Christian Guttmann

Making Allocations Collectively: Iterative Group Decision Making under Uncertainty

A major challenge in the field of Multi-Agent Systems (MAS) is to enable autonomous agents to allocate tasks and resources efficiently. This paper studies an extended approach to a problem we refer to as the Collective Iterative Allocation (CIA) problem. This problem involves a group of agents that progressively refine allocations of teams to tasks. This paper considers the case where the performance of a team is variable and non-deterministic. This requires that each agent is able to maintain and update its probabilistic models using observations of each teams performance. A key result is that each agent needs the capacity to store only two or three observations of a teams performance to find near optimal allocations, and a further increase of this capacity will reduce the number of reallocations significantly.

Towards Models of Incomplete and Uncertain Knowledge of Collaborators' Internal Resources

Collaboration plays a critical role when a group is striving for goals which are difficult or impossible to achieve by an individual. Knowledge about collaborators’ contributions to a task is an important factor when establishing collaboration, in particular when a decision determines the assignment of activities to members of the group. Although there are several systems that implement collaboration, one important problem has not yet received much attention – determining the effect of incomplete and uncertain knowledge of collaborators’ internal resources (i.e. capabilities and knowledge) on the outcomes of the collaboration. We approach this problem by building models of internal resources of individuals and groups of collaborators. These models enable a system to estimate collaborators’ contributions to the task. We then assess the effect of model accuracy on task performance. An empirical evaluation is performed in order to validate this approach.

Syndromic surveillance in the United Arab Emirates

Opportunities for innovation in view of three complex problems faced by the UAE health care providers are described. The information dissemination problem faced could be approached by creating new channels for providing the population with public health information. These channels are precisely the ones typically used in so-called syndromic surveillance, including care-related data from communicable disease spread indicators, but also tweets and blog posts, for example. Syndromic surveillance could likewise assist the health authorities in addressing the knowledge elicitation problem: how to get more information on the life style, self care, and prevention among individual citizens. To some extent the prediction problem - how to predict the spread of infectious disease in the future and how to mathematically model social behaviour in the case of various health-threatening scenarios - would also be addressed by syndromic surveillance. Fully employed, the solutions proposed would provide new ICT services enabling preparedness for many forms of communicable disease outbreaks, as well as for natural disasters.

Voting policies that cope with unreliable agents

Collaboration plays a critical role when a team is striving for goals that are difficult to achieve by an individual. In previous work, we defined the ETAPP (Environment-Task-Agents-Policy-Protocol) framework, which describes the collaboration of a team of agents. According to this framework, team members propose agents to perform a task, and the team applies a voting policy to choose an agent for the task. In this paper, we expand on three parameters of this framework. We model team members that have variable proposal making attitudes, and team members whose performance exhibits different levels of stability. We then consider two new voting policies for group decision-making, and use a simulation-based evaluation to investigate the interaction between the different types of team members and the voting policies. Our results show that our previous optimistic voting policy, which chooses the agent that seems to have the best performance, yields an unstable task performance for teams where even a few agents do not make the best possible proposal. In contrast, our new voting policies yield a stable task performance.

Enabling the Next Generation Learning Environment

As more and more services that universities traditionally offer are provided through or underpinned by systems made of computers and networks, the role of these systems become more important. Previously independent functions can now be combined, or easily exchange data and information, hence offering new opportunities. In order to leverage and exploit these new possibilities, these services must be built to access each others data and functionalities in an open, secure and simple manner. This paper describes a smart learning platform that provides the backbone to the University of the Future for the next generation campus environment.

Goal-directed approach for process specification and service selection in customer life cycle management

Service selection is the first step in customer life cycle management where services are selected to meet a customer’s goals or objectives, personalised to the circumstances of the customer. The aim of this paper is twofold: (1) to develop concepts and algorithms for goal-directed service selection; and (2) to compare and reconcile our goal-directed approach with a service-oriented approach. The proposed goal-directed service selection algorithm is based on a goal-directed domain description that represents the customer objectives and the business processes. We use service component architectures with formalised contractual service process definitions as a software engineering approach to architectural design and realisation of service-oriented architectures (SOA). The comparison aims to understand the relationship between and benefits of a goal-directed approach and a service oriented approach . We use case studies from two complex customer care management domains to demonstrate the concepts. The implemented algorithms are tested in a health care case study.

Promotion of selfish agents in hierarchical organisations

In hierarchical organisations, a preferred outcome is to promote a more productive worker to a more influential status. However, productivity is rarely directly measurable, so an individual worker often has both motive and opportunity to misrepresent his productivity. This leads to an alternative possibility: the promotion of selfish individuals. We use an agent-based model to study how selfishness and competency of agents influence their promotion in hierarchical organisations. We consider the case where selfish agents can overstate their productivity and thus obtain undeserved promotions. Our results suggest that more productive agents reach positions of power most of the time. However, even under ideal conditions, selfish agents occasionally dominate the higher levels of a hierarchical organisation, which in turn has a dramatic effect on all lower levels. For organisations of around 100-10,000 employees with 3-4 hierarchy levels, on average, the promotion of selfish agents is minimized and the promotion of competent agents is maximized. Finally, we show that judging the productivity of an individual agent has a greater impact on promoting selfish behaviour than judging the productivity of an individuals team. These results illustrate that agent-based models provide a powerful framework for examining how local interactions contribute to the large-scale properties of multi-layered organisations.

Designing intelligent healthcare operations

Effective decision making is vital in all healthcare activities. While this decision making is typically complex and unstructured, it requires the decision maker to gather multi-spectral data and information in order to make an effective choice when faced with numerous options. Unstructured decision making in dynamic and complex environments is challenging and in almost every situation the decision maker is undoubtedly faced with information inferiority. The need for germane knowledge, pertinent information and relevant data are critical and hence the value of harnessing knowledge and embracing the tools, techniques, technologies and tactics of knowledge management are essential to ensuring efficiency and efficacy in the decision making process. The systematic approach and application of knowledge management (KM) principles and tools can provide the necessary foundation for improving the decision making processes in healthcare. The example of the orthopaedic operating room processes will illustrate the application of the integrated model to support effective decision making in the clinical environment.