода алгоритмических знаний.This paper presents the environmental aspects of generation of algorithmic knowledge domains. Facilities of algorithmic formalization and synthesis of knowledge domains represent a triad - abstraction, biology and ecology of programming. As an abstract mechanism is used an algebraic formalism of the theory of clones. The biological component is responsible for the spread of obtained algorithmic knowledge to other tasks of the subject area and related areas. Environmental component is designed to build tools for support of abstract and biological components of the theory of clones. As part of the environmental components are offered different interpretations of algorithmic operations, facilities of the study of parallelism (including structural), the output engine of algorithmic knowledge, means to support the accumulated knowledge bases of subject areas

Assessing Conceptual and Algorithmic Knowledge in General Chemistry with ACS Exams

In 2005, the ACS Examinations Institute released an exam for first-term general chemistry in which items are intentionally paired with one conceptual and one traditional item. A second-term, paired-questions exam was released in 2007. This paper presents an empirical study of student performances on these two exams based on national samples of students who took the exams as part of a general chemistry course sequence. Psychometric data for student performances are presented in terms of classical difficulty and discrimination indexes, as well as item characteristic curves, as are more commonly used in item response theory. Having these data provided for all items on these two exams presents background information that researchers in chemistry education can use in studies in which these exams are part of the assessment paradigm that is used. Finally, because ACS Exams items may not be published, this manuscript presents examples of paired questions that can be used to describe the nature of the exam in subsequent publications that use these exams

Implicit and explicit learning in ACT-R

A useful way to explain the notions of implicit and explicit learning in ACT-R is to define implicit learning as learning by ACT-R's learning mechanisms, and explicit learning as the results of learning goals. This idea complies with the usual notion of implicit learning as unconscious and always active and explicit learning as intentional and conscious. Two models will be discussed to illustrate this point. First a model of a classical implicit memory task, the SUGARFACTORY scenario by Berry & Broadbent (1984) will be discussed, to show how ACT-R can model implicit learning. The second model is of the so-called Fincham task (Anderson & Fincham, 1994), and exhibits both implicit and explicit learning

ЭКОЛОГИЧЕСКИЕ АСПЕКТЫ ПРЕДСТАВЛЕНИЯ ЗНАНИЙ СРЕДСТВАМИ АЛГЕБРЫ АЛГОРИТМИКИ

Работа представляет экологические аспекты порождения алгоритмических знаний предметных областей. Средства алгоритмической формализации и синтеза знаний предметных областей представляют собой триаду – абстракции, биологию и экологию программирования. В качестве абстрактного механизма используется алгебраический аппарат теории клонов. Биологическая компонента отвечает за распространение полученных алгоритмических знаний на другие задачи данной предметной области и близких к ней областей. Экологический компонент предназначен для формирования инструментальных средств поддержки методов абстрактной и биологической составляющих теории клонов. В рамках экологической компоненты предлагаются различные интерпретации алгоритмических операций, средства исследования параллелизма (в частности структурного на примере клеточных автоматов Минского), механизмы вывода алгоритмических знаний. \ud This paper presents the environmental aspects of generation of algorithmic knowledge domains. Facilities of algorithmic formalization and synthesis of knowledge domains represent a triad - abstraction, biology and ecology of programming. As an abstract mechanism is used an algebraic formalism of the theory of clones. The biological component is responsible for the spread of obtained algorithmic knowledge to other tasks of the subject area and related areas. Environmental component is designed to build tools for support of abstract and biological components of the theory of clones. As part of the environmental components are offered different interpretations of algorithmic operations, facilities of the study of parallelism (including structural), the output engine of algorithmic knowledge, means to support the accumulated knowledge bases of subject areas.\u

An expert system for a local planning environment

In this paper, we discuss the design of an Expert System (ES) that supports decision making in a Local Planning System (LPS) environment. The LPS provides the link between a high level factory planning system (rough cut capacity planning and material coordination) and the actual execution of jobs on the shopfloor, by specifying a detailed workplan. It is divided in two hierarchical layers: planning and scheduling. At each level, a set of different algorithms and heuristics is available to anticipate different situations.\ud \ud The Expert System (which is a part of the LPS) supports decision making at each of the two LPS layers by evaluating the planning and scheduling conditions and, based on this evaluation, advising the use of a specific algorithm and evaluating the results of using the proposed algorithm.\ud \ud The Expert System is rule-based while knowledge (structure) and data are separated (which makes the ES more flexible in terms of fine-tuning and adding new knowledge). Knowledge is furthermore separated in algorithmic knowledge and company specific knowledge. In this paper we discuss backgrounds of the expert system in more detail. An evaluation of the Expert system is also presented

Privacy Architectures: Reasoning About Data Minimisation and Integrity

Privacy by design will become a legal obligation in the European Community if the Data Protection Regulation eventually gets adopted. However, taking into account privacy requirements in the design of a system is a challenging task. We propose an approach based on the specification of privacy architectures and focus on a key aspect of privacy, data minimisation, and its tension with integrity requirements. We illustrate our formal framework through a smart metering case study.Comment: appears in STM - 10th International Workshop on Security and Trust Management 8743 (2014