Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Adopting a computational approach to identifying and modelling future development initiatives within the urban context

In 2002 the State Government of Victoria, in partnership with the City of Greater Geelong and Deakin University, initiated and urban design framework to explore development opportunities of a series of redundant industrial sites adjacent to Geelong's central activities area (CAA). One of the objectives of the framework was to explore a range of initiatives based on the expansion of Deakin University Waterfront campus from 700 students to an expected cohort of 5000 students. Working with a set of predefined constructs for different types of city-based user groups, such as net floor space requirements and time/travel distances between facilities, the following paper presents a simple computational approach to assist in identifying, mapping, and spatially exploring different development scenarios.

Interplay of ethical trust and social moral norms : environment modelling and computational mechanisms in agent-based social simulation

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and social moral norms addresses that challenge via an analytical framework on the spread of moral norms, the modelling of social environment and the selection of spread mechanisms as applied to agent-based social simulation. We describe the mechanism alignment mapping, two forms of interaction modelling between the social environment and agents, and the results obtained from the simulation of our computational model. These results provide an insight into how the agent-based paradigm can be applied as a technique of investigation for normative moral processes in computational social sciences.

A computational approach to the reconstruction of surface geometry from early temple superstructures

Recovering the control or implicit geometry underlying temple architecture requires bringing together fragments of evidence from field measurements, relating these to mathematical and geometric descriptions in canonical texts and proposing "best-fit" constructive models. While scholars in the field have traditionally used manual methods, the innovative application of niche computational techniques can help extend the study of artefact geometry. This paper demonstrates the application of a hybrid computational approach to the problem of recovering the surface geometry of early temple superstructures. The approach combines field measurements of temples, close-range architectural photogrammetry, rule-based generation and parametric modelling. The computing of surface geometry comprises a rule-based global model governing the overall form of the superstructure, several local models for individual motifs using photogrammetry and an intermediate geometry model that combines the two. To explain the technique and the different models, the paper examines an illustrative example of surface geometry reconstruction based on studies undertaken on a tenth century stone superstructure from western India. The example demonstrates that a combination of computational methods yields sophisticated models of the constructive geometry underlying temple form and that these digital artefacts can form the basis for in depth comparative analysis of temples, arising out of similar techniques, spread over geography, culture and time.

Computational simulation of gas flow and heat transfer near an immersed object in fluidized beds

To improve the understanding of the heat transfer mechanism and to find a reliable and simple heat-transfer model, the gas flow and heat transfer between fluidized beds and the surfaces of an immersed object is numerically simulated based on a double particle-layer and porous medium model. The velocity field and temperature distribution of the gas and particles are analysed during the heat transfer process. The simulation shows that the change of gas velocity with the distance from immersed surface is consistent with the variation of bed voidage, and is used to validate approximately dimensional analysing result that the gas velocity between immersed surface and particles is 4.6Umf/εmf. The effects of particle size and particle residence time on the thermal penetration depth and the heat-transfer coefficients are also discussed.

Computational simulation of chassis design and manufacturing

Australian Universities including Deakin University are entering the Formula One SAE car competition this year which was initialised in USA many years ago and has attracted many universities to participate all over the world. There are many categories to be competed for novelty of the design. To achieve optimised design of the car, computational techniques have widely been used in all aspects of the design and manufacturing of the car for the team at Deakin University. In this work, the design and manufacturing of the chassis was computationally simulated to optimise the structure for stress and natural frequencies. The chassis is designed using solid modelling code IDEAS with the geometry exported to ABAQUS, a FEM software, for optimisation.

Improving the quality of die castings by using artificial neural networks for porosity defect modelling

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.

Computational investigations on periodic trends in Group 14 chemistry

Describes the use of computer-aided molecular modelling to investigate trends in the chemistry of the Group 14 elements, namely carbon, silicon, germanium, tin and lead. The chemical behaviour of two classes of molecules containing Group 14 elements was related to trends in the fundamental properties of these elements.

Tree façades : generative modelling with an axial branch rewriting system

The methods and algorithms of generative modelling can be improved when representing organic structures by the study of computational models of natural processes and their application to architectural design. In this paper, we present a study of the generation of branching structures and their application to the development of façade support systems. We investigate two types of branching structures, a recursive bifurcation model and an axial tree based L-system for the generation of façades. The aim of the paper is to capture not only the form but also the underlying principles of biomimicry found in branching. This is then tested, by their application to develop experimental façade support systems. The developed algorithms implement parametric variations for façade generation based on natural tree-like branching. The benefits of such a model are: ease of structural optimization, variations of support and digital fabrication of façade components.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

Agents in adversarial domains - modelling environments in parallel

We present a model of an environment to evaluate the behavior of an agent trying to hide from a pursuer is presented. The model computes the direction and the amount of protection provided by the environment. The computational complexity of this problem is improved by using a parallel implementation of this algorithm.

Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model

In this paper, we exploit the discrete Coxian distribution and propose a novel form of stochastic model, termed as the Coxian hidden semi-Makov model (Cox-HSMM), and apply it to the task of recognising activities of daily living (ADLs) in a smart house environment. The use of the Coxian has several advantages over traditional parameterization (e.g. multinomial or continuous distributions) including the low number of free parameters needed, its computational efficiency, and the existing of closed-form solution. To further enrich the model in real-world applications, we also address the problem of handling missing observation for the proposed Cox-HSMM. In the domain of ADLs, we emphasize the importance of the duration information and model it via the Cox-HSMM. Our experimental results have shown the superiority of the Cox-HSMM in all cases when compared with the standard HMM. Our results have further shown that outstanding recognition accuracy can be achieved with relatively low number of phases required in the Coxian, thus making the Cox-HSMM particularly suitable in recognizing ADLs whose movement trajectories are typically very long in nature.