Beyond 2017: the Australian Defence Force and amphibious warfare

Overview: The delivery of Australia’s new amphibious warships, HMAS Canberra and Adelaide, is an important milestone in the ADF’s quest to develop a strategically relevant amphibious warfare capability. Australia’s position in the world makes the effort a strategic imperative, but the ADF still has a long way to go and many critical decisions ahead if it’s to develop an amphibious warfare capability that’s ready for future challenges. The resources committed to the effort and the associated opportunity costs have been and will be substantial, and the overall need for the capability must be weighed against other priorities, but if Australia’s going to do it, we should do it properly. The aim of the paper was to identify some of the key decisions to be made by ADF leaders over the next two years to ensure that Australia has an amphibious warfare capability that’s effective and relevant to future challenges and provide specific recommendations on the

Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains

Assurance of learning standards and scaling strategies to enable expansion of experiential learning courses in management education

In today’s dynamic globalized business environment, management educators must develop pedagogies that support students to manage and lead in rapidly changing business contexts. An increasing number of institutions use experiential learning as a component of their curriculum to address this challenge. Initially, a response to industry criticism that graduates were unable effectively apply skills needed to be successful, experiential learning has become a baseline expectation in management education programs. Students increasingly expect opportunities to practice and demonstrate competency in the theories they learn in the classroom by applying them in real-world projects. However, expanding such opportunities for students is limited by a unique set of complex administrative challenges inherent in this approach. To expand opportunities for students, institutions must overcome scalability obstacles resulting from the customized nature of the offerings. Business challenges where student teams work with external partners provide a real world learning experience. But they also pose difficulty in applying a standardized approach to assurance of learning. Course content must be redeveloped each time the course is offered, as external projects must be sourced, leading to input and output variation. Advising, monitoring, and assessing students is resource intensive, because at many schools each team is assigned a different business challenge. This article offers a set of assurance of learning standards that institutions can apply to project-based experiential learning courses and posits that greater cross-departmental integration in sourcing projects and better use of technology can increase the efficacy and efficiency of the courses to address the scalability issue.Educatio

Characteristics, Contracts, and Actions: Evidence from Venture Capitalist Analyses

We study the investment analyses of 67 portfolio investments by 11 venture capital (VC) firms. VCs consider the attractiveness and risks of the business, management, and deal terms as well as expected post-investment monitoring. We then consider the relation of the analyses to the contractual terms. Greater internal and external risks are associated with more VC cash flow rights, VC control rights; greater internal risk, also with more contingencies for the entrepreneur; and greater complexity, with less contingent compensation. Finally, expected VC monitoring and support are related to the contracts. We interpret these results in relation to financial contracting theories.

Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment

organizational resilience, strategic agility, competitive dynamics

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018