Chee Fon Chang

SBDO: a new robust approach to dynamic distributed constraint optimisation

Here we introduce a novel algorithm for continual optimisation of dynamic distributed constraint optimisation problems. By using techniques derived from argumentation for communication the algorithm does not need to use an ordering over the variables. The lack of a hierarchy allows the algorithm to efficiently solve dynamic problems, as well as be completely asynchronous, fault tolerant and anytime. However it prevents an ordered search, making the algorithm incomplete.

Support-based distributed search: a new approach for multiagent constraint processing

Distributed Constraint Satisfaction Problems provide a natural mechanism for multiagent coordination and agreement. To date, algorithms for Distributed Constraint Satisfaction Problems have tended to mirror existing non-distributed global-search or local-search algorithms. Unfortunately, existing distributed global-search algorithms derive from classical backtracking search methods and require a total ordering over agents for completeness. Distributed variants of local-search algorithms (such as distributed breakout) inherit the incompleteness properties of their predecessors, or depend on the creation of new communication links between agents. This paper presents an algorithm in which a global ordering is not required, while avoiding the problems of existing local-search algorithms.

Support-based distributed search

Distributed Constraint Satisfaction Problems provide a natural mechanism for multiagent coordination and agreement. To date, algorithms for Distributed Constraint Satisfaction Problems have tended to mirror existing non-distributed global-search or local-search algorithms. Unfortunately, existing distributed global-search algorithms derive from classical backtracking search methods and require a total ordering over agents for completeness. Distributed variants of local-search algorithms (such as distributed breakout) inherit the incompleteness properties of their predecessors, or depend on the creation of new communication links between agents. This paper presents an algorithm in which a global ordering is not required, while avoiding the problems of existing local-search algorithms.

Semantic Monitoring and Compensation in Socio-technical Processes

Socio-technical processes are becoming increasingly important, with the growing recognition of the computational limits of full automation, the growth in popularity of crowd sourcing, the complexity and openness of modern organizations etc. A key challenge in managing socio-technical processes is dealing with the flexible, and sometimes dynamic, nature of the execution of human-mediated tasks. It is well-recognized that human execution does not always conform to predetermined coordination models, and is often error-prone. This paper addresses the problem of semantically monitoring the execution of socio-technical processes to check for non-conformance, and the problem of recovering from (or compensating for) non-conformance. This paper proposes a semantic solution to the problem, by leveraging semantically annotated process models to detect non-conformance, and using the same semantic annotations to identify compensatory human-mediated tasks.

Using distributed agents for patient scheduling

Ensuring optimum use of scarce resources is one of the largest challenges facing health providers today. However it is not easy to generate an optimised schedule, as the health system is unusually and highly dynamic. Scheduling systems must be extremely flexible while still producing an efficient, acceptable schedule. Furthermore the scheduling system should be able to cross health boundaries inside and outside hospitals to perform load sharing. To solve this problem we propose an encoding of the patient scheduling problem as a dynamic distributed constraint optimisation problem and show how it can be solved using Support Based Distributed Optimisation. The resulting system will be able to generate good schedules and update them in real time. It is also able to maintain privacy across hospital boundaries to enable load balancing.

Practical application of support-based distributed search

Algorithms for distributed constraint satisfaction problems have tended to mirror existing non-distributed global-search or local-search algorithms. Unfortunately, existing distributed global-search algorithms derive from classical backtracking search methods and require a total ordering over variables for completeness. Distributed variants of local-search algorithms (such as distributed breakout) inherit the incompleteness properties of their predecessors. A meeting scheduling problem translates to a DisCSP where a global ordering is difficult to maintain and creates undesirable behaviours. We present a practical demonstration of an algorithm in which a global ordering is not required, while avoiding the problems of local-search algorithms

Support-Based Distributed Optimisation: An Approach to Radiotherapy Scheduling

The public health system is plagued by inefficient use of resources. Frequently, the results are lengthy patient treatment waiting times. While many solutions for patient scheduling in health systems exist, few address the problem of coordination between independent autonomous departments. In this study, we describe the use of a distributed dynamic constraint optimisation algorithm (Support Based Distributed Optimisation) for the generation and optimisation of schedules across autonomous units. We model the problem of scheduling radiotherapy patients across several independent oncology units as a dynamic distributed constraint optimisation problem. Such an approach minimises the sharing of private information such as department operation details as well as patient privacy information while taking into consideration patient preferences as well as resource utilisation to find a pareto-optimal solution.

Learning Relationships Between the Business Layer and the Application Layer in ArchiMate Models

Enterprise architecture provides a visualisation tool for stakeholder to manage and improve the current organization strategy to achieve its objectives. However, building an enterprise architecture is a time-consuming and often highly complex task. It involves data collection and analysis in several levels of granularity, from the physical nodes to the business execution. Existing solutions does not provide techniques to learn the relationship between the levels of granularity. In this paper, we proposed a method to correlate the business and application layers in ArchiMate notation.

Multi-Objective Distributed Constraint Optimization using Semi-Rings

In this paper, we extend the Support Based Distributed Optimization (SBDO) algorithm to support problems which do not have a total pre-order over the set of solutions. This is the case in common real life problems that have multiple objective functions. In particular, decision support problems. These disparate objectives are not well supported by existing Distributed Constraint Optimization Problem (DCOP) techniques, which assume a single cost or utility function. As a result, existing Distributed COP techniques (with some recent exceptions) require that all agents subscribe to a common objective function and are therefore unsuitable for settings where agents have distinct, competing objectives. This makes existing constraint optimization technologies unsuitable for many decision support roles, where the decision maker wishes to observe the different trade-offs before making a decision.

Combining credibility in a source sensitive argumentation system

There exist many approaches to agent-based conflict resolution which adopts argumentation as their underlying conflict resolution machinery. In most argumentation systems, the credibility of argument sources plays a minimal role. This paper focuses on combining credibility of sources in a source sensitive argumentation.