Increasing the Numeric Expressiveness of the Planning Domain Definition Language

The technology of artificial intelligence (AI) planning is being adopted across many different disciplines. This has resulted in the wider use of the Planning Domain Definition Language (PDDL), where it is being used to model planning problems of different natures. One such area where AI planning is particularly attractive is engineering, where the optimisation problems are mathematically rich. The example used throughout this paper is the optimisation (minimisation) of machine tool measurement uncertainty. This planning problem highlights the limits of PDDL's numerical expressiveness in the absence of the square root function. A workaround method using the Babylonian algorithm is then evaluated before the extension of PDDL to include more mathematics functions is discussed

NTFS Permissions Explorer

Administrating and monitoring NTFS permissions can be somewhat of a cumbersome and convoluted task. In today’s modern data rich world there has never been a more important time to ensure that your data is secured against unwanted access. This software-based solution has been produced to aid user understand of the current implemented permissions and identify possible problems

Representing the Process of Machine Tool Calibration in First-order Logic

Machine tool calibration requires a wide range of measurement techniques that can be carried out in many different sequences. Planning a machine tool calibration is typically performed by a subject expert with a great understanding of International standards and industrial best-practice guides. However, it is often the case that the planned sequence of measurements is not the optimal. Therefore, in an attempt to improve the process, intelligent computing methods can be designed for plan suggestion. As a starting point, this paper presents a way of converting expert knowledge into first-order logic that can be expressed in the PROLOG language. It then shows how queries can be executed against the logic to construct a knowledge-base of all the different measurements that can be performed during machine tool calibration

The role of Artificial Intelligence in digital forensics:Case studies and future directions

The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and Model-Based Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations

Towards Automated Vulnerability Assessment

Vulnerability assessment (VA) is a well established method for determining security weaknesses within a system. The VA process is heavily reliant on expert knowledge, something that is attributed to being in short supply. This paper explores the possibility of automating VA and demonstrates an initial proof-of-concept involving decision-making skills comparable with a human-expert. This is achieved through encoding a domain model to represent expert-like capabilities, and then using model-based VA planning to determine VA tasks. Although security evaluation is a complex task, through the help of such models, we can determine the ways to find potential vulnerabilities without an expert present. This technique allows time constrained assessments, where a 'risk factor' is also encoded to represent the significance of each security flaw. The ultimate goal of this work-in-progress is to realistically simulate a human vulnerability auditor. This paper demonstrates the first step towards that goal; a systematic transformation of the VA knowledge into a PDDL representation, accommodating a broad range of time constrained investigative actions. The output plan and its analysis evidently evinces many potential benefits such as increased feasibility and productivity

Adaptive decision support for suggesting a machine tool maintenance strategy: from reactive to preventative

Purpose -- To produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach -- A maintenance cost estimation model is utilised within the research and development of this decision support system. An empirical-based methodology is pursued and validated through case study analysis. Findings -- A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case-study, a 28% reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications -- The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value -- The paper presents an adaptive decision support system to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique

Auditing file system permissions using Association Rule Mining

Identifying irregular file system permissions in large, multi-user systems is challenging due to the complexity of gaining structural understanding from large volumes of permission information. This challenge is exacerbated when file systems permissions are allocated in an ad-hoc manner when new access rights are required, and when access rights become redundant as users change job roles or terminate employment. These factors make it challenging to identify what can be classed as an irregular file system permission, as well as identifying if they are irregular and exposing a vulnerability. The current way of finding such irregularities is by performing an exhaustive audit of the permission distribution; however, this requires expert knowledge and a significant amount of time. In this paper a novel method of modelling file system permissions which can be used by association rule mining techniques to identify irregular permissions is presented. This results in the creation of object-centric model as a by-product. This technique is then implemented and tested on Microsoft's New Technology File System permissions (NTFS). Empirical observations are derived by making comparisons with expert knowledge to determine the effectiveness of the proposed technique on five diverse real-world directory structures extracted from different organisations. The results demonstrate that the technique is able to correctly identify irregularities with an average accuracy rate of 91%, minimising the reliance on expert knowledge. Experiments are also performed on synthetic directory structures which demonstrate an accuracy rate of 95% when the number of irregular permissions constitutes 1% of the total number. This is a significant contribution as it creates the possibility of identifying vulnerabilities without prior knowledge of how to file systems permissions are implemented within a directory structure

A Hybrid Approach to Process Planning: The Urban Traffic Controller Example

Automated planning and scheduling are increasingly utilised in solving evsery day planning task. Planning in domains with continuous numeric changes present certain limitations and tremendous challenges to advanced planning algorithms. There are many pertinent examples to the engineering community; however, a case study is provided through the urban traffic controller domain. This paper introduce a novel hybrid approach to state-space planning systems involving a continuous process which can be utilised in several applications. We explore Model Predictive Control (MPC) and explain how it can be introduce into planning with domains containing mixed discrete and continuous state variables. This preserves the numerous benefits of AI Planning approach by the use of explicit reasoning and declarative modelling. It also leverages on the capability of MPC to manage numeric computation and control of continuous processes. The hybrid approach was tested on an urban traffic control network to ascertain it practicability on a continuous domain; the results show its potential to control and optimise heavy volumes of traffic

Comfort-constrained distributed heat pump management

This paper introduces the design of a demand response network control strategy aimed at thermostatically controlled electric heating and cooling systems in buildings. The method relies on the use of programmable communicating thermostats, which are able to provide important component-level state variables to a system-level central controller. This information can be used to build power density distribution functions for the aggregate heat pump load. These functions lay out the fundamental basis for the methodology by allowing for consideration of customer-level constraints within the system-level decision making process. The proposed strategy is then implemented in a computational model to simulate a distribution of buildings, where the aggregate heat pump load is managed to provide the regulation services needed to successfully integrate wind power generators. Increased exploitation of wind resources will place similarly themed ancillary services in high-demand, traditionally provided by dispatchable energy resources that are ill-suited for the frequent power gradients that accompany wind power generation.Comment: 2011 International Conference on Smart Grid and Clean Energy Technologie