Reasoning about Knowledge and Belief: A Syntactical Treatment

The study of formal theories of agents has intensified over the last couple of decades, since such formalisms can be viewed as providing the specifications for building rational agents and multi-agent systems. Most of the proposed approaches are based upon the well-understood framework of modal logics and possible world semantics. Although intuitive and expressive, these approaches lack two properties that can be considered important to a rational agent's reasoning: quantification over the propositional attitudes, and self-referential statements. This paper presents an alternative framework which is different from those found in the literature in two ways: Firstly, a syntactical approach for the representation of the propositional attitudes is adopted. This involves the use of a truth predicate and syntactic modalities which are defined in terms of the truth predicate itself and corresponding modal operators. Secondly, an agent's information state includes both knowledge and beliefs. Independent modal operators for the two notions are introduced and based on them syntactic modalities are defined. Furthermore, the relation between knowledge and belief is thoroughly explored and three different connection axiomatisations for the modalities and the syntactic modalities are proposed and their properties investigated

A Directional Change Based Trading Strategy with Dynamic Thresholds

Traders always seek a trading strategy that can increase and maximize their profits. However, given the current challenges in financial time-series streams – data elements (tick prices) arrive in real-time or almost real-time and at high velocity (at finer time scales) – it is difficult to identify and spot the best time and the most profitable price for trading. The Directional Change (DC) is an event-based approach for summarizing price movements based on a fixed given threshold value. An event in the DC approach is detected if the price change between two points satisfies the given threshold value. In this research, we aim to present a trading strategy based on the DC approach and a dynamic threshold to replace the fixed given one. We call this strategy, the Dynamic Threshold Trading Strategy (DT-TS). Thus, once a DC event is detected (a price change is identified) using the defined dynamic threshold, a trading action is triggered as prices continue to increase or decrease depending on the detected DC event. The trading action to be taken (buy or sell) depends on the previous day price transitions. An experiment was conducted on the FTSE 100 minute-by-minute prices stream to evaluate the DT-TS against different fixed threshold values and different trading strategies. Results showed that the DT-TS was the most profitable strategy among different fixed thresholds and all other examined trading strategies

CSM-349 - Benford's Law: An Empirical Investigation and a Novel Explanation

This report describes an investigation into Benford?s Law for the distribution of leading digits in real data sets. A large number of such data sets have been examined and it was found that only a small fraction of them conform to the law. Three classes of mathematical model of processes that might account for such a leading digit distribution have also been investigated. We found that based on the notion of taking the product of many random factors the most credible. This led to the identification of a class of lognormal distributions, those whose shape parameter exceeds 1, which satisfy Benford?s Law. This in turn led us to a novel explanation for the law: that it is fundamentally a consequence of the fact that many physical quantities cannot meaningfully take negative values. This enabled us to develop a simple set of rules for determining whether a given data set is likely to conform to Benford?s Law. Our explanation has an important advantage over previous attempts to account for the law: it also explains which data sets will not have logarithmically distributed leading digits. Some techniques for generating data that satisfy Benford?s law are described and the report concludes with a summary and a discussion of the practical implications

Deploying self-organisation to improve task execution in a multi-agent systems

This paper discusses how the performance of a network of agents can be improved using a self-organisation technique. The multi-agent network performance can be improved by organizing the agents in clusters. Furthermore, principles of self-organisation can be used to create agent organisations triggered when some of the agents have high load. Hence, busy agents within the network may decide to create an organisation to receive extra support from other less busy agents in order to execute more tasks. The paper presents a simulation based on Repast Simphony that has been used to develop the proposed model and describes a set of experiments showing the performance of the system with and without the self-organisation technique

Moving towards Adaptive Search

Information retrieval has become very popular over the last decade with the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as intranets and digital libraries. Such collections are the focus of the recently started AutoAdapt project1. The project seeks to aid user search by providing well-structured domain knowledge to assist query modification and navigation. There are two challenges: acquiring the domain knowledge and adapting it automatically to the specific interest of the user community. At the workshop we will demonstrate an implemented prototype that serves as a starting point on the way to truly adaptive search

Using domain models for context-rich user logging

This paper describes the prototype interactive search sys- Tem being developed within the AutoAdapt project1. The AutoAdapt project seeks to enhance the user experience in searching for information and navigating within selected do- main collections by providing structured representations of domain knowledge to be directly explored, logged, adapted and updated to refject user needs. We propose that this structure is a valuable stepping-stone in context-rich logging of user activities within the information seeking environment. Here we describe the primary components that have been implemented and the user interactions that it will support

Towards an adaptive SOA-based QoS & Demand-Response Provisioning Architecture for the Smart Grid

Dynamic selection of services and by extension of service providers are vital in today’s liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, throughput, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with a data analytics/machine learning architecture to further enrich the provisioning aspect this work is bringing to the Smart Grid market of energy

Building an adaptive E-learning system

© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Research in adaptive learning is mainly focused on improving learners' learning achievements based mainly on personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed based upon two main sources of personalization information that is, learning behaviour and personal learning style. To determine the initial learning styles of the learner, an initial assigned test is employed in our approach. In order to more precisely reflect the learning behaviours of each learner, the interactions and learning results of each learner are thoroughly recorded and in depth analysed, based on advanced machine learning techniques, when adjusting the subject materials. Based on this rather innovative approach, an adaptive learning prototype system has been developed