arXiv subjects

Simon Parsons

Publications and source records attributed to Simon Parsons.

31 records · Page 2Linked to original sources

arXiv2017

Two forms of minimality in ASPIC+

Many systems of structured argumentation explicitly require that the facts and rules that make up the argument for a conclusion be the minimal set required to derive the conclusion. ASPIC+ does not place such a requirement on arguments, instead requiring that every rule and fact that are part of an argument be used in its construction. Thus ASPIC+ arguments are minimal in the sense that removing any element of the argument would lead to a structure that is not an argument. In this brief note we discuss these two types of minimality and show how the first kind of minimality can, if desired, be recovered in ASPIC+.

cs.AI↗

arXiv2017

On the links between argumentation-based reasoning and nonmonotonic reasoning

In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in [9] with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by ASPIC+ under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by ASPIC+ is - according to the axioms of [9] - non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems they considered possible. This weakness underpins ASPIC+'s success in modelling other reasoning systems, and we conclude by considering the relationship between ASPIC+ and other weak logical systems.

cs.AI↗

arXiv2014

An Argumentation-Based Framework to Address the Attribution Problem in Cyber-Warfare

Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. In this paper, we introduce a formal reasoning system called the InCA (Intelligent Cyber Attribution) framework that is designed to aid an analyst in the attribution of a cyber-operation even when the available information is conflicting and/or uncertain. Our approach combines argumentation-based reasoning, logic programming, and probabilistic models to not only attribute an operation but also explain to the analyst why the system reaches its conclusions.

cs.CR↗

arXiv2013

Re-entrant Structural Phase Transition in a Frustrated Kagome Magnet, Rb2SnCu3F12

Rb2SnCu3F12 has been studied using synchrotron powder X-ray diffraction, powder neutron diffraction and single crystal X-ray diffraction at a range of temperatures (100 to 500 K). A broad but clear phase transition, possibly of re-entrant character, is found to occur in the powder form, whereas the corresponding transition is not seen in the single crystal.

cond-mat.str-el↗

arXiv2013

On reasoning in networks with qualitative uncertainty

In this paper some initial work towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reasoning, as is the case with other methods, but also allows the qualitative propagation within networks of values based upon possibility theory and Dempster-Shafer evidence theory. The method is applied to two simple networks from which a large class of directed graphs may be constructed. The results of this analysis are used to compare the qualitative behaviour of the three major quantitative uncertainty handling formalisms, and to demonstrate that the qualitative integration of the formalisms is possible under certain assumptions.

cs.AI↗

arXiv2013

Refining Reasoning in Qualitative Probabilistic Networks

In recent years there has been a spate of papers describing systems for probabilisitic reasoning which do not use numerical probabilities. In some cases the simple set of values used by these systems make it impossible to predict how a probability will change or which hypothesis is most likely given certain evidence. This paper concentrates on such situations, and suggests a number of ways in which they may be resolved by refining the representation.

cs.AI↗

arXiv2013

Maximizing Matching in Double-sided Auctions

In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, showing that the maximal matching algorithm is favored in scenarios where trading volume is a priority and that it may possibly improve allocative efficiency over equilibrium matching as well. A parameterized algorithm that incorporates both maximal matching and equilibrium matching as special cases is also presented to allow flexible control on how much to trade in a double auction.

cs.GT↗

arXiv2013

Risk Agoras: Dialectical Argumentation for Scientific Reasoning

We propose a formal framework for intelligent systems which can reason about scientific domains, in particular about the carcinogenicity of chemicals, and we study its properties. Our framework is grounded in a philosophy of scientific enquiry and discourse, and uses a model of dialectical argumentation. The formalism enables representation of scientific uncertainty and conflict in a manner suitable for qualitative reasoning about the domain.

cs.AI↗

arXiv2013

Pivotal Pruning of Trade-offs in QPNs

Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. Due to their coarse level of representation detail, qualitative probabilistic networks do not provide for resolving trade-offs and typically yield ambiguous results upon inference. We present an algorithm for computing more insightful results for unresolved trade-offs. The algorithm builds upon the idea of using pivots to zoom in on the trade-offs and identifying the information that would serve to resolve them.

cs.AI↗

arXiv2013

Local structure correlations in plastic cyclohexane - a Reverse Monte Carlo study

Two solid phases of cyclohexane have been investigated over a temperature range spanning 13 to 266 K on a powdered, perdeuterated sample using neutron total scattering. Phase II has an ordered structure (C2/c) that forms below 186 K. Between 186 and 280 K it exists as a plastic solid - phase I (Fm3m), where the molecules are rotationally disordered about the lattice points of the face-centred cubic cell. Data-dependent atomistic configurations that represent the 'instantaneous' crystal structure have been generated from the total scattering data using Reverse Monte Carlo refinement. Analysis of local structure reveals that instantaneous distortions in phase I resemble the average structure of phase II.

cond-mat.mtrl-sci↗

arXiv2012

Formalizing Scenario Analysis

We propose a formal treatment of scenarios in the context of a dialectical argumentation formalism for qualitative reasoning about uncertain propositions. Our formalism extends prior work in which arguments for and against uncertain propositions were presented and compared in interaction spaces called Agoras. We now define the notion of a scenario in this framework and use it to define a set of qualitative uncertainty labels for propositions across a collection of scenarios. This work is intended to lead to a formal theory of scenarios and scenario analysis.

cs.AI↗

arXiv2010

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.

cs.GT↗

arXiv2009

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field.

cs.AI↗