Search

Wildlife tourism (WT) is an important economic sector globally, which can sustain national and local economies. These activities have been reconceptualised as consumptive because of their impacts on the wildlife, and the problem of managing WT as a common-pool resource issue. We use an individual-based model to simulate the dynamics of a WT destination in different development phases. We then ask if any of the governance structures commonly proposed to solve common pool resource issues are appropriate to sustainably manage a WT destination during its development. The level of specialisation of tourists visiting a destination can influence both the exploitation of the wildlife and the socio-economic success of the industry, and no single governance structure leads to sustainability in every stage of a WT destination lifecycle. Given the dynamics of WT destinations, an adaptive governance framework is crucial to avoid wildlife depletion and economic failure of the industry.

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.

This paper presents a fuzzy qualitative representation of conventional trigonometry with the goal of bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in the domain of physical systems, especially in intelligent robotics. Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; a Cartesian translation and orientation are defined by their normalized fuzzy partitions. Conventional trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques. This approach provides a promising representation transformation interface to analyze general trigonometry-related physical systems from an artificial intelligence perspective.Fuzzy qualitative trigonometry has been implemented as a MATLAB toolbox named XTRIG in terms of 4-tuple fuzzy numbers. Examples are given throughout the paper to demonstrate the characteristics of fuzzy qualitative trigonometry. One of the examples focuses on robot kinematics and also explains how contributions could be made by fuzzy qualitative trigonometry to the intelligent connection of low-level sensing & control tasks to high-level cognitive tasks.

This paper presents a model-based approach to online robotic fault diagnosis: First Priority Diagnostic Engine (FPDE). The first principle of FPDE is that a robot is assumed to work well as long as its key variables are within an acceptable range. FPDE consists of four modules: the bounds generator, interval filter, component-based fault reasoner (core of FPDE) and fault reaction. The bounds generator calculates bounds of robot parameters based on interval computation and manufacturing standards. The interval filter provides characteristic values in each predetermined interval to denote corresponding faults. The core of FPDE carries out a two-stage diagnostic process: first it detects whether a robot is faulty by checking the relevant parameters of its end-effector, if a fault is detected it then narrows down the fault at the component level. FPDE can identify single and multiple faults by the introduction of characteristic values. Fault reaction provides an interface to invoke emergency operation or tolerant control, even possibly system reconfiguration. The paper ends with a presentation of simulation results and discussion of a case study.

We present a methodology for the selection of candidate generation and prediction techniques for model-based diagnostic systems (MBDS). We start by describing our taxonomy of the solution space based upon the three main functional blocks of a top-level MBDS architecture (the predictor, the candidate generator and the diagnostic strategist). We divide the corresponding problem space into user requirements and system constraints which are further subdivided into task and fault requirements, and plant and domain knowledge constraints respectively. Finally we propose a set of guidelines for selecting tools and techniques in the solution space given descriptions of diagnostic tasks in the problem space.

In this paper the issue of utilising multiple models for diagnosis of dynamic systems is explored. Models are defined by their properties; which are selected from the three categories: variables, relations, and structures. Also, Model-based Diagnosis is generally perceived to consist of three tasks: fault detection, fault isolation, and fault identification, dealing with the existence, location, and degree of a fault, respectively. In order to utilise multiple models for diagnosis it is necessary to have a correlation between the model properties and the diagnostic tasks. This provides a coherent means of guiding the switching process according to the task to be performed. The process is demonstrated with reference to fault isolation of a laboratory scale process system rig.